memory representation in the medial prefrontal cortex...the meaning of the stimulus...
TRANSCRIPT
Memory representation in the medial prefrontal cortex
Cortical communication, and the development of a prefrontal ensemble code for associative memory
by
Mark Morrissey
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Psychology
University of Toronto
© Copyright by Mark Morrissey 2016
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Memory representation in the medial prefrontal cortex
Cortical communication, and the development of a prefrontal ensemble code for associative memory
Mark Morrissey
Doctor of Philosophy
Department of Psychology
University of Toronto
2016
Abstract
Despite knowledge of the critical importance of the medial prefrontal cortex (mPFC) in both the
consolidation and long-term retrieval of event memory in the brain, it is unclear which aspects of
a memory are represented within this region and how they change with learning and memory
consolidation. Likewise, how the mPFC interacts with other facets of the memory network to
shape these representations is also uncertain. In this thesis I employed electrophysiological
techniques to examine individual prelimbic mPFC (PrL) neurons selectivity for various task
features in an associative memory paradigm in rats across learning, consolidation and over-
training. Further, I recorded oscillatory activity from the rhinal cortices and ventral hippocampus
to examine how these regions may modulate mPFC memory representations across time. I reveal
similar proportions at all time-points of both neurons highly selective for specific task features,
and neurons exhibiting more generalized activity. Looking at population level activity across
time however revealed a shift from more discrete encoding during learning to more generalized
encoding post-consolidation. Further, PrL neurons whose firing patterns were modulated by
rhinal and ventral hippocampal theta oscillations uncover a potential source of this change in
encoding across time. Specifically, I show that discrete populations of PrL neurons display
phase-locked spiking to theta oscillations in the lateral entorhinal cortex, the perirhinal cortex,
and ventral hippocampus. Whereas the proportions of these different populations were not
observed to change across time, the information contained within their activity did. PrL neurons
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phase-locked to rhinal theta oscillations were highly selective to task features early on in the
acquisition of the memory and became more generalized with learning and consolidation.
Neurons phase-locked to ventral hippocampal theta on the other hand became more selective for
the meaning of the stimulus post-consolidation. I discuss how these findings fit with existing
theories of the mPFC role in long-term memory including schema development and indexing of
cortical memory activity. Taken together, these results demonstrate memory encoding patterns
within the prelimbic mPFC and highlight, through communication with several important nodes
of the memory network, potential mechanisms through which the encoding of different memory
features evolve across learning.
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Acknowledgments
I would like to express my deepest appreciation to my supervisor, Dr. Kaori Takehara-Nishiuchi,
who gave me the opportunity to embark on a career in science, has provided numerous and
generous opportunities for my continual growth and has supported me immensely throughout my
graduate studies. Thank you for being such a wonderful mentor.
I would also like to thank my loving wife for all of her patience, support and encouragement, and
for pushing me to chase my dreams.
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Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents .............................................................................................................................v
List of Tables ................................................................................................................................. ix
List of Figures ..................................................................................................................................x
Chapter 1 ..........................................................................................................................................1
Introduction .................................................................................................................................1
1.1 Theories on network organization underlying episodic memory ........................................2
1.1.1 Hippocampal index theory .......................................................................................4
1.1.2 Theories of memory consolidation ..........................................................................8
1.2 Role of the medial prefrontal cortex in consolidated memory ..........................................11
1.2.1 Sub-regions of the medial prefrontal cortex ..........................................................12
1.2.2 Medial prefrontal cortex and consolidated memory ..............................................14
1.2.3 Activity of single prefrontal neurons .....................................................................19
1.2.4 Networks of long-term memory .............................................................................21
1.3 Dissertation Objectives ......................................................................................................27
1.3.1 Questions................................................................................................................27
1.3.2 In-vivo electrophysiology ......................................................................................28
1.3.3 Trace eyeblink conditioning ..................................................................................30
1.3.4 Specific aims ..........................................................................................................35
Chapter 2 ........................................................................................................................................37
Materials and Methods ..............................................................................................................37
2.1 Animals ..............................................................................................................................37
2.2 Electrode Construction.......................................................................................................37
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2.2.1 Electrodes for local field potential recording .........................................................37
2.2.2 Electrodes for single unit recording .......................................................................37
2.3 Surgical Procedures ...........................................................................................................38
2.3.1 Local field potential electrode implantation ..........................................................38
2.3.2 Microdrive-array and tetrode implantation ............................................................39
2.4 Adjustment of tetrode locations .........................................................................................39
2.5 Data Acquisition ................................................................................................................39
2.5.1 Trace eyeblink conditioning ..................................................................................40
2.5.2 Context Selectivity .................................................................................................41
2.5.3 EMG, LFP, and Unit recording..............................................................................41
2.5.4 Schedule .................................................................................................................42
2.6 Data Analysis .....................................................................................................................42
2.6.1 Behaviour analysis .................................................................................................42
2.6.2 Spike sorting ..........................................................................................................44
2.6.3 Firing rate analysis .................................................................................................44
2.6.4 Mutual information ................................................................................................46
2.6.5 Population firing rate matrix correlation................................................................47
2.6.6 Support vector machine learning ...........................................................................48
2.6.7 Phase-locked firings ...............................................................................................50
2.7 Statistical Analyses ............................................................................................................51
2.8 Histology ............................................................................................................................52
Chapter 3 ........................................................................................................................................53
Prelimbic Representations of Associative Memory Throughout Learning and
Consolidation ............................................................................................................................53
3.1 Introduction ........................................................................................................................53
3.2 Results ................................................................................................................................55
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3.2.1 Acquisition and expression of the conditioned response .......................................55
3.2.2 Single unit activity patterns ...................................................................................57
3.2.3 Changes in selectivity across time .........................................................................61
3.2.4 Population activity patterns....................................................................................65
3.2.5 Decoding Prelimbic population activity ................................................................69
3.2.6 Contextual encoding of a consolidated associative memory .................................74
3.3 Discussion ..........................................................................................................................79
3.3.1 Summary ................................................................................................................79
3.3.2 Single Neuron Selectivity ......................................................................................80
3.3.3 Population level encoding ......................................................................................80
3.3.4 Implications of the varying degrees and changes in selectivity observed .............81
Chapter 4 ........................................................................................................................................87
Prelimbic-cortical communication shaping changes in the selectivity of the prelimbic
ensemble code with consolidation ............................................................................................87
4.1 Introduction ........................................................................................................................87
4.2 Results ................................................................................................................................90
4.2.1 Prelimbic neuron phase-locking to the rhinal cortices and hippocampus ..............90
4.2.2 Prelimbic phase-locking is not feature specific .....................................................94
4.2.3 Regional phase-locking does not change across the learning stages .....................96
4.2.4 Decoding of region-specific phase-locking populations across time ....................97
4.3 Discussion ........................................................................................................................103
4.3.1 Summary ..............................................................................................................103
4.3.2 Functionally defined cell types in the prelimbic cortex .......................................104
4.3.3 Implications of the role of each afferent input during memory acquisition and
retrieval ................................................................................................................108
4.3.4 Differences in selectivity between functional cell-types .....................................110
Discussion ...............................................................................................................................113
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5.1 Summary ..........................................................................................................................113
5.2 Conclusions ......................................................................................................................115
5.2.1 Role of medial prefrontal cortex in memory........................................................115
5.2.2 Future directions ..................................................................................................120
References ....................................................................................................................................123
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List of Tables
Table 1. Correlations between population firing rates across conditions ..................................... 68
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List of Figures
Figure 1. Overview of hippocampal-cortical patterns of connectivity. .......................................... 8
Figure 3. Connectivity and Organization of a Circuit that may support Memory Consolidation
and Retrieval. ................................................................................................................................ 26
Figure 4. Experimental Procedure and Trace Eyeblink Conditioning. ......................................... 41
Figure 5. Acquisition and Expression of the Conditioned Eyeblink Response. ........................... 57
Figure 6. Histology and Single Unit Isolation. ............................................................................. 58
Figure 7. Single Neuron Selectivity for Task Features. ................................................................ 60
Figure 8. Behaviourally Defined Learning Stages. ....................................................................... 62
Figure 9. Selectivity Across Learning Stages. .............................................................................. 63
Figure 10. Mutual Information defined Selectivity. ..................................................................... 65
Figure 11. Prelimbic Neuron Population Matrices. ...................................................................... 67
Figure 12. Matrix Correlation Differences Between Relational and Identity Features. ............... 69
Figure 13. Support Vector Machine Classification Accuracy. ..................................................... 70
Figure 14. SVM Classification Accuracy changes for Stimulus Features Across Time. ............. 72
Figure 15. Context Manipulation Paradigm and Behavioural Results. ........................................ 75
Figure 16. Single Neuron and Population Selectivity for Contextual Features. ........................... 76
Figure 17. SVM decoding of Contextual Features. ...................................................................... 78
Figure 18. Histology and examples of prefrontal locking and the proportion of locked cells. .... 93
Figure 19. Prelimbic selectivity of phase-locking to specific regions and type of information
encoded by these cells. .................................................................................................................. 95
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Figure 20. Phase-locking across learning stages........................................................................... 97
Figure 21. SVM Classification Accuracy for Stimulus Features Across Time as a Function of
Functional Cell Types. ................................................................................................................ 102
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Chapter 1
Introduction
The memory system of the brain consists of a network of brain regions whose complexity
parallels the complexity of the underlying memory itself. Simple forms of reflexive memory may
only require a few circuits processing inputs and directing outputs outside of conscious
awareness (Christian & Thompson, 2003; Martinez, 2014; McCormick & Thompson, 1984).
Compare this to a memory of a previous event combining a vast amount of largely separate
sensory inputs, together containing information about the place and context in which something
occurred, detailed representations of features and stimuli, and the timing and order of events in
which they happened (Eichenbaum, Sauvage, Fortin, Komorowski, & Lipton, 2012;
Eichenbaum, Yonelinas, & Ranganath, 2007; Knierim, Neunuebel, & Deshmukh, 2013; Squire,
1992). Untangling the networks involved in the formation, storage and retrieval of this complex
form of memory, referred to as the episodic memory system, has been a difficult challenge for
the study of the brain. It requires an understanding of how the brain processes information,
untangling the complex circuitry involved in combining this information together, and finally
decoding the vast computations taking place within the brain, enabling the phenomenon of
retrieval or ‘remembering’. Several brain regions have been identified as crucial aspects of this
network and the goal of my dissertation was to deepen our understanding of the functioning of
one such region. Whereas the hippocampus, since its discovery as a fundamental node in the
episodic memory network, has been extensively studied for its role in memory formation and
retrieval, a region of the frontal cortex, the medial prefrontal cortex and its various sub-regions,
has relatively only recently become the focus of comprehensive investigation (Frankland &
Bontempi, 2005; Insel & Takehara-Nishiuchi, 2013).
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One of several prominent theories of the neuronal networks of memory formation and storage posits
that initial plasticity in the hippocampus rapidly forms the association among elements of an event,
and through bidirectional pathways between the hippocampus and neocortex, the memory gradually
becomes reorganized for long-term storage somewhere within the neocortex, independent of the
hippocampus (Alvarez & Squire, 1994; McClelland, McNaughton, & O’Reilly, 1995). Growing
evidence suggests that an area of the frontal cortex, the medial prefrontal cortex (mPFC), may play a
pivotal role in the neocortical network that supports stable event memories (Frankland & Bontempi,
2005; Insel & Takehara-Nishiuchi, 2013).
The main objective of my project was to characterize the aspects of an event memory that are
represented by the mPFC network and to investigate interactions between the mPFC and other
cortical structures of the memory network. Specifically I attempted to characterize the specific
aspects of an event that are encoded by prelimbic mPFC neurons, how this encoding may change
across time, from before learning the association to after the consolidation of the memory, and if
these representations are shaped by interactions with the rhinal cortices and hippocampus.
1.1 Theories on network organization underlying episodic memory
The memory systems of the brain can be divided by the type of information they contain, the neural
circuits governing their representation, and whether they can be explicitly recalled (‘declared’) or if
their recall lies outside of conscious awareness (Tulving, 1987). A common example is learning to
ride a bike, the ability of which is a form of procedural or skill learning likely involving the basal
ganglia circuitry, motor cortex and cerebellum (Bédard & Sanes, 2014; Charlesworth, Warren, &
Brainard, 2012; Hikosaka, Nakamura, Sakai, & Nakahara, 2002; Wiestler & Diedrichsen, 2013). The
retrieval of this memory, as demonstrated by producing the action, is completely independent from
the memory of the episode in which the learning took place, where you were, who was with you,
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what happened. These latter memories are classified as part of the explicit, or declarative memory
system, comprising episodic or event memories and rely on the functioning of the medial temporal
lobes. We know these two types of memory depend on independent systems because patients with
damage to the medial temporal lobes can still learn and retrieve procedural or skill memories despite
the inability to recall ever having learned them (Milner, 1959; Milner, 2005). Such patients are
shown to have a selective impairment in the episodic memory system (Milner, 1959; Squire, 1992;
Tulving, 1987) and studies of these types of patients were the first to inspire the notion that the
medial temporal lobes may serve a function specific to the storage and retrieval of episodic or
declarative memories (Scoville & Milner, 1957). Research using animal models has since made
significant contributions to our understanding of how these structures accomplish this.
Rodents do not have a medial temporal lobe, however the same structures exist in similar anatomical
positions with similar anatomical connectivity and function (Amaral & Witter, 1995; Manns &
Eichenbaum, 2006; Squire, 1992). Referred to more generally as the hippocampal system, in rodents
(and in humans and primates) the main structures consist of the hippocampus (dentate gyrus, CA1,
CA2, CA3, and subiculum), the entorhinal cortex, postrhinal cortex (parahippocampal in primates
and humans) and perirhinal cortex (Amaral & Witter, 1995; Burwell, 2000). However because
rodents cannot fulfill a key requirement of the declarative memory system according to the classic
psychological definition, that is, they can’t declare something, it is often questioned whether they
really have this type of memory and serve as good models for its investigation (Tulving, 2001).
Rodents can however be tested for their memory of a prior event, and exhibit savings of what, when,
and where something took place (Eichenbaum et al., 2007; Knierim et al., 2013). Using this
definition there is ample evidence to support the notion that they do have what is commonly called
episodic-like memory or memory that is a model of episodic memory and that the encoding and
retrieval of this type of memory requires the hippocampal system, analogous to that in humans
(Aggleton & Pearce, 2001; Bunsey & Eichenbaum, 1996; Manns & Eichenbaum, 2006; Rolls, 2010;
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Squire, 1992; Weisz, Rios, & Argibay, 2012). For example, rodents can be observed to form very
accurate memories of a spatial layout (Moser et al., 2014; O’Keefe, 1983; Rolls, Stringer, &
Trappenberg, 2002), or of events that transpired in a particular context (LeDoux, 1995; Penick &
Solomom, 1991), the formation and retrieval of which is severely disrupted by hippocampal
dysfunction (Squire, 1992).
Though it has long been acknowledged that the medial temporal lobe (hippocampal system) are
involved in memory, the hippocampal formation has remained at the forefront of memory research
since the initial discovery that this specific region of the brain may be the seat of episodic memory
formation (Milner, 2005; Scoville & Milner, 1957; Squire, 2009). The hippocampus has since been
studied in exquisite detail, from its anatomy, connectivity and physiological properties to its
involvement in a variety of tasks (Manns & Eichenbaum, 2006; Morris, 2001; Squire, 1992). Cells
within the hippocampus have been revealed to encode locations (Eichenbaum, Stewart, & Morris,
1990; O’Keefe, 1983; Wilson & McNaughton, 1993), associations (Liu et al., 2012), objects
(Komorowski, Manns, & Eichenbaum, 2009), even time (MacDonald, Lepage, Eden, & Eichenbaum,
2011). It was within the hippocampus where researchers discovered place cells, neurons that fire in a
particular place in a particular environment, and are proposed to form mental representations of space
(O’Keefe, 1983). It was also in the hippocampus where researchers discovered engram cells, neurons
active during an episode whose specific inactivation prevents recall of that episode, and whose
specific activation triggers retrieval of the event (Liu et al., 2012; Ramirez, Tonegawa, & Liu, 2014).
1.1.1 Hippocampal index theory
At its core, the most widely accepted view of hippocampal encoding proposes that the hippocampus
forms an index of the cortical activity that was present during the actual experiencing of an event
(Teyler & DiScenna, 1986) and that the content of a memory is actually stored in these distributed
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cortical networks (Damasio, 1989). Recent advances in the neurobiological toolbox have lent strong
support to this theory, for example cells active during a memory encoding experience were tagged
and genetically driven to express a light controlled proton pump (ArchT). When animals underwent
retrieval of the memory, specific inhibition of CA1 cells active during memory encoding prevented
retrieval of the memory and importantly, prevented the activation of cells in the surrounding cortical
regions that were also active during encoding (Tanaka et al., 2014).
More recently, efforts have begun to shift some focus onto the cortices surrounding the hippocampus,
which serve as a key node between the hippocampus and the rest of the neocortex. These cortices
provide direct access from the neocortical sites presumed to store memory, to the hippocampal
indices presumed to encode their activity patterns. Cortical inputs into the hippocampus travel almost
exclusively through these extra-hippocampal regions, and likewise, outputs from the hippocampus
back to the cortex again traverse these regions (Burwell, 2000; Witter, 2007; Witter, Hoesen, &
Amaral, 1989). The extra-hippocampal structures, as they are often referred, consist of the perirhinal
cortex, the postrhinal cortex and the entorhinal cortex. Anatomically they can be observed to be
arranged in a hierarchical like system, with the hippocampus at the top and the entorhinal cortex right
below (see Figure 1). Together in the next level of the hierarchy are the perirhinal and postrhinal
cortices which receive a variety of largely segregated inputs from various cortical regions and in turn
largely project to separate regions of the entorhinal cortex, in addition to some direct projections to
the hippocampus (Burwell & Amaral, 1998b). The perforant and temporo-ammonic pathways, the
primary cortical projections into the hippocampus, originate from layers II and III, respectively, of
the entorhinal cortex. Cortical projections back out from the hippocampus largely originate in area
CA1 and the subiculum and target the deep layers of the entorhinal cortex, which in turn projects
back down the hierarchy and to the various cortical targets (Agster & Burwell, 2009; Burwell &
Amaral, 1998a; Canto, Wouterlood, & Witter, 2008). Although the precise contributions of these
surrounding cortical regions to memory formation and retrieval is still being elucidated, it is evident
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their presence is necessary for the formation and retrieval of many types of event memories (Burwell,
2004; Cho, Beracochea, & Jaffard, 1993; Cho & Jaffard, 1995; Herfurth, Kasper, Schwarz, Stefan, &
Pauli, 2010; Manes, Graham, Zeman, de Luján Calcagno, & Hodges, 2005; Suzuki, Zola-Morgan,
Squire, & Amaral, 1993; Thornton, Rothblat, & Murray, 1997; Zola-Morgan, Squire, Amaral, &
Suzuki, 1989).
As incoming information about the environment moves from the primary sensory cortices to the
association cortices greater degrees of sensory integration can be found, and it is believed to be the
hippocampus that is the highest point of integration. A nice example of this varying degree of
integration can be seen in the rodent object recognition task. The simplest form of the task, novel
object recognition, tests a rodent’s memory for a previously encountered object, taking advantage of
their preference for exploring a novel versus a previously encountered object. To successfully
discriminate between a new and previously explored object requires a stored representation of the
encountered object and integration of the various features of the two current objects to detect which
is novel and which has been experienced. Perirhinal cortex lesions severely impair this simple novel
object recognition, implying a certain degree of integration taking place prior to this information
arriving in the hippocampus (Ennaceur, Neave, & Aggleton, 1996; Mumby & Pinel, 1994). The task
can be made more difficult by using the same object but presenting it in different locations in an
environment or in different environmental contexts. This type of object-location or object-context
test requires the conjunctive representation of the object and either the location or environment in
which it was encountered. Whereas lesions of the entorhinal cortex do not affect the simpler novel
object recognition paradigm, they severely impair performance in novel object-location and novel
object-context tests, suggesting that this region is important for these more complex conjunctive
representations requiring a higher level of integration (Wilson, Langston, et al., 2013; Wilson,
Watanabe, Milner, & Ainge, 2013). Introducing long delays between exploring and testing
sessions,adding an element of temporal order or a representation of location or context to these tests
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requires the hippocampus for normal performance (Clark, West, Zola, & Squire, 2001; Clark, Zola,
& Squire, 2000; Mumby, Gaskin, Glenn, Schramek, & Lehmann, 2002)
An explanation for these findings can be found in the anatomical connectivity and following the
route of different types of information into the hippocampus. Following a more rudimentary view,
two primary streams of information can be observed, a what and a where stream. Information about
objects and events is sent to the perirhinal cortex from uni- and polymodal association cortices,
which then project most heavily to the lateral aspects of the entorhinal cortex (LEC) as part of the
what stream. On the other hand, visuospatial information originating in the visual cortices, the
retrosplenial cortex and the parietal cortex is sent to the postrhinal cortex, which then projects most
heavily to the medial aspects of the entorhinal cortex (MEC) as part of the where stream. Projections
from the LEC and MEC to the hippocampus converge on the same cells in the dentate gyrus and
CA3, and diverge onto separate cells in the CA1 of the hippocampus. Feedback from the
hippocampus to the entorhinal cortex and connections between the LEC and MEC allow integration
of this somewhat separate information (Knierim et al., 2013). Therefore the increasing levels of
integration of different types of information observed in the behavioural paradigms described above
are supported by the anatomical organization of the hippocampal system. The hippocampus,
receiving varying levels of integrated information, can ultimately place events within a spatio-
temporal contextual structure, linking events, context and related memories (Eichenbaum et al.,
2012; Komorowski et al., 2009).
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Figure 1. Overview of hippocampal-cortical patterns of connectivity.
A general overview of the flow of information into and out of the hippocampus from various neocortical sites. Lines between structures indicate connectivity, and the thickness of the line indicates the strength of connections. HPC hippocampus; LEC lateral entorhinal cortex; MEC medial entorhinal cortex; PER perirhinal cortex; POR postrhinal cortex; mPFC medial prefrontal cortex; Tev Ventral temporal association area; AUD/p/v primary, ventral, posterior auditory areal; Ald/v/p dorsal, ventral, posterior agranular insular cortex; gustatory cortex; VIC visceral area; RSPd dorsal retrosplenial cortex; RSPv ventral retrosplenial cortex; PRLp posterior parietal association cortex; SSs supplementary somatosensory area; SSp primary somatosensory areal; VISl lateral visual cortex; VISm medial visual cortex; VISp primary visual cortex. Adapted from Agster and Burwell (2009).
1.1.2 Theories of memory consolidation
The idea that the hippocampus may not always be involved in the retrieval of a hippocampal
dependent memory goes back as far as the original evidence that the hippocampus was crucial for
memory formation (Scoville & Milner, 1957). It had been reported that patients with medial temporal
lobe amnesia showed evidence of preserved memories for information acquired long-before the
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disruption (Milner, 1959; Scoville & Milner, 1957). This led to the theory that over-time, memories
undergo a process of consolidation, wherein connections between cortical regions in which the
memory is presumed to preside are strengthened, and the role of the hippocampus to initiate the
retrieval of the memory decreases (Alvarez & Squire, 1994; McClelland et al., 1995). This is known
as the standard model of consolidation, and it has remained a contentious area of brain memory
research. Patient studies have often produced conflicting reports on the state of a person’s memory
for previous events after medial temporal lobe damage, even in the same patients (see Bayley, Gold,
Hopkins, & Squire, 2005; Moscovitch & Nadel, 1998; Nadel & Moscovitch, 1997; Squire, 1992). In
animals, the presence of similarly conflicting findings is evident, with many studies revealing a clear
temporal gradient after disruption of the hippocampal system (Cho & Jaffard, 1995; Kim, Clark, &
Thompson, 1995; Kim & Fanselow, 1992; Winocur, 1990; S. M. Zola-Morgan & Squire, 1990) and
others displaying a flat gradient (Bolhuis, Stewart, & Forrest, 1994; D. G. Mumby, Astur, Weisend,
& Sutherland, 1999; Sutherland et al., 2001). In both mice and rats, lesions of the hippocampus
impairing memory retrieval immediately after learning, when produced at 3-4 weeks after learning
have been observed to spare memory in contextual fear conditioning (Frankland et al., 2006), context
discrimination (Szu-Han Wang, Teixeira, Wheeler, & Frankland, 2009), spatial five-arm maze task
(Maviel, Durkin, Menzaghi, & Bontempi, 2004), trace eyeblink conditioning (Kim et al., 1995; Kaori
Takehara, Kawahara, & Kirino, 2003), trace fear conditioning (Quinn, Ma, Tinsley, Koch, &
Fanselow, 2008) and paired-associate memory (Dorothy Tse et al., 2007). In contrast, in many tests
requiring spatial navigation, like the Morris Water Maze, impairments are typically observed after
hippocampal lesions regardless of how long prior the memory was formed (Broadbent, Squire, &
Clark, 2006; Clark, Broadbent, & Squire, 2005; Gervais, Barrett-Bernstein, Sutherland, & Mumby,
2014; Winocur, Sekeres, Binns, & Moscovitch, 2013)
Such conflicts led to an alternative explanation of the findings, the multiple trace theory proposed
that the hippocampus would always be required for the retrieval of a memory that required the
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hippocampus for its formation. It was proposed that the re-activation and re-experiencing of these
memories creates additional indices in the hippocampus. Therefore, the more indices within the
hippocampus, the greater the probability that a trace of this memory will survive hippocampal
disruption, explaining some of the conflicting findings in the literature (Nadel & Moscovitch, 1997).
It also suggested that the creation of multiple indices, each providing contextual information for each
episode, allows for the neocortical extraction of the abstract or overlapping features of these episodes
independent of context. This suggests that abstract and semantic information could be retrieved
without the aid of the hippocampus, whereas discreet, contextual or autobiographical information
would always require the hippocampus for successful retrieval (Nadel & Moscovitch, 1997). Later,
the transformation theory extended this postulate by suggesting that a cortical gist-like or abstract
memory and a hippocampal detailed contextual memory dynamically interact, and depending on the
strength and retrieval circumstance, the dominance of one over the other can change (Winocur,
Moscovitch, & Bontempi, 2010). Many studies support this view (see Moscovitch et al., 2005) but
conflicting findings are also present; for example mice were observed to be able to successfully
discriminate between two contexts without a hippocampus 42 days after the formation of the
memory, but not 1 day after (Szu-Han Wang et al., 2009).
The nature of the time-limited role of the hippocampus and the nature of what memories are like
without a hippocampus is still fairly uncertain. However, what does seem clear, is that certain
memories can be retrieved independent of the hippocampus that was once essential for their
formation, and that this process requires both time and cortical plasticity (Frankland & Bontempi,
2005). Regardless of the nature of the long-term memory, both theories suggest cortical restructuring
supports the consolidation of memory and it is these changes that permit the retrieval of the memory
without input from the hippocampus (Alvarez & Squire, 1994; McClelland et al., 1995; Nadel &
Moscovitch, 1997; Winocur et al., 2010). Indeed, cortical plasticity over the period presumed to
encompass these changes has been shown to be necessary for the successful retrieval of remote
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memory. For example, CamKII mutant mice that displayed intact hippocampal LTP but impaired
cortical LTP were able to learn and retrieve a memory shortly after learning, but retrieval of the
memory several weeks after learning was significantly impaired (Frankland, O’Brien, Ohno,
Kirkwood, & Silva, 2001).
1.2 Role of the medial prefrontal cortex in consolidated memory
In one of the first examples of specific brain regions involved in remote memory retrieval, an
intensive study mapped the networks involved in remote memory retrieval in mice using (14C)2-
deoxyglucose to measure regional levels of glucose metabolism during memory retrieval in a spatial
discrimination task either 5-days or 25-days after acquisition of the task (Bontempi, Laurent-Demir,
Destrade, & Jaffard, 1999). The study confirmed decreased activity of the hippocampus at the 25-day
retrieval time-point and also identified several regions that unexpectedly showed increased activity at
this time-point compared to retrieval 5 days after learning. Increased activity was observed at the 25-
day (remote) time-point in the frontal cortex, the temporal cortex, and the anterior cingulate cortex
(Bontempi et al., 1999). The increased recruitment of frontal regions for older memories had also
been reported in human memory experiments, particularly the medial prefrontal cortex (Fink et al.,
1996; Markowitsch, 1995; Takashima et al., 2006).
At this time it was beginning to appear that the mPFC was a playing a unique role in memory
retrieval, as earlier interest in the mPFC had linked its function to short-term memory maintenance
(Kolb, 1984) and also in the formation of memory (Buckner, Kelley, & Petersen, 1999; Chachich &
Powell, 1998; Kronforst-Collins & Disterhoft, 1998; Meunier, Jaffard, & Destrade, 1991; Weible,
Weiss, & Disterhoft, 2003). This led to an analysis of the necessity of the mPFC (consisting of the
anterior cingulate AC, prelimbic PrL, and infralimbic IL cortices) for memory retrieval at different
time-points in trace eyeblink conditioning (Takehara et al., 2003). These experiments revealed little
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effect of mPFC lesions shortly after memory acquisition, but when the mPFC was lesioned several
weeks after learning, severe memory impairments were observed (Kaori Takehara et al., 2003).
Shortly after these findings increased immediate early gene activity (Fos & Zif268) was observed
within mPFC sub-regions during remote memory retrieval (Frankland, 2004; Maviel et al., 2004). In
addition, targeted reversible inaction of these sub-regions revealed specific involvement of the AC in
contextual fear conditioning (Frankland, 2004), the PrL cortex in trace eyeblink conditioning
(Takehara-Nishiuchi, Nakao, Kawahara, Matsuki, & Kirino, 2006), and the prelimbic and AC in the
spatial five-arm maze (Maviel et al., 2004) for remote but not recent memory retrieval. Similar
findings have since been observed in the Morris Water Maze (Lopez et al., 2012; Teixeira, Pomedli,
Maei, Kee, & Frankland, 2006), trace fear conditioning (Quinn et al., 2008) and paired-associate
memory (Wang, Tse, & Morris, 2012). Together, these findings suggest that over the course of
several weeks after learning a change in the memory network responsible for the retrieval of the
memory undergoes a shift. This implies several key points, 1) that there is likely dynamic
communication taking place between regions of the neocortex and the hippocampus throughout this
consolidation process (Maviel et al., 2004), 2) that the mPFC, particularly the PrL and AC appear to
play an enhanced role in older more remote memories, and 3) that mPFC-hippocampal interactions
may be important for this shift (Frankland & Bontempi, 2005).
1.2.1 Sub-regions of the medial prefrontal cortex
The medial prefrontal cortex (mPFC) of the rodent typically refers to a set of structures comprising
the rostral cingulate cortex (see Figure 2). This encompasses the most anterior aspects of the dorsal
anterior cingulate cortex (dAC), the prelimbic cortex (PrL), and the infralimbic cortex (IL). The area
immediately posterior can be classified as the caudal cingulate cortex and contains the caudal aspects
of the dorsal anterior cingulate, and the ventral anterior cingulate (vAC). Finally, the posterior
aspects of the cingulate consist of the dorsal and ventral retrosplenial cortices (dRS & vRS). Regions
13
of the mPFC are divided based on cytoarchitectural and morphological differences (Jones & Witter,
2007), differences in efferent and afferent projections (Hoover & Vertes, 2007; B. F. Jones & Witter,
2007; Vertes, 2004), and differential effects of their disruption on behaviour (Baldi & Bucherelli,
2015). For example PrL but not IL disruption impairs trace fear conditioning memory formation and
reconsolidation, whereas IL but not PrL disruption impairs the extinction of this memory (reviewed
in Baldi & Bucherelli, 2015). Though there are clear differences in patterns of inputs and outputs of
the sub-regions of the mPFC, there are also many overlapping similarities (Hoover & Vertes, 2007;
Jones & Witter, 2007). For example both the IL and PrL send projections to the amygdala, but the IL
targets the medial, basomedial, cortical and central nuclei of the amygdala, whereas the PrL only
projects to the central and basolateral amygdala (Vertes, 2004). Both regions send fairly dense
bilateral projections to the midline thalamus, including the paratenial, paraventricular,
medial/intermedial dorsal nucleus, interanteromedial nucleus, central medial nucleus, and nucleus
reuniens. Similarities also exist in cortical projections, both the PrL and IL project to the anterior
piriform cortex, the orbitomedial, insular cortex, and the entorhinal cortices (Vertes, 2004).
The mPFC also communicates strongly with the hippocampus. A direct monosynaptic unidirectional
pathway exists between the ventral hippocampus and mPFC (Hoover & Vertes, 2007; Vertes, 2004)
and a very recent study identified a direct pathway from the AC to the dorsal hippocampus that was
shown to have the capacity to drive memory retrieval (Rajasethupathy et al., 2015). The mPFC is
also strongly connected with the rhinal cortices, particularly the entorhinal cortex (EC) and perirhinal
cortex (PER) (Jones & Witter, 2007). The AC, PrL, and IL project primarily to the lateral aspects of
the EC, and to the perirhinal cortex, with very weak connectivity with the medial aspects of the EC
and the postrhinal cortex. The remaining structures of the caudal anterior cingulate and posterior
cingulate on the other hand show the inverse, with much stronger connections with the medial
entorhinal and postrhinal cortices than with the lateral and perirhinal cortices, implying their
differential involvement in the two primary streams of information what and where discussed earlier
14
(Jones & Witter, 2007). These connections and interactions with the hippocampal system are
proposed to be key to the process of consolidation, but the specific interactions driving this process
are still not clear (Frankland & Bontempi, 2005; Insel & Takehara-Nishiuchi, 2013; Preston &
Eichenbaum, 2013).
Figure 2. Flat-map of the Cingulate cortex Defining Sub-regions.
Illustration of a flat-map of the cingulate cortex cytoarchitectonic sub-divisions based on findings of Jones and Witter (2007). Colours designate common divisions of the medial prefrontal (pink) caudal anterior cingulate (purple) and retrosplenial cortices (blue). Abbreviations: vAC, ventral anterior cingulate cortex; dAC, dorsal anterior cingulate cortex; fx, fornix; hipp, hippocampus; IL, infralimbic cortex; PrL, prelimbic cortex; vRS, ventral retrosplenial cortex (RSv-a, RSvb: parts a and b respectively); dRS, dorsal retrosplenial cortex. Figure adapted from Jones and Witter (2007), Insel and Takehara-Nishiuchi (2012).
1.2.2 Medial prefrontal cortex and consolidated memory
Investigations into a shift in the memory network with consolidation has confirmed that plasticity
and cortical restructuring is taking place within the mPFC during a several week period after
learning, and that these changes rely on an intact hippocampus (Restivo, Vetere, Bontempi, &
Ammassari-Teule, 2009). Importantly, these changes have also been observed to be necessary for
successful memory retrieval at remote time-points (Takehara-Nishiuchi et al., 2006; Vetere et al.,
15
2011). In trace eyeblink conditioning, NMDA dependent plasticity mechanisms specifically within
the PrL cortex of the mPFC were shown to be essential for remote retrieval of memory during a two-
week period immediately after learning, but not after this time-point (Takehara-Nishiuchi et al.,
2006). Subsequently it was shown that dendritic spine growth (spinogenesis) within the AC shortly
after learning but not 6 weeks after learning was essential to remote memory retrieval in contextual
fear conditioning (Vetere et al., 2011). These findings reveal that not only are structural changes
taking place within mPFC sub-regions during the period presumed to be when consolidation of the
memory takes place, but that these changes are necessary for the formation of a long-term
representation of the memory in a state that can be successfully retrieved.
Within the mPFC, there are also many overlapping effects of lesions and inactivation of specific sub-
regions, likely because of the similar connection patterns just described. Studies of the effects of
lesions or inactivation of the mPFC on behaviour have produced somewhat variable results, likely a
result of differences in task features, compensation from spared mPFC regions, and inconsistencies
and imprecision in sub-region targeting (reviewed in Cassady 2014). This is confounded with the fact
that the mPFC seems to be important in many different tasks. Electrophysiological studies are
similarly confounded, as single neurons in the mPFC are observed to respond to many different
features in many different tasks, to varying degrees (Rigotti et al., 2013).
Differences between mPFC sub-regions in their role in the consolidation of memory and its long-
term retrieval has also remained fairly nebulous. For example, the necessity of the PrL but not the
AC region for remote memory in trace eyeblink conditioning has been demonstrated (Oswald,
Maddox, Tisdale, & Powell, 2010; Takehara-Nishiuchi et al., 2005, 2006). In contextual fear
memory, the AC but not PrL, was found to be important for remote memory retrieval (Frankland,
2004) and in a spatial memory task both PrL and AC inactivation produced impairments in remote
memory retrieval (Maviel et al., 2004). In trace fear conditioning, non-specific lesions of the mPFC
16
impair remote memory retrieval (Beeman, Bauer, Pierson, & Quinn, 2013; Quinn et al., 2008).
Disruption of the PrL specifically impairs remote retrieval in trace fear conditioning (Runyan,
Moore, & Dash, 2004) though to my knowledge the specific contributions of the AC and IL for
remote retrieval in this task has not been directly assessed. The relative contribution of these sub-
regions though still somewhat unclear, seem to be task specific, and the AC and PrL in particular
appear to be essential to the consolidation process.
The idea that a specific region of the brain would acquire a role in remote memory retrieval was not
originally predicted by theories of memory consolidation (Alvarez & Squire, 1994; McClelland et
al., 1995). Under this original view, cortico-cortical connections of the networks representing the
memory are slowly strengthened over time (McClelland et al., 1995). However the idea of the mPFC
becoming an essential node in this remote memory network suggests a revision to this model. It has
been proposed that at this more remote time-point of a memories representation in the brain that the
mPFC may be fulfilling a similar function as to that ascribed to the hippocampus for memory
formation and initial retrieval according to hippocampal index theory (Frankland & Bontempi, 2005;
Takehara-Nishiuchi & McNaughton, 2008).
An example of the benefit of such a system was recently demonstrated in studies of schema memory.
The original theory describing a rapid hippocampal driven memory acquisition system and a slower
neocortical consolidation system argued that the advantage of this system was to prevent the
‘catastrophic interference’ of existing information represented in neocortical synaptic connections if
new information was suddenly added to this neocortical network (McClelland, McNaughton, &
O'Reilly, 1995). However this idea was recently challenged in experiments on schema learning. A
schema is a framework of knowledge or information upon which new learning can be built (Bartlett
& Bartlett, 1995; Piaget, 1929). Rats trained to associate different flavours with the different
locations of rewards in an event arena developed a schema like memory structure (Tse et al., 2007).
17
Learning and retrieval of memories in this task required the hippocampus but after a typical
consolidation phase were shown to be retrievable without an intact hippocampus. When rats were
trained with a new cue-location pair that was similar to an existing consolidated schema, this new
information was observed to undergo a rapid consolidation process, in that the hippocampus, while
required for its acquisition, was only necessary for the retrieval of the memory for up to 2 day after
learning (Dorothy Tse et al., 2007). This finding suggested that new similar information can be added
rapidly to an existing memory framework. A recent extension of the original complementary learning
system theory (McClelland et al., 1995) shows that the rapid incorporation of similar information to
an existing memory framework is compatible with the original theory and does not result in
catastrophic interference (McClelland, 2013). Interestingly, this rapid incorporation was further
shown to depend on the mPFC (Tse et al., 2011; Wang et al., 2012). Therefore, this points to the
mPFC as a structure that is important for the long-term retrieval of memories and plays an important
role in building schemas or incorporating new information into existing knowledge structures
(Preston & Eichenbaum, 2013). This also points to the potential necessity of such a structure for the
long-term maintenance of memories, due to the sheer size and weak interconnectivity among
neocortical regions, for stored memories to be efficiently cued, retrieved, separated and updated,
likely requires the presence in this network of a brain region that can serve these functions over the
long-term.
This adds to growing evidence that suggests that newly formed memories are not merely transferred
to the cortex, but instead become integrated into existing neocortical memory networks or schemas,
through the modification and reorganization of these networks (McKenzie et al., 2014; Szu-Han
Wang & Morris, 2010a). If the mPFC becomes the central node in these networks, possibly retaining
an index code of neocortical memory patterns and building and updating schematic frameworks, the
question remains, is the reactivation of this now mPFC guided memory or schema the same as the
original hippocampal guided memory? Many have argued that it is not (Winocur & Moscovitch,
18
2011; Winocur et al., 2010). Whether the contents of a schema include only the abstract features of
common elements among integrated events (van Kesteren, Ruiter, Fernández, & Henson, 2012) or
whether a schema can contain detailed features specific to discrete events represented within a
related event network is not exactly clear (Preston & Eichenbaum, 2013). The mPFC has often been
linked to categorical, or rule based encoding (Durstewitz, Vittoz, Floresco, & Seamans, 2010; Rich &
Shapiro, 2009; Wallis, Anderson, & Miller, 2001). This may be accomplished by a more semantic-
like encoding, or extracting consistencies across events with the more distinctive aspects of each
event being lost (Richards et al., 2014).
On the other hand, the development of organized mental representations that share the properties of
schemas have been observed that contain details of individual events. For example, the Tse et al.
(2007) study showed that prior learning facilitates subsequent memory for details of new events that
fit existing knowledge, the incorporation and retrieval of which depends on the mPFC. Similarly,
mice trained in the Morris water maze where the platform locations were systematically changed
across training sessions were better able to incorporate representations of new distinct platform
locations once the original schematic framework was given enough time to develop (Richards et al.,
2014). Again this incorporation relied on processing within the mPFC (Richards et al., 2014). These
examples demonstrate that new information is not simply absorbed into the existing information in
an abstract manner but that details specific enough to solve a spatial maze task are preserved.
Additionally, the phenomenon of reconsolidation suggests that memories can be modified and
updated throughout their lifetime (Nader, 2015; K. Nader, Schafe, & Le Doux, 2000). In
reconsolidation, a previously consolidated memory, through its retrieval, enters a state of lability
which requires another round of consolidation to be restabilized. It has been suggested that
reconsolidation may reflect the reorganization of a schema to incorporate new information
(McKenzie & Eichenbaum, 2011). This opens up the possibility of memory updating, possibly
through prefrontal-hippocampal interactions to support, build and adapt overlapping mental
19
representations which contain enough details of specific events to detect conflicts between new and
existing information (McKenzie et al., 2014; Preston & Eichenbaum, 2013; Richards et al., 2014).
Trying to understand what features of a memory are represented by the mPFC and how consolidation
may change these representations were a main goal of my project. These previous investigations
examine memory representation indirectly based on behavioural observation. Here I will
systematically examine the level of specificity and abstraction of the prefrontal memory code directly
through the use of electrophysiological recordings.
1.2.3 Activity of single prefrontal neurons
One common overlapping feature across studies of the role of the mPFC sub-regions in memory
retrieval highlights its necessity when information must be bridged across a temporal delay. For
example, associating two discontiguous stimuli in trace conditioning, the PrL cortex is typically
seen to be important (Fuster, 2001; Gilmartin, Miyawaki, Helmstetter, & Diba, 2013). The PrL
region has been linked to working memory processes, and electrophysiological recordings from
this region reveal that PrL neurons will display persistent firing between two temporally
separated stimuli (Gilmartin & McEchron, 2005; Takehara-Nishiuchi & McNaughton, 2008). A
similar phenomenon is observed in neurons located in the primate dorsolateral prefrontal cortex
(dlPFC) during a delay period (Fuster, 1973). It is suggested that the PrL cortex of the rodent
which is more similar to the primate ventromedial pfrefrontal cortex (vmPFC) from an
anatomical standpoint, shares many similarities in function with the primate dlPFC and may be
viewed as a more primitive version of this primate frontal region (Seamans, Lapish, &
Durstewitz, 2008)
In trace fear conditioning acquisition, PrL neurons are observed to increase firing to the CS with
learning and exhibit persistent responses throughout the trace interval, IL neurons on the other
20
hand show learning related decreases in activity to the stimuli (Gilmartin & McEchron, 2005).
PrL neurons show persistent firing in in trace eyeblink conditioning as well, with the strength of
their activity and selectivity for the memory association gradually increasing across learning and
consolidation (Hattori, Yoon, Disterhoft, & Weiss, 2014; Takehara-Nishiuchi & McNaughton,
2008). Interestingly, the changes observed in PrL firing overlaps with the timing of severe
impairments of prelimbic inactivation on retrieval (Takehara-Nishiuchi et al., 2005) and with the
period during which NMDA receptor activity is necessary for successful long-term retrieval
(Takehara-Nishiuchi et al., 2006).
In addition to the association selective activity just described, single neurons in the mPFC have
been observed to encode objects within an environment (Weible, Rowland, Pang, & Kentros,
2009) and also to the place in which an object was previously presented (Weible, Rowland,
Monaghan, Wolfgang, & Kentros, 2012). In the novel-object recognition task, some single
anterior cingulate neurons that were responsive to an object in an environment, when placed
back in the environment in the object’s absence, continued to fire in the location where the object
had been. This effect increased with familiarization with the object and was still evident one-
month later (Weible et al., 2012), suggesting that these neurons also maintain long-term traces of
experience within environments.
In primates, medial prefrontal neurons have been shown to respond to many different stimuli in
many different tasks (di Pellegrino & Wise, 1993; Fuster, 1973; Fuster, Bodner, & Kroger, 2000;
Rao, Rainer, & Miller, 1997; Watanabe, 1996). Single prefrontal neurons have been shown to
encode concrete rules between specific stimuli and specific responses (White & Wise, 1999),
abstract rules (Wallis et al., 2001), categorical representations (Freedman, Riesenhuber, Poggio,
& Miller, 2001), and response strategies (Genovesio, Brasted, Mitz, & Wise, 2005). In many
21
cases this has been linked to a proposed role for this region in flexible cognition and decision
making. Likewise in rats, prelimbic mPFC neurons have been shown to show activity selective
for behavioural strategies, displaying activity dependent on the strategy used to solve a task
despite identical behaviour (Rich & Shapiro, 2009). Such encoding may be related to the
proposed abstraction of medial prefrontal memory representations and schema formation. In
these examples, many prefrontal neurons encode task relevant features in an abstract, categorical,
or rule-based manner, but others display stimulus selective activity, and some even show highly
selective conjunctive encoding (Wallis et al., 2001). In addition, all of these studies record from
animals well-trained with the stimuli and features of the employed task. It is not clear therefore,
if these abstract representations develop with repeated experience or if these generalized
representations are simply an intrinsic property of medial prefrontal encoding.
1.2.4 Networks of long-term memory
Besides unraveling the features of memory encoded by the mPFC and better understanding the state
of consolidated memory, another big question involves how the mPFC interacts with the
hippocampus and other regions of the episodic memory network to form and consolidate these
memories. The pathway between the hippocampus and prefrontal cortex is well known and proposed
to be the main driver behind the consolidation of hippocampal dependent memories (Frankland &
Bontempi, 2005; Preston & Eichenbaum, 2013; Simons & Spiers, 2003; Takehara-Nishiuchi, 2014).
However, how interactions between the prefrontal cortex, the hippocampus, and the
extrahippocampal structures determines this process is still uncertain. It is these interactions in
particular that have interested researchers and are proposed to be a possible mechanisms through
which hippocampal-prefrontal interactions supporting memory consolidation may occur (Insel &
Takehara-Nishiuchi, 2013; Preston & Eichenbaum, 2013; Takehara-Nishiuchi, 2014).
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1.2.4.1 Entorhinal Cortex
The entorhinal cortex (EC) sits as the primary interface between the hippocampus and the neocortex,
receiving a vast amount of cortical inputs, partially through strong connectivity with the perirhinal
and postrhinal cortices and also directly from an array or mono and polymodal association and
sensory cortices (Rebecca D. Burwell & Amaral, 1998a; Insausti, Herrero, & Witter, 1997).
Projections from many neocortical areas arrive in the superficial layers of the EC, this information is
then transferred into the hippocampus through direct connections from these same layers to the
dentate gyrus/CA3 and also the CA1. Projections from the CA1 and subiculum to the deep layers of
the EC in turn maintain the primary output pathway from the hippocampus back to the neocortex
(Kerr, Agster, Furtak, & Burwell, 2007; Witter, 2007; Witter et al., 1989). The EC is also
reciprocally connected with the mPFC, maintaining the same projection patterns as the entorhinal-
hippocampal connections. Projections to the mPFC originate in the superficial layers of the EC,
whereas output from the mPFC to the EC targets the deep layers of the EC (Apergis-Schoute, Pinto,
& Pare, 2006; Hoover & Vertes, 2007). Thus, anatomically the EC appears to be in a prime position
to interact with both the hippocampus and mPFC. Numerous findings demonstrate the role of the
entorhinal cortex in memory acquisition and retrieval (reviewed in Buzsáki & Moser, 2013;
Coutureau & Di Scala, 2009; Morrissey & Takehara-Nishiuchi, 2014). It was also noted that in
patients in which medial temporal lobe damage extends into the rhinal cortices much more extensive
retrograde amnesia is often observed (Bayley et al., 2005; S. Zola-Morgan et al., 1989). Similarly,
extended temporal gradients have been observed after entorhinal lesions in rodents (Cho et al., 1993;
Remondes & Schuman, 2004).
Our own work looked at the role of the entorhinal cortex in memory retrieval focusing on the
functions of entorhinal subdivisions. Based on structural anatomy and patterns of connectivity the EC
can be divided into medial (the more caudomedial aspects, MEC), and lateral (the rostrolateral
aspects, LEC) sub-regions (Canto et al., 2008). As briefly described earlier, the major inputs into the
23
EC provide a major source of division with the more dense inputs arriving from the nearby perirhinal
(PER) and postrhinal (POR) cortices. The POR maintains connections most heavily with visual and
spatial cortical areas such as the visual, posterior parietal and retrosplenial cortices and primarily
targets the medial portions of the entorhinal cortex. Whereas the PER tends to receive inputs from a
wide range of association areas such as the piriform, insular, cingulate, frontal, and temporal cortices,
and primarily targets the lateral portions of the entorhinal cortex (Rebecca D. Burwell & Amaral,
1998a). These anatomical patterns of connectivity can also be seen to continue as a topological
arrangement of connections between the MEC, LEC and the hippocampal formation (Canto et al.,
2008; Hargreaves, 2005).
These projection patterns have led to the suggestions that these sub-regions of the EC may serve
functionally distinct roles in hippocampal dependent memory (Hargreaves, Rao, Lee, & Knierim,
2005; Knierim et al., 2013). The most striking division between the MEC and LEC from a functional
standpoint comes from electrophysiological recordings during exploration in rodents where neurons
located within the most caudodorsal regions of the MEC are observed to fire in a grid-like pattern
within a spatial environment (grid cells) (Hafting, Fyhn, Molden, Moser, & Moser, 2005).
Conversely a near absence of such spatially responsive cells is observed in the LEC (Hargreaves et
al., 2005). These results suggest that the MEC but not the LEC, contributes to spatial memory
representation, they also raise a possibility that the LEC, but not MEC, would contribute to non-
spatial memory. To address this point, we assessed the relative contributions of the LEC and MEC to
the retrieval of both recent and remote memory in trace eyeblink conditioning, a non-spatial
associative memory paradigm (Morrissey, Maal-Bared, Brady, & Takehara-Nishiuchi, 2012). Here
we saw that LEC but not MEC inactivation impaired memory retrieval both 1-day and 4-weeks after
learning (Morrissey et al., 2012). It is thought that the LEC may play an important role in bridging
the gap between the CS and US, as persistent firing to a stimulus has been observed as an intrinsic
property of cells in this region (Tahvildari, Fransén, Alonso, & Hasselmo, 2007). In addition, lateral
24
entorhinal lesions were not observed to impact trace eyeblink acquisition with a shorter trace free
interval (Suter, Weiss, & Disterhoft, 2013). Using a longer trace interval, investigators showed that
LEC inactivation prior to conditioning impeded the acquisition of the association (Tanninen et al.,
2015).
These findings point to a role for the LEC in associative memory retrieval prior to and after the
consolidation of this memory, supporting the idea of the entorhinal cortex as an intermediary
between the hippocampus and mPFC cortex. Interestingly, EC-prefrontal connectivity patterns show
that the PrL is strongly connected with the LEC (and the perirhinal cortex), and sparsely to the MEC
(Jones & Witter, 2007). Simultaneous local field potential recordings from the hippocampus, LEC
and PrL during memory acquisition and consolidation in associative memory also reveal differences
in communication patterns across learning and remote retrieval (Takehara-Nishiuchi, Maal-Bared, &
Morrissey, 2012). Learning correlated synchronized oscillations in the theta frequency between the
LEC and hippocampus were high in the initial days of learning and decreased with retention. In
contrast, theta synchrony between the LEC and PrL was observed to gradually increase over-time,
remaining high during post-consolidation retrieval (Takehara-Nishiuchi et al., 2012). It has been
suggested therefore, that a key aspect of memory consolidation may be network modifications
between these circuits, with the entorhinal cortex providing neocortical access to the hippocampus
for memory formation and to the mPFC for the consolidation and long-term retrieval of these
memories (Insel & Takehara-Nishiuchi, 2013; Takehara-Nishiuchi, 2014).
1.2.4.2 Perirhinal cortex
The perirhinal cortex (PER) provides the LEC with the most dense input of all cortical regions
(Kerr et al., 2007) and like the LEC is also reciprocally connected with the prelimbic mPFC.
Fewer studies have investigated the role of the PER in memory consolidation, however plasticity
in and communication between the PER and mPFC was shown to be necessary for long-term
25
memory retrieval in object recognition memory (Barker & Warburton, 2008). In addition,
disruption of the PER was also shown to impair fear context memory up to 100 days after
training, suggesting that similar to the LEC, the PER may continue to play an active role in the
retrieval of consolidated memory (Burwell, 2004). The perirhinal cortex (PER) has also recently
been observed to be important in trace eyeblink memory acquisition (Suter et al., 2013).
Perirhinal interactions with the mPFC through electrophysiological recordings suggest the
communication between these regions is important in memory formation (Hannesson, Howland,
& Phillips, 2004; Paz, Bauer, & Pare, 2007). The role of the PER in the long-term memory
network is not exactly clear, however the current evidence suggests it may play an important
function.
1.2.4.3 Ventral hippocampus
Another important pathway that has suggested to be important for the role of the mPFC in memory
retrieval is the direct mPFC projection observed from the ventral hippocampus (vHPC) and
subiculum (Hoover & Vertes, 2007; Jones & Witter, 2007). vHPC-mPFC synchrony has been
observed in anxiety evoking situations (Adhikari, Topiwala, & Gordon, 2010) and in spatial memory
encoding (Spellman et al., 2015). The vHPC has been shown to be involved in acquisition and
retrieval of trace and contextual fear memory (Gilmartin, Kwapis, & Helmstetter, 2012; Raybuck &
Gould, 2010; Wang et al., 2013; Zhu et al., 2014). Few studies have directly examined the role of the
ventral hippocampus in long-term memory, but it was observed that trace fear conditioning memory
retrieval was not affected by ventral hippocampal lesions, but contextual fear memory retrieval
seemed to be similarly disrupted at both recent and remote time-points (Beeman et al., 2013). The
vHPC and the vHPC-mPFC interaction was also shown to be important for contextual fear memory
generalization. At remote memory time-points, fear memory generalization was related to mPFC as
well as vHPC activity, and disrupting the function of these regions impaired contextual
26
generalization (Cullen, Gilman, Winiecki, Riccio, & Jasnow, 2015). This finding is supported by
recordings from vHPC neurons, which in contrast to neurons in the dorsal hippocampus, gradually
develop context selective activity that forms a more generalized representation of contextual meaning
(Komorowski et al., 2013). Theta synchrony between the mPFC and dorsal hippocampus, a well
established property of hippocampal-prefrontal interactions(Adhikari et al., 2010;Jones & Wilson,
2005), has also been revealed to be modulated by ventral hippocampal activity (Adhikari, Topiwala,
& Gordon, 2011). Therefore it has been proposed that these ventral hippocampal-mPFC interactions
may support prefrontal dependent memory by building schema-like contextual representations
(Preston & Eichenbaum, 2013).
Figure 3. Connectivity and Organization of a Circuit that may support Memory
Consolidation and Retrieval.
Illustration of the connectivity of regions involved in memory formation, recent and remote memory retrieval linking the medial prefrontal cortex (mPFC), the hippocampal system and the various sensory and association cortices.
27
1.3 Dissertation Objectives
1.3.1 Questions
A strong case is being made for the importance of the mPFC in the consolidation of event memory
and as a crucial node in the long-term memory network (Frankland & Bontempi, 2005; Insel &
Takehara-Nishiuchi, 2013; Preston & Eichenbaum, 2013; Simons & Spiers, 2003). Uncovering what
the mPFC is encoding to permit the successful retrieval and consolidation of episodic memory will
form an important piece of our deepening understanding of the networks of memory in the brain.
Within the prelimbic cortex in particular, memory selective activity has been shown to gradually
develop over the course of learning and consolidation, remaining high during remote memory
retrieval (Hattori et al., 2014; Takehara-Nishiuchi & McNaughton, 2008). However, the following
questions remain.
1) What features of the memory are these cells encoding?
2) How does their encoding for particular features change over the course of learning and
consolidation?
3) How does their encoding of past situations influence their encoding of a similar new
situation?
4) How is their encoding shaped (or modulated) by incoming inputs from the hippocampal
system over the course of learning and consolidation ?
To address these questions, I have conducted a series of experiments in which I applied in vivo
electrophysiological approaches to a trace eyeblink conditioning paradigm in rats.
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1.3.2 In-vivo electrophysiology
Electrophysiological techniques offer a unique opportunity to study brain functions by
monitoring actual computations taking place within and between brain networks in line with the
temporal scale in which they occur. Placing very small electrodes into a brain regions of interest
allows for the monitoring of extracellular potential changes within the vicinity of electrode tips.
This technique, the monitoring of extracellular activity, can capture the activity of neurons in two
forms. The first type is highly dependent on electrode size, by using a wire thin enough that the
tip can sit between small groups of cells, action potentials (spikes or firings) of single neurons
can be detected. The second type detects the oscillatory activity of neuron populations, typically
the larger the electrode the larger the population. Action potentials reflect a neuron’s output, and
their frequency and temporal pattern allow for decoding types of information that each neuron
represents and computes. By comparing the frequency and timing of action potentials across
conditions in which task features are systematically manipulated, I will be able to decode in my
paradigm the particular task features a particular neuron is responsive to.
Oscillatory activity, on the other hand, reflects fluctuations in the excitability of local neurons
(that is, fluctuations in membrane potentials), thereby affecting the timing of local neurons’
firing (i.e., their output) as well as their sensitivity to incoming inputs. The oscillatory activity
can be decomposed to two dimensions, amplitude and phase. The amplitude of the oscillation
reflects the local synchronization of large populations of neurons , whereas the phase of the
oscillation, reflects the degree of excitability of the neurons, closer to or further away from
threshold, and influences the spike timing of the cells in the network ( Elbert, 1987). The
oscillatory activity is useful to probe over all activity of a recorded region as well as to probe
communication between distant brain regions. In particular, the correlated fluctuation of
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oscillatory amplitude (coherence) and the synchronization of oscillatory phase (phase
synchronization) between two regions are proposed to indicate temporal coupling of neuronal
processing by bringing these regions into the depolarization phase at the same time (Fell &
Axmacher, 2011). As the timing of a neuron spiking in one region can be modulated by the
particular phase of an oscillation in another region, neuron spiking can be locked to the excitable
phase of afferent inputs. Here the particular phase of an ongoing oscillations in one region can
affect the spiking of neurons in an afferent region. This phase-locking of neurons to oscillations
or spike-field coupling, has been a proposed mechanism for the flexible routing of neuronal
signals (Burchell et al., 1998), cell assembly formation (Singer, 1999), and coding (Konig et al.,
1995).
Oscillatory activity, measured as local field potentials (LFPs), can be decomposed to oscillations
at different frequency bands. Previous studies have assigned different functions to oscillations at
specific frequency bands. Among them, theta rhythms, 4-12 Hz oscillations, are prominent
within the hippocampus, have been consistently implicated in complex behaviours requiring
mnemonic processing in rodents, primates, and humans (Kahana, Sekuler, Caplan, Kirschen, &
Madsen, 1999; Lee, Simpson, Logothetis, & Rainer, 2005; O’Keefe & Recce, 1993; Skaggs,
McNaughton, Wilson, & Barnes, 1996; Vanderwolf, 1969) and are within the frequency range
proposed to synchronize areas across large distances (Buzsáki, 2002). Theta synchrony between
regions, when two regions’ ongoing theta oscillations become locked, has also been observed
between the hippocampus and many connected regions like the entorhinal cortex (Frank, Brown,
& Wilson, 2001), striatum (Berke, Okatan, Skurski, & Eichenbaum, 2004), amygdala
(Seidenbecher, Laxmi, Stork, & Pape, 2003), cerebellum (Hoffmann & Berry, 2009; Wikgren,
Nokia, & Penttonen, 2010) and prefrontal cortex (Anderson, Rajagovindan, Ghacibeh, Meador,
& Ding, 2010; Jones & Wilson, 2005). In trace eyeblink conditioning, 7-11 Hz synchronized
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theta oscillations between the hippocampus and the lateral entorhinal cortex and prelimbic cortex
revealed a network shift relevant to memory acquisition and expression (Takehara-Nishiuchi et
al., 2012). Additionally, prelimbic neurons have been observed to display phase-locked activity
to theta oscillations in both the dorsal (Siapas, Lubenov, & Wilson, 2005) and ventral (Adhikari
et al., 2010, 2011) hippocampus. Here I focused on the phase-locking of prelimbic neurons to the
ongoing theta oscillations (7-11 Hz) from several connected regions of the hippocampal system.
The electrophysiological approach is advantageous for my purposes for several reasons.
Importantly, with these data, I can observe the types of features single neurons in the prelimbic
medial prefrontal cortex may be encoding by actually monitoring their responses in real-time. By
sampling the activity of several hundred individual medial prefrontal neurons at different time-
points of learning, consolidation and retrieval in a memory task, I can obtain a snapshot of
memory relevant representations within these cells activity and observe how they may change
across these time-periods in the same animals. I can also relate this activity to the simultaneous
oscillations in several different brain regions to assess patterns of communication and how they
may support or be affected by memory consolidation.
1.3.3 Trace eyeblink conditioning
Classical eyeblink conditioning is a well-established model of associative memory. As a model it has
several advantageous features for the purposes of my study from both a technical and functional
standpoint. Eyeblink conditioning involves the repeated pairing of a conditioned stimulus (CS; e.g.
tone) with stimulation of the eyelid (unconditioned stimulus, US; e.g. airpuff or electrical
stimulation). Gradually over repeated pairings, presentation of the CS triggers the subject to produce
an adaptive eyeblink response (conditioned response, CR) in anticipation of the upcoming US. Delay
eyeblink conditioning consists of the paired presentation of an overlapping CS and US: the US-onset
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is slightly delayed from the CS-onset but the two co-terminate. The neural circuitry driving this
simple form of Pavlovian conditioning has been described in exquisite detail, requiring the brainstem
and the cerebellum for the timing of the CR and association of the CS and US (reviewed in Freeman
& Steinmetz, 2011; Thompson et al., 1997). However, the introduction of a small stimulus free trace
interval (several hundred milliseconds) in between the CS and US drastically changes the neural
circuitry involved in linking the two together (Christian & Thompson, 2003; Krupa, Thompson, &
Thompson, 1993; Woodruff-Pak & Disterhoft, 2008). In trace eyeblink conditioning (TEBC), as it is
known, making the CS and US temporally discontiguous requires the recruitment of higher order
brain structures to form the association. In this paradigm the brain must maintain a memory ‘trace’ of
the CS during this CS-US interval to enable the association of the CS with the upcoming US.
The hippocampus has been shown to be essential for the formation of TEBC, as hippocampal lesions
severely disrupt acquisition of the association between the CS and US in the trace paradigm (Beylin
et al., 2001; Moyer, Deyo, & Disterhoft, 1990; Solomon & Vander Schaaf, 1986; Weiss,
Bouwmeester, Power, & Disterhoft, 1999). Hippocampal lesions also severely impair the behavioural
expression of an already acquired association between the CS and US, provided the lesion is
produced shortly after acquisition (Kim et al., 1995; Takehara et al., 2003; Takehara, Kawahara,
Takatsuki, & Kirino, 2002). Similarly, when the lesion is produced immediately after acquisition, the
animal is also unable to reacquire the association (Takehara et al., 2003). It believed that the
hippocampus is essential in binding these temporally discontinuous stimuli, and it has been
demonstrated that the severity in impairment after hippocampal lesions is dependent upon the length
of the temporal interval between the CS and US, with greater impairments observed with longer
intervals (Tseng, Guan, Disterhoft, & Weiss, 2004; Walker & Steinmetz, 2008).
Among hippocampus-dependent memory paradigms used in rodents, TEBC is particularly
suitable for neurophysiological investigation and has many advantages as a model to study the neural
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mechanisms of learning and memory encoding in the brain. For one, it is not easily acquired in
rodents, requiring on average 6+ days with 100 trials per day to achieve asymptotic learning with
responses in over 60% of trials. This is advantageous for electrophysiological investigation as it
greatly increases the sample size compared to trace fear conditioning for example, in which learning
can take place in one session with only a few trials. Additionally, TEBC also offers the opportunity
to observe subtle changes in the encoding of the memory as the association is gradually acquired,
compared with memory paradigms in which memory formation occurs very rapidly. These
advantages are also afforded by the fact that TEBC contains precise time-stamped events (CS-
presentation) to easily relate neural responses to. In fact, many studies have successfully used this
paradigm to uncover neuronal representation in the hippocampus formed during and after trace
eyeblink conditioning (McEchron, Weible, & Disterhoft, 2001; McEchron & Disterhoft, 1997;
Munera, Gruart, Munoz, Fernandez-Mas, & Delgado-Garcia, 2001; Weible et al., 2006; Weiss,
Kronforst-Collins, & Disterhoft, 1996).
Furthermore, this paradigm can be directly applicable to human subjects. Importantly, in
humans, the acquisition of the association in TEBC also relies on proper hippocampal functioning
(McGlinchey-Berroth, Carrillo, Gabrieli, Brawn, & Disterhoft, 1997; Woodruff-Pak, 1993). Another
key feature in humans is that the successful acquisition of the trace procedure depends on an
awareness of the temporal relationship between the CS and US, awareness being an analogue of
conscious knowledge and a key defining feature of declarative or episodic memory (Clark & Squire,
1999). Considering this evidence trace eyeblink conditioning in rodents has been suggested to be a
good model of episodic memory and therefore useful to dissect the neural circuitry and
representations of episodic memory in the brain (Woodruff-Pak & Disterhoft, 2008).
In addition to the hippocampus, the number of forebrain regions are involved in memory
acquisition and later expression in trace eyeblink conditioning. These include the rhinal cortices
33
(Ryou et al., 2001; Suter et al., 2013; Tanninen et al., in press), various sensory cortices (Galvez,
Weible, & Disterhoft, 2007; Steinmetz, Harmon, & Freeman, 2013), the caudate nucleus (Flores
& Disterhoft, 2009 & 2013), and several thalamic nuclei (Oswald, Knuckley, Maddox, &
Powell, 2007; Powell & Churchwell, 2002). The medial prefrontal cortex is also involved in TEBC
memory formation, as its disruption can slow acquisition of the association (Kronforst-Collins &
Disterhoft, 1998; McLaughlin, Skaggs, Churchwell, & Powell, 2002; Takehara-Nishiuchi et al.,
2005; Weible, McEchron, & Disterhoft, 2000), and it has been used to differentiate functional roles
among many prefrontal regions. For example, in contrast to arrested acquisition after mPFC lesions,
lesions of the caudal anterior cingulate (cAC) are shown to severely disrupt TEBC acquisition
(Kronforst-Collins & Disterhoft, 1998; Weible et al., 2000). The importance of several of these
frontal regions in this task have also been shown to be memory stage dependent. Lesions to the
anterior portions of the anterior cingulate (dAC) and the prelimbic mPFC (PrL) which only mildly
disrupt the speed of acquisition, severely impair expression of the acquired memory, particularly
when the damage is induced several days to several weeks after learning (Oswald, Maddox, &
Powell, 2008; Oswald et al., 2010; Takehara-Nishiuchi et al., 2006). Interestingly, lesions to cAC
shown to severely disrupt acquisition, had no impairment when performed one-week after learning
(Oswald et al., 2010).
Single neuron recordings from these regions in TEBC have further advanced our understanding of
their functioning in this task, as reviewed in Chapter 1.2.3. Neurons located within the cAC show
strong firing changes to the CS in TEBC during the initial acquisition sessions, however this
activity is observed to decrease with learning (Hattori et al., 2014; Weible et al., 2003). Firing
patterns from neurons located within the rostral dAC and PrL on the other hand, show strong
changes to the CS that is maintained across the trace interval (Hattori et al., 2014; Siegel,
Kalmbach, Chitwood, & Mauk, 2012; Siegel & Mauk, 2013; Takehara-Nishiuchi &
34
McNaughton, 2008). Such persistent activity continues to the onset of the US across the temporal
gap in which no stimuli are presented and is observed to become stronger as the CR is acquired
and consolidated (Hattori et al., 2014; Siegel et al., 2012; Siegel & Mauk, 2013; Takehara-
Nishiuchi & McNaughton, 2008). Prelimbic neurons on the other hand show persistent responses
during the trace interval between the CS and US in trace eyeblink conditioning, and the
magnitude of this response increases and becomes more selective to the memory association over
the course of the consolidation period (Hattori et al., 2014; Takehara-Nishiuchi & McNaughton,
2008), very similar to the changes observed in the dorsal hippocampus during the initial
acquisition of the memory (Weible, O’Reilly, Weiss, & Disterhoft, 2006).
In a similar study also using trace eyeblink conditioning, neurons located in the prelimbic, rostral
dorsal anterior cingulate (rAC) and caudal dorsal anterior cingulate (cAC) were recorded
simultaneously (Hattori et al., 2014). Clear differences emerged across time in the response
profiles of the neurons from the different regions. Prelimbic neurons, consistent with Takehara &
McNaughton (2008), gradually acquired sustained firing rate changes during the trace interval,
presumably bridging the gap between the CS and US. In contrast, cAC neurons displayed strong
robust responses to the CS early in learning, but these tended to fade with learning and
consolidation. Neurons in the rAC displayed decreases in responsivity within learning sessions,
something not observed in the PrL or cAC. The authors suggest that in this task, the different
regions of the mPFC may work together during the formation and retrieval of the memory, with
the rAC providing salience signals to ready the animal and the rest of the mPFC network for the
association to be formed, the cAC driving attention and stimulus selection, and PrL cells in this
task may be encoding the temporal or associative features of the memory eventually becoming
the key node in retrieval (Hattori et al., 2014). In my project I focus specifically on the prelimbic
35
region of the mPFC based on the previous findings in the trace eyeblink conditioning task, the
same task employed here.
1.3.4 Specific aims
To examine which features of a memory prelimbic mPFC cells are encoding, I have conducted
multiple analyses of single prelimbic neuron activity as well as their population activity with two
specific aims. The first aim was to identify which aspects of the memory these neurons are selective
for and how these representations evolve across the learning and consolidation phases of event
memory (Chapter 3). I compared firing changes across the conditions in terms of the number of
selective neurons as well as the degree of selectivity in each neuron. I also applied correlation
analyses and multivariate analyses on firings of neuron populations to quantify the degree of
selectivity of population firing patterns. These analyses revealed that the prelimbic ensemble consists
of neurons with a range of selectivity, some highly selective for task features while others more
generalized for stimulus meaning, and a similar proportion of each was observed at all time-points
studied. The information encoded within the population of neurons recorded, however, did appear to
change across time, from highly selective to discrete features early in learning, to more generalized
response patterns post consolidation.
The second aim was to investigate how interactions with other cortical regions involved in
this memory may shape these prelimbic representations, and if these interactions encourage changes
in prelimbic representations throughout learning and consolidation (Chapter 4). To address this I
additionally recorded local field potential activity from the lateral entorhinal cortex, perirhinal cortex,
and ventral hippocampus simultaneously with the single prelimbic recordings during our memory
paradigm. I was then able to examine whether prelimbic neurons firing was modulated by oscillatory
activity in these different brain regions, whether this changed across the different stages of the
memory, and whether particular regions support different types of prelimbic selectivity. These
36
analyses revealed that fairly distinct populations of prelimbic neurons display event related activity
that is modulated by the local oscillatory activity in each of the targeted regions. Again the
proportions of these populations did not change across the course of the learning paradigm, but their
population activity revealed changes in the information encoded by these neurons depending on their
preferred contact and the state of the memory across the phases of learning and consolidation.
Specifically, neurons modulated by lateral entorhinal, perirhinal, or ventral hippocampal theta
oscillations became less selective for discrete stimulus features across time, but neurons modulated
by the ventral hippocampus in particular became more selective for the association over-time.
Collectively I aimed to and revealed prelimbic mPFC encoding of memory features, expanding
knowledge of how this region may support consolidation and post-consolidation retrieval and how
other cortical regions may factor in to this role by monitoring actual computations in the neocortex.
37
Chapter 2
Materials and Methods
2.1 Animals
All experiments were performed on male Long-Evans rats (Charles River Laboratories, St. Constant,
QC, Canada) between 16-25 weeks old at the time of surgery. A total of 15 rats underwent the
experimental procedure, however 9 had to be removed from the final analysis due to various technical
and mechanical issues. Rats were housed individually in Plexiglass cages and maintained on a reversed
12-hour light/dark cycle. Water and food was available ad libitum. All methods were approved by the
Animal Care and Use Committee at the University of Toronto.
2.2 Electrode Construction
2.2.1 Electrodes for local field potential recording
Electrodes were made in-house using Teflon-coated stainless steel wire (AM Systems) and 26 gauge
stainless steel cannula (Amazon Supply) and tailored for each brain target. Two wires were inserted
into the stainless steel cannula offset in the dorsal-ventral axis by .45-.7 mm depending on the target
and secured with cyanoacrylate.
2.2.2 Electrodes for single unit recording
Tetrodes were made in-house by twisting together four 12 μm polyimide coated nichrome wires
(Sandvik). To permit independently adjustable tetrode depths, each tetrode was housed inside a screw
operated microdrive. The complete Microdrive-array consisted of a bundle of 12 microdrives, each
guiding a tetrode, contained within a 3d printed plastic base (Kloosterman et al., 2009). The
Microdrive-array also enclosed the Electrode Interface Board (EIB-54-Kopf, Neuralynx) to which all
38
electrodes were connected and served as the interface between the recording and stimulating electrodes
and the recording system. Prior to implantation, the impedance of the nichrome tetrode wires were
reduced to ~250 khOms by electroplating them with gold. Tetrodes were then drawn inside a 1.8 mm
diameter stainless steel cannula at the Micro-drive array base and a small drop of sterilized mineral oil
was added to ensure smooth movement of the tetrodes after implantation.
2.3 Surgical Procedures
Following guidelines set by the Institutional Animal Care Committee at the University of Toronto, all
surgeries were conducted under aseptic conditions in a sterile surgical suite. For the chronic
implantation of electrodes and the Microdrive Array, rats were anaesthetized under isoflurane (1–
1.5% by volume in oxygen at a flow rate of 1.5 L/min; Holocarbon Laboratories, River Edge, NJ)
and placed in a stereotaxic holder with the skull surface in the horizontal plane. For post-operative
analgesia rats were given a subcutaneous injection of ketoprofen (1.5mg/kg). The shaved head was
cleaned with 70% ethanol followed by Betadine and the skull surface exposed with an incision in the
anterior-posterior direction along the midline with a scalpel blade.
2.3.1 Local field potential electrode implantation
To chronically implant electrodes for long-term LFP recordings small burr holes were opened in the
skull using a micro-drill (.6mm) to target the lateral entorhinal cortex (LEC), perirhinal cortex (PER)
and ventral hippocampus (vHPC) at the following coordinates relative to bregma: for LEC, 6.45 mm
posterior, 5.4 mm lateral; for PER, 6.0 mm posterior/ 6.65 mm lateral; for vHPC, 5.15 mm posterior/
4.8 mm lateral. Several more burr holes were created around the skull to hold stainless steel screws
(Morris Screw) to anchor the implantations. For each brain region the electrodes were carefully
lowered into the brain at each drill site. LEC electrodes were lowered at a 10° lateral angle to 9.95
mm ventral to bregma, PER electrodes lowered straight to 7.75 mm ventral to bregma, and vHPC
electrodes were lowered to 8.45 mm ventral to bregma. Electrodes were held in place using self-
39
curing dental acrylic. For referencing of local field potential activity, a stainless steel screw wired to
the electrode interface board was implanted in the surface of the cerebellum.
2.3.2 Microdrive-array and tetrode implantation
All tetrodes were targeted to the prelimbic medial prefrontal cortex (PrL mPFC). To implant the
tetrode bundle, a craniotomy was opened over the PrL mPFC at 3.2 mm anterior and 1.4 mm lateral
to bregma and the dura matter removed. The Micro-drive array was then lowered at a 9.5° medial
angle until the base made contact with the surface of the brain. The craniotomy was then sealed with
Kwik-Sil and the array was held in place with self-curing dental acrylic.
2.4 Adjustment of tetrode locations
Immediately after the surgery, all tetrodes were lowered 1 mm into the brain. For the next 3-4 weeks
the rat was connected to the system each day to visualize the quality of activity and monitor
movement of the tetrodes. Each tetrode was lowered slightly each day (75-125 μm) over the course
of this 3-4 week period to target tetrodes tips to the PrL mPFC at 3.0-4.0 mm ventral from the brain
surface. This procedure increases the yield of neurons and stability of the signal during the recording.
One tetrode was positioned superficially in the cortex (1 mm below brain surface) to serve as a
reference electrode for single-unit activity. Once the recordings began, tetrode position was adjusted
only as necessary to obtain good quality high yield recordings. It is therefore possible that recordings
were repeated for some neurons across days. Tetrode position adjustments were only made after a
given recording session providing ~ 24 hours for the tetrode to stabilize before the next recording,
2.5 Data Acquisition
All rats experienced the same general experimental procedure. Beginning 3-4 weeks following
Micro-drive array implantation, when stable single unit recordings were achieved and tetrodes were
40
positioned within the PrL mPFC, rats were subjected to daily conditioning in the trace eyeblink
paradigm.
2.5.1 Trace eyeblink conditioning
Rats were placed in a large dark rectangular box, fitted with an LED light source and speaker. Within
the box rats were enclosed in a square plexiglass container, fitted with holes on one side to enable
sound-waves from the speaker to enter the enclosure. The conditioned stimulus (CS) was presented
for 100-msec and consisted of an auditory stimulus (85-dB, 2.5-kHz tone) or a visual stimulus (white
LED light blinking at 50 Hz). The unconditioned stimulus (US) was a mild electrical shock to the
eyelid (100 Hz square pulse, 0.3-2.0 mA, US), and the intensity carefully monitored via webcam and
adjusted to ensure a proper eyeblink/headturn response.
Conditioning took place over two sessions, each with 100 trials, separated by a 10 minute rest period.
Each session consisted of 20 presentations of the CS alone, followed by 80 trials in which the CS is
paired with the US, separated by a stimulus-free interval of 500 msec (see Figure 4). The first session
used only one of the two CS (e.g. tone), and the second session used the other CS (e.g. light), with
the CS order and schedule pseudorandomized across days and across rats. This design provided four
conditions for comparison: Tone-Alone, Tone-Paired, Light-Alone, and Light-Paired. Before and
after each conditioning session the rat was placed in a comfortable rest box separate from the
conditioning box for 10 minutes.
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Figure 4. Experimental Procedure and Trace Eyeblink Conditioning.
Diagram of the experimental paradigm (left) wherein rats underwent two conditioning sessions daily in the same conditioning chamber (Box A). Each session consisted of 100 trials of conditioned stimulus (CS) presentations (Light or Tone) in which the first 20 trials the CS was presented alone and the last 80 trials the CS was paired with the unconditioned stimulus (US). On the right, a diagram of the trace eyeblink conditioning procedure wherein the offset of the CS and the onset of the US were separated by a 500 millisecond stimulus-free interval. The bottom figure displays an example of an adaptive eyeblink conditioned response (CR) monitored with electromyogram (EMG) eyelid activity observed as an increase in signal amplitude immediately before the US onset. Artefact from the US was ignored in the analysis of EMG activity.
2.5.2 Context Selectivity
In several animals (n = 3), upon completion of the full conditioning procedure, I investigated whether
prelimbic neurons would show a high degree of selectivity for contextual features. Animals
underwent a similar conditioning procedure over three days in which the conditioning context was
manipulated but the conditioning stimulus remained the same. In two sessions, rats underwent tone
trace eyeblink conditioning with the first 20 trials being Tone-Alone and the last 80 trials Tone-
Paired. One session was run in the same context as the previous 30+ days of conditioning (Context
A) in a dark box with brown floors and plain walls, in the other session the conditioning took place in
a box in which the visual and textile features were manipulated (Context B; light box, stripped walls,
white floor) (see Figure 15). The session order was pseudorandomized across rats. Firing rates were
compared and analyzed in the same manner as described above, but here looking at selectivity for the
environmental context, the relationship, or their conjunction.
2.5.3 EMG, LFP, and Unit recording
During the daily conditioning sessions, action potentials from individual neurons in the prelimbic
medial prefrontal cortex, local field potentials (LFPs) in the lateral entorhinal cortex, perirhinal
cortex, ventral hippocampus and mPFC, and electromyogram (EMG) activity from the eyelid were
simultaneously recorded. Action potentials were captured using the tetrode technique, which allows
42
for recording the activity of many individual neurons per recording session (Gray, Maldonado,
Wilson, & McNaughton, 1995; Wilson & McNaughton, 1993). Experimental rats were connected to
the system through an Electrode Interface Board (EIB-54-Kopf, Neuralynx, Bozeman, MT)
contained within the Micro-drive array fixed to the animals head. The EIB was connected to a
headstage (HS-54, Neuralynx, Bozeman, MT) and signals were acquired through the Cheetah Data
Acquisition System (Digital Lynx and Cheetah Software, Neuralynx, Bozeman, MT). A single
tetrode consists of 4 individual micro-wires spun together each of which measures rapid changes in
extracellular electrical potential. A threshold voltage was set at 40-50 mV, and if the voltage on any
channel exceeded this threshold, activity would be collected from all four channels. Spiking activity
of single neurons was sampled for 1 ms at 32 kHz and signals were amplified and filtered between
600- 6000 Hz. LFP activity and EMG activity was continuously sampled and filtered between .1-
400 Hz and 300-3000 Hz, respectively.
2.5.4 Schedule
Recordings and data acquisition began on the first day of conditioning and continued for 30+ days.
During the first 3 days of the experimental procedure the rat was habituated to the apparatus, with
one session each on the first 2 days and the full procedure on the third day, all done with no
presentation of either CS or US. These habituation sessions also served to assess spontaneous
eyeblinks in the absence of conditioning stimuli.
2.6 Data Analysis
2.6.1 Behaviour analysis
The adaptive conditioned eyeblink response (CR) which represents the learning of the association
between the conditioned (CS) and unconditioned (US) stimuli was assessed through the analysis of
electromyogram (EMG) activity recorded from the upper left eye-lid muscle. Each trial was assessed
offline with custom codes (Matlab) for the presence of a conditioned response (CR) detected as a
43
significant increase in eyelid EMG amplitude immediately before US onset (Morrissey et al., 2012;
Takehara-Nishiuchi & McNaughton, 2008). Specifically, EMG activity was sampled around the
presentation of the conditioned stimulus in each trial and the instantaneous amplitude of the signal
was calculated as the absolute value of the Hilbert transform of the filtered signal (using the
hilbert function in Matlab). For each trial, the average amplitude during a 300-ms period
immediately before CS-presentation was defined as the Pre-Value and the averaged amplitude during
a 200-ms period immediately before US-presentation was defined as the CR-Value. A Threshold
value was set as the averaged Pre-Value plus 2 standard deviations. For a given trial, if the CR-Value
exceeded the Pre-Value and the Threshold, that trial was classified as containing a CR. However if
the Pre-Value for a given trial exceeded the Threshold value, the trial was classified as hyperactive
and discarded. The ratio of trials containing a CR within each condition to the total number of valid
trials within each condition represented the CR% for a given condition (CS-Alone, CS-Paired) for
each session (Tone, Light).
To assess changes in neuron activity across the various stages of learning and long-term memory
storage, time-points were defined within each individual rat according to their acquisition and
expression of the conditioned eyeblink response (CR). These criteria were selected based on
observation of general patterns of CR acquisition and expression across many animals. In the first
few days of training, rats show very few trials in which they exhibit the CR, I define this period as
Before Learning, i.e. before the animal has begun to associate the CS with the US. However once rats
begin to form this association the percentage of trials in which they exhibit a CR rapidly increases
but can fluctuate greatly across days, I define this period as During Learning. Eventually the rats
reach a point in which their responding plateaus and reaches asymptote, from this point on I
generally see small fluctuations in response rate across days but rarely see large deviations. This
point defines the end of this During Learning period. All days past this point are defined in weeks
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Post Learning. To operationally define these learning stages I set a threshold of responding. All days
prior to the rat displaying a conditioned response in 30% of trails are defined as Before Learning. All
days following two consecutive days of the rat displaying a CR in 60% of trials are defined in weeks
Post Learning. All days in between Before Learning and the beginning of the Post Learning stage are
defined as During Learning (see Figure 8).
2.6.2 Spike sorting
Putative single neurons were isolated offline using a specialized software package in Matlab
(KlustaKwik, author: K.D. Harris, Rutgers, The State University of New Jersey, Newark, NJ;
MClust, author: D.A. Redish, University of Minnesota, Minneapolis, MN; Waveform Cutter,
author: S.L. Cowen, University of Arizona, Tucson, AZ). Before the activity of individual
neurons can be analyzed, signal contributions from background noise and other nearby neurons
must be disentangled in a process known as spike sorting. The software used to achieve this
combines both automatic spike-sorting and manual sorting allowing for sorting clusters based on
the relative action potential amplitudes on the different tetrode channels and various other
waveform parameters including peak/valley amplitudes, energy, and waveform principle
components (Gray et al., 1995). The final result was a collection of time stamps associated with
each action potential from a given neuron. Only neurons with < 1% of inter stimulus intervals
distribution falling within a 2 msec refractory period were used in the final analysis.
2.6.3 Firing rate analysis
Activity from tetrodes was processed offline using spike sorting algorithms to isolate the activity
of putative single neurons (units). The result was a collection of putative single neurons for a
given days recording, each represented by timestamps corresponding to action potentials for the
entire length of the recording. The number of action potentials (spikes) from a given neuron
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during a series of 1-msec bins which cover a 4-second period (from 2 seconds before CS-onset to
2-seconds after CS-onset) was counted in each trial for each of the four conditions. Activity
during the presentation of the US and shortly after was ignored due to electrical artifacts from the
stimulating electrodes. If a neuron did not show a spike in more than 15 trials in either the tone
or light paired conditions, the neuron was removed from further analyses.
To determine the selectivity of prelimbic neurons I directly compared firing rate changes relative
to baseline across the four conditions within each individual neuron. For each neuron, mean
spike counts across trials during a baseline period (500-msec immediately before CS-onset) in
the tone and light paired conditions determined the baseline firing rates. A neuron was classified
as responsive if the averaged firing rate during the CS-period (100-msec) or during the trace
interval (TI: 500-msec period between CS and US presentations) was different from baseline
using the students t test with Bonferroni correction (p < .025) in either the tone or light
conditions. Neurons whose firing rate during the CS or trace interval did not significantly change
from baseline in Paired trials were classified as Non-Responsive.
To determine selectivity, firing rates during the CS and trace intervals were compared across the
four conditions (Tone: CS-alone, CS-paired; Light: CS-alone, CS-paired) for responsive neurons.
To correct for differences in baseline activity between the four conditions, firing rates were
standardized to mean baseline firing rates and converted to z-scores: �̃�𝑟𝑒𝑠𝑝−�̃�𝑏𝑎𝑠𝑒
𝑆𝐷𝑏𝑎𝑠𝑒 where �̃�𝑟𝑒𝑠𝑝
equals the mean of the firing rate during CS or the TI from a given condition, �̃�𝑏𝑎𝑠𝑒 equals the
mean of the firing rate during the 500 msec period immediately prior to CS onset and 𝑆𝐷𝑏𝑎𝑠𝑒 is
the standard deviation of the firing rate during this Pre-CS baseline period in the same condition.
These standardized firing rates were then compared across conditions to determine if the neuron
shows selectivity for the CS-US association (Relationship, CS-Alone ≠ CS-Paired), for a
46
particular CS (Identity, Light ≠ Tone) and any combination of the two (Conjunctive, RxI). Again
these comparisons were done using paired-sample t-tests with Bonferroni correction. The results
of this characterization will provide information about the overall selectivity of neurons within
the prelimbic mPFC during a memory task. The proportion of each type of selectivity was then
calculated and compared across the learning stages with the chi-square test.
2.6.4 Mutual information
Mutual information (MI) gives us a measure of how much one variable tells us about another
variable (Wallisch, 2014). For example, whether the firing rate of a neuron tells us about a
particular stimulus. In this manner it allows us to define the information that the firing rates of a
neuron at different time-windows contains about the stimulus or condition. For each neuron, I
evaluated its degree of selectivity along a continuum for the Relationship, Identity, and
Conjunction of the two. MI represents the entropy of the stimulus, minus the entropy of the
stimulus given the response (Shannon & Weaver, 1949). Here the stimulus represents the
particular condition, and the response represents the neurons firing rate. For my purposes, I
calculated the MI represented in each neurons firing rate for the three features of selectivity by
calculating the joint and marginal probability distributions of each stimulus (trial type) and
response (firing rate) for each neuron with Alone vs. Paired trials (Relationship), Tone vs. Light
trials (Identity), and Tone-Alone vs. Tone-Paired vs. Light-Alone vs. Light-Paired trials
(Conjunction).
47
In the preceding equation, for Relationship, I represents the mutual information of the binned
firing rate (R, 10 bins) for two trial types, Paired and Alone (S). P(s,r) is the joint probability of a
particular trial type and a particular firing rate. Whereas P(s) and P(r) are the marginal
probabilities of each trial type (Paired or Alone) and each response (firing rate), respectively.
In this example, a higher MI value for a neuron indicates a higher degree of selectivity for the
Relationship, relational stimulus features. MI values were calculated during 200 msec time
windows with a 50% overlap for a 2 second period centered on the CS for each neuron
separately for Relationship, Identity, and Conjunction estimates. To prevent biasing the estimates
(Panzeri, Senatore, Montemurro, & Petersen, 2007; Wallisch, 2014), MI at chance level was
estimated by randomly shuffling the trial identities. This process was repeated 100 times, and the
mean of this null distribution from shuffled data was subtracted from the actual MI value
(Hatsopoulos, Ojakangas, Paninski, & Donoghue, 1998; Panzeri et al., 2007). Neurons with a
corrected MI value above 0.4 were considered ‘selective’.
Binning can have a big impact on the estimates produced, for spike counts using larger bins loses
information (for example 20 spikes may end up in the same bin as 10 spikes) whereas using very
small bins introduces a larger bias (Wallisch, 2014). For my data binning firing rates into 10 bins
was observed to produce enough separation to preserve information while avoiding significant
bias.
2.6.5 Population firing rate matrix correlation
To examine the similarity between firings rates of population of neurons across the different
conditions, I constructed a population firing rate matrix which contains the binned firing rate of
all recorded neurons during a 2 second period centered on the CS from Tone-Paired trials. Firing
rates for each neuron were normalized to mean baseline firing rates during a 500 msec window
48
before CS onset. I then sorted these neurons based on their change in firing rate during the trace
interval (TI) relative to baseline with those neurons with a maximal increase at one end and those
with a maximal decrease at the other.
𝑇𝑃 = [
𝑥11 ⋯ 𝑥1𝑚
⋮ ⋱ ⋮𝑥𝑛1 ⋯ 𝑥𝑛𝑚
] 𝑇𝐴 = [
𝑦11 ⋯ 𝑦1𝑚
⋮ ⋱ ⋮𝑦𝑛1 ⋯ 𝑦𝑛𝑚
] 𝐿𝑃 = [
𝑧11 ⋯ 𝑧1𝑚
⋮ ⋱ ⋮𝑧𝑛1 ⋯ 𝑧𝑛𝑚
]
The matrix TP stores the firing rate activity during the Tone-Paired condition in Cells (n) x Time
(m) bins. Each row contains the firing rate of a single neuron in 50-msec bins for a period of 2-
seconds centered on the CS-onset, averaged across trials and the sorted from a maximal increase
(n=1) to a maximal decrease (n=n) in firing rate during the TI relative to baseline. Matrices TA
and LP store the firing rate activity in the same cell order (1:n) from TP for Tone-Alone and
Light-Paired conditions, respectively. The Pearson R correlation statistic between the template
matrix TP, and matrices TA and LP provide an indication of whether prelimbic neurons show
more similar patterns of activity to the same stimulus that differed in its relational features
(Tone-Alone vs. Tone-Paired) or to a different stimulus that shared the same relational features
(Tone-Paired vs. Light-Alone).
2.6.6 Support vector machine learning
Subsequently I sought to examine whether a supervised machine learning algorithm could
decode memory features from prelimbic neuronal population activity. Support Vector Machine
(SVM) is a supervised learning model that looks for patterns in data to find the best way to
separate data sets. SVM produces a model from training data which then predicts the target
values of test data given only the test data attributes (Hsu, Chen & Lin, 2010). For the current
study, the attributes were the normalized firing rates of a population of neurons and the target
49
values were the conditions from which they were sampled. SVM is advantageous because in
addition to completing linear classification, using the kernel trick, SVM can perform non-linear
classification by mapping inputs into a higher dimensional feature space.
For a 2-second period centered on the onset of the CS, firing rates during 200 msec windows
were sampled with a 50% overlap and were linearly scaled by dividing each spike count by the
maximum in each window: 𝑆𝑖 =𝑆𝑖
𝑆𝑚𝑎𝑥 where Si equals the spike count for the ith trial and Smax
equals the maximum spike count from a trial during that window. This procedure produced
values ranging from 0-1. All algorithms were run in Matlab using the freely accessible LIBSVM
library (Chang & Lin, 2011). I used the radial-basis-function (RBF) kernel to classify the four
conditions with a multi-class SVM. To prevent over-fitting, the parameters C and γ were selected
beforehand by performing a grid-search using cross-validation on exponentially growing
sequences of C and γ. The pair with the best cross-validation accuracy was then selected (Chang
& Lin, 2011). Using these parameters, the SVM was trained with the concatenated firing rates of
a population of neurons from 10 randomly chosen trials in each condition. A separate set of data
from 10 different trials was then used to test the classification accuracy. Each SVM was repeated
200 times with different samplings of 10 training trials from each condition and 10 separate test
trials. The significance of the classification performance was assessed by running the same SVM
procedure with the identities of the trials randomly shuffled and repeated 200 times. Chance
level of prediction given 4 trial types was ¼ = .25.
To look at specific classification errors made, confusion matrices were created indicating the
percentage of classifications in which a response vector belonging to condition x was classified
as condition y from a 200 msec time window during the TI between CS-offset and US onset.
These percentages were computed from the classifications of the test trials across the 200
50
resampling’s. The accuracy of classification for each condition was summed to produce accuracy
estimates for the stimulus Relationship (Alone vs. Paired), Identity (Light vs. Tone), and the
conjunction of the two. The accuracy percentage of each type of selectivity was then computed
from these values along with 99% confidence intervals.
2.6.7 Phase-locked firings
Analyses of local field potential (LFP) activity was done offline using custom written codes in
Matlab (Mathworks,Natick,MA,USA). Movement artefact was observed as clipping in the LFP
signal. Prior to any analyses, trials containing clipping during a period spanning from 1 second prior
to the CS to the onset of the US were removed.
To quantify the degree of phase-locking of firings of each neuron, phase data from theta frequency
filtered LFP activity from each trial centered on the CS-presentation was extracted using the Hilbert
Transform. I then looked at the consistency of individual spike-LFP phases from these theta filtered
signals (Vinck, Battaglia, Womelsdorf, & Pennartz, 2012). For each neuron, a phase-value from the
ongoing theta oscillation was assigned every-time the neuron fired an action potential, I then used
this to determine if the neuron displayed a preference to fire an action potential at a particular theta
phase. Significance was determined by the Rayleigh test for circular uniformity. The Rayleigh test is
equivalent to a likelihood ratio test, and the test statistic 2𝑅2
𝑛⁄ , where R= resultant length, n =
sample size, follows an asymptotic chi-square distribution with 2 degrees of freedom (Siapas,
Lubenov, & Wilson, 2005b; Sirota et al., 2008) and tests of significance using the variance-stabilized
log(Z), 𝑍 = 𝑅2
𝑛⁄ (Siapas et al., 2005b; Sirota et al., 2008). The proportion of neurons whose
distribution of collected phase values was significantly different from the uniform distribution (p <
.05) were then totaled for each region, and the log(Z) of these neurons indicated the degree of phase-
locking.
51
2.7 Statistical Analyses
Statistical analyses were performed using SPSS and Matlab, where appropriate. In Chapter 3,
ANOVA was used to compare changes in behavioural responding across time and between
sessions. Follow-up tests used the students t test with Bonferonni correction. Paired-samples t
tests were used to compare firing rate changes to baseline and between conditions with
Bonferonni corrections for multiple comparisons.
To compare correlations between the population matrices, Steiger’s Z-test for correlated
correlations within a population. Here 𝑍 = [𝑍12 − 𝑍13] ∗ √[𝑁−3]
√2∗[1−𝑟23]∗ℎ.
Where Z12 and Z13 are the Fischer’s Z transformations of r12 and r13, respectively, and
ℎ =1−[𝑓∗𝑟𝑚2]
1−𝑟𝑚2 where 𝑓 =
1−𝑟23
2∗[1−𝑟𝑚2] and 𝑟𝑚2 =
𝑟122 +
𝑟132
2.
In Chapters 3 and 4, proportions were compared using the chi-square analysis. Follow-up tests of
significant chi-square tables compared individual chi-square cells using the z-test (Marascuilo &
Serlin, 1988; Sharpe, 2015). Here, 𝑧 = (𝛹 − 0)/𝑆𝐸𝛹. This test compares individual cells from
the chi-square table, where 𝛹 = 𝜌1− 𝜌2 represents the comparison of interest with ρ1 being the
proportion in the first contrast cell relative to its column marginal and ρ2 being the proportion in
the second contrast cell relative to its column marginal. 𝑆𝐸𝛹 = √(1)2𝑆𝐸2𝜌1 + (−1)2𝑆𝐸2
𝜌2
represents the standard error of that contrast. The obtained z value was then compared against the
square root of the chi-square critical value for the entire contingency table to determine
significance (Marascuilo & Serlin, 1988). Confidence intervals were calculated using the
Clopper-Pearson method with the binofit Matlab function.
52
2.8 Histology
Upon completion of all recordings, the location of electrodes were marked by electrolytic lesions.
Rats were first injected intraperitoneally with an overdose of sodium pentobarbital. For tetrodes, 5
μA was passed through one wire of each tetrode for 20 seconds (positive to electrode, negative to
animal ground), for LFP electrodes 20 μA was passed for 45 seconds (positive to electrode, negative
to animal ground). Rats were then perfused intracardially with 0.9% saline followed by 10% buffered
formalin. The brain was removed from the skull and stored in 10% formalin for several days. To
prepare the brain for cryogenic sectioning, the tissue was infiltrated with 30% sucrose solution. The
brain was then frozen and sectioned in a cryostat (Leica) at 50 μm. Sectioned tissue was stained with
cresyl violet and imaged under a light microscope to locate electrode locations. Only recordings from
tetrodes located in the prelimbic mPFC were used for single unit analysis. For LFP analysis only
activity from electrodes located in the target regions were used.
53
Chapter 3
Prelimbic Representations of Associative Memory Throughout Learning and Consolidation
3.1 Introduction
The medial prefrontal cortex (mPFC) has emerged as an important node among the network
of brain regions responsible for the formation, consolidation, and retrieval of memories of events or
episodes (Frankland & Bontempi, 2005; Insel & Takehara-Nishiuchi, 2013; Szu-Han Wang &
Morris, 2010). Whereas its exact role in these different states of a memories existence is uncertain,
there is strong evidence to suggest that for memory retrieval it plays a much more significant role
after the consolidation phase than before (Frankland et al., 2004; Lopez et al., 2012; Maviel et al.,
2004; Quinn et al., 2008; Takehara et al., 2003; Teixeira et al., 2006). This functional niche of the
mPFC has been demonstrated through various experimental approaches (see Chapter 1.2 for
discussion). For associative memory in particular, both functional and electrophysiological
experiments converge on the finding that the prelimbic (PrL) mPFC seems to be intimately involved
in the consolidation of a memory and its long-term retrieval (Hattori et al., 2014; Runyan et al., 2004;
Stern, Gazarini, Vanvossen, Hames, & Bertoglio, 2014; Takehara-Nishiuchi & McNaughton, 2008;
Takehara-Nishiuchi et al., 2006). Electrophysiological investigations offer an excellent complement
to functional experiments and provide the means to assess actual neuronal computations taking place
in real-time. In associative memory, electrophysiological recordings of PrL neurons have revealed
that these neurons display activity selective for a memory association, both in their individual and
population activity patterns. In trace eyeblink conditioning this selectivity was observed as persistent
changes in firing during the stimulus-free trace interval which increased in strength with learning and
consolidation of the association (Hattori et al., 2014; Takehara-Nishiuchi & McNaughton, 2008).
However these studies leave open questions about the encoding of memory in the PrL, for example,
54
which features of a memory are maintained in this network, and how does the content of mPFC
memory representations change as it engages in the long-term consolidation of the memory?
The mPFC has been linked to schema formation and targeted as a potential key brain region in the
building, maintenance and updating of memory frameworks (Preston & Eichenbaum, 2013; Richards
et al., 2014; Wang et al., 2012). How these frameworks are maintained, and the detail to which
specific events are represented within the mPFC is not clear. Here, I attempt to uncover how features
of a memory event are represented within the PrL. Specifically, I looked at the selectivity of
prelimbic neurons to the distinct features of a memory, and how this selectivity and encoding
changes throughout learning, consolidation and retrieval of the memory. I simultaneously recorded
the action potentials of many individual neurons in the rat prelimbic mPFC while rats received daily
conditioning in trace eyeblink conditioning. The paradigm employed was designed to manipulate the
identity and relationship of the conditioned stimulus to permit the examination of which features of
an episodic memory prelimbic neurons were responsive to. Rats were conditioned in this paradigm
daily over the course of 4+ weeks to allow for the direct comparison of prelimbic activity before
learning, during learning, and during post-consolidation retrieval.
Here I reveal the presence of neurons in the prelimbic cortex exhibiting varying degrees of selectivity
for either the relational features of the conditioned stimulus (CS), the identity of the CS, or a
conjunction of the two. At the single neuron level there is a greater proportion of responsive cells that
do not show selectivity and those selective for the relationship of the CS than cells that display more
highly selective activity. Also at the single neuron level the proportions of these neurons across time
does not significantly change. However, I show here that in looking at activity at the population
level, we see a shift across time from higher selectivity in the early phases or learning to more
generalized selectivity in the post-consolidation retrieval stages of the paradigm.
55
3.2 Results
Experiments were conducted on 4-6 month old Long-Evans rats (n=5) with functioning tetrodes
located in the prelimbic medial prefrontal cortex. Animals were surgically implanted with a chronic
Micro-drive array (see Chapter 2.3 for detailed methods on surgical procedure and the tetrode
method). Animals underwent daily conditioning in the trace eyeblink paradigm for 30+ days (see
Chapter 2.5.1for a clear overview of the procedure). Briefly, the paradigm employed the use of two
different conditioned stimuli (Tone & Light) in two different relational conditions (Alone & Paired).
Each day, each stimuli was presented in one of two 100 trial sessions, the first 20 trials of which the
stimulus was presented alone, and the last 80 trials the stimulus was paired with eyelid shock (US).
This resulted in 4 conditions that differed in their combination of the identity and relationship of the
stimulus (Tone-Alone; Tone-Paired; Light-Alone; Light-Paired). Electrophysiological signals were
recorded from tetrodes located in the prelimbic region throughout the procedure and were sorted
offline to isolate stable single units for analyses.
3.2.1 Acquisition and expression of the conditioned response
Rats underwent trace eyeblink conditioning wherein two different stimuli (a tone and a light)
predicted the same outcome (eyelid shock). This was conducted daily in two conditioning
sessions, the first with one CS (e.g. tone) the second with the other CS (e.g. light), with session
order determined pseudo-randomly (see Figure 5). Each conditioning session consisted of 100
trials, the first 20 of which the CS was presented alone (CS-alone) and in the following 80 trials
the CS was paired with the US (CS-paired). Animals gradually acquired the adaptive conditioned
eyeblink response (CR) over the course of training during the Tone-Paired and Light-Paired
conditions, and learned to not respond during the Tone-Alone and Light-Alone conditions
(Figure 5). Repeated measures ANOVA revealed a significant Condition Day interaction
(F(93,393) = 4.702, p < .001). Follow up tests reveal that for Tone sessions CR% significantly
56
differs between Alone and Paired trials (F(1,131) = 1227.241, p < .001) and a significant
Condition x Day interaction was detected (F(31,131) = 8.282 p < .001). For the Paired condition
there was a significant effect of Day (F(31,162) = 8.418, p < .001), that was not observed in the
Alone conditioned (F(31,162) = .931, p = .576). Similarly, for the Light sessions, CR%
significantly differs between Alone and Paired trials (F(1,131) = 1605.870, p < .001, and there
was a significant Condition Day interaction (F(31,131) = 7.61, p < .001). For the Paired
condition there was a significant effect of Day (F(31,162) = 5.32, p < .001), that was not observed
for the Alone condition (F(31,162) = .901, p = .620). There was no difference between Alone
conditions from the Tone and Light sessions. However, for Paired conditions CR% in Light
sessions was slightly higher than Tone conditions (F(1,131) = 7.968, p = .006, M = 55.25 vs.
51.10), but there was no Condition Day interaction. This difference between Tone and Light
Paired conditions was only evident during early acquisition and disappeared after Day 12 of
conditioning (F(1,67) = 1.014, p = .317, M = 65.93 vs. 64.32).
57
Figure 5. Acquisition and Expression of the Conditioned Eyeblink Response.
A) Illustration of the experimental paradigm. B) Averaged acquisition and expression of the conditioned eyeblink response (CR) across animals (n = 5), across days, in each of the four conditions. Rats gradually acquired the conditioned response in both the Tone-Paired (TP, red) and Light-Paired (LP, blue) conditions while not responding during the Tone-Alone (TA, magenta) and Light-Alone (LA, turquois) conditions.
3.2.2 Single unit activity patterns
My first aim was to determine the selectivity of prelimbic neurons for the various features of the
memory. For each individual neuron, the averaged firing rate across trials during the CS-period
and TI-period was compared to the averaged firing rate during a 500-msec period immediately
before CS-onset during Tone-Paired and Light-Paired conditions. A significant change from
baseline activity in either the Tone or Light Paired conditions determined if a neuron was
responsive to the CS or not. The total percentage of responsive neurons from all sampled neurons
58
(n = 1288) reveals that prelimbic neurons are more responsive during the TI period (70.89%)
than during the CS period (31.06%) (X2(1, N = 2572) = 413.043, p < .001). (Figure 6).
Figure 6. Histology and Single Unit Isolation.
A) Representative image of a tetrode tip located within the prelimbic medial prefrontal cortex (top). Final locations of all usable tetrodes targeted at the prelimbic cortex (black dots, bottom). Images and coordinates adapted from Paxinos and Watson (2007, 6th edition). B) Example of cluster sorting in MClust (left) and illustrative waveforms of isolated single units (right) from a single tetrode from one recording session.
Of these responsive neurons, firing rates were normalized to baseline activity in each condition
and within each neuron, activity was compared across the four conditions. Student’s paired
sample t-tests with Bonferroni correction revealed 4 different types of selectivity 1) Non-Selective
(CS-alone = CS-paired & Light = Tone); 2) Relationship-Selective (CS-alone ≠ CS-paired & Light =
Tone); Identity-Selective (CS-alone = CS-paired & Light ≠ Tone); 4) Conjunctive (CS-alone ≠ CS-
paired & Light ≠ Tone) (Figure 7). The total percentage of each type of selectivity reveals the type of
information being represented by prelimbic neuron activity. During the TI period the largest
proportion observed was Non-Selective (37.35%). Of the other types of selectivity I observed a
59
substantial representation of each, Identity (21.25%), Relationship (23.22%), and the Conjunction of
the two (18.18%) (Figure 7). These findings suggest that within the prelimbic region there is a
diversity of neuronal responses to the various features of a memory episode, encompassing very
unselective stimulus activity to highly selective activity to a particular stimulus in a particular
condition.
60
Figure 7. Single Neuron Selectivity for Task Features.
A) Representative raster plots and peri-stimulus time histograms (PSTHs) from prelimbic units smoothed with a 50-msec Hanning window for each type of observed selectivity of responsive neurons. Selectivity was defined by firing rate differences between the four conditions: Tone-Alone (TA, magenta), Light-Alone (LA, turqouis), Tone-Paired (TP, red), Light-Paired (LP, blue). This revealed 4 types of selectivity: Non-selective: CS-Alone = CS-Paired & Tone = Light; Relationship: CS-Alone ≠ CS-Paired & Tone = Light; Identity: CS-Alone = CS-Paired & Light ≠ Tone; Conjunctive RxI: CS-Alone ≠ CS-Paired & Light ≠ Tone. Examples of each type of selectivity are shown with raster plots, top, showing spiking activity (dots) during a 1-second period centered on the onset of the conditioned stimulus (CS, 0) in the Tone (left) and Light (session) and PSTH (bottom) showing the smoothed averaged firing rate for each condition. B) Proportion of responsive (left) and each type of selective (right) neurons observed by looking at the 100-msec period when the CS is presented (top) and the 500-msec trace interval (TI) between CS and US (bottom) of all neurons recorded. More responsive neurons were observed during the ISI (70.89%) than during the CS (31.06%). Of responsive neurons during the ISI period, similar proportions of each type of selectivity were observed.
61
3.2.3 Changes in selectivity across time
The necessity of the prelimbic region for memory retrieval changes over time (Maviel et al.,
2004; Quinn et al., 2008; Takehara-Nishiuchi et al., 2005; Takehara-Nishiuchi et al., 2006). This
suggests that changes in connectivity and possibly changes in encoding among neurons within
the prelimbic region may occur over the course of this time period. Once I had established the
range of selectivity of prelimbic neurons, I sought to examine whether these representations at
the single neuron level change across time. The first way to examine this was to look at the
proportions of each response type at different time-points throughout the conditioning procedure
to see if any differences emerge. As described in Chapter 2.6.1 neuronal data collected for each
rat was divided into individual behaviorally defined time-periods of conditioning: Before
learning period, prior to acquisition of the CS-US association as evidenced by a very low
percentage of trials exhibiting the adaptive conditioned response (CR); During learning, as the
animal begins to acquire the association gradually with fluctuating performance across days; Post
leaning period, once responding reaches an asymptotic stable level all time-points beyond this
period are defined in weeks Post learning (see Figure 8).
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Figure 8. Behaviourally Defined Learning Stages.
Example of acquisition and expression of the conditioned response in one rat displaying how learning stages were individually defined based on behavioural data. Conditioned response rates during Tone-Paired (TP, red) and Light-Paired (LP, blue) indicate periods prior to acquisition of CR in 30% of trials (Before Learning, light grey), period between this point and asymptotic responding (During Learning, medium grey) and all days following (Post Learning, dark grey). An epoch 3 weeks into the Post Learning period (3w-Post) is highlighted as this was a major comparison in subsequent analyses.
I focused my comparisons on neurons sampled from three time-points, the Before learning
period (n = 149), the During learning period (n = 487), and the 3 week post-learning period (3w-
Post, n = 188) consisting of conditioning days taking place 3 weeks after asymptote had been
reached. The proportion of neurons exhibiting each type of response selectivity was calculated
across animals (n = 5) for these different time periods (Figure 9). A chi-square analysis revealed
no differences between the three time-points in the proportion of each type (X2(8, n = 822) =
11.753, p = .163).
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Figure 9. Selectivity Across Learning Stages.
Neurons categorized by their response patterns during the task were separate and their relative proportions compared across the behaviourally defined learning stages before learning (Before, light grey), during learning (During, medium grey) and three weeks after asymptotic responding (3w-Post, black) as described in Section 2.6.1. No differences in the proportions were revealed between time periods.
While the mean comparison approach is revealing, it has its drawbacks in that we are imposing a
threshold to classify selectivity to a particular feature. Alternatively, mutual information (MI)
allows us to quantify selectivity along a continuum by calculating the information contained
between the stimulus features and the responses of neurons. The MI between variables is
expressed in bits with 0 being no information shared, or completely independent variables. I
calculated the MI separately for the stimulus relational features (Relationship: Alone vs. Paired),
stimulus identity features (Identity: Tone vs. Light), and the conjunctional stimulus features
(Conjunction: Relationship vs. Identity) to provide the degree of selectivity of each neurons
response rate. Again this was done at the Before learning, During learning, and 3w-Post learning
time-points. Figure 10 illustrates a scatterplot of this data in which it can again be observed that
more information about the stimulus features is evident during the trace interval (TI) than during
64
the CS presentation. MI values were shuffle-corrected using the mean of MI values from 100
repetitions of shuffled data. All neurons with a shuffle-corrected MI value above 0.4 were
counted as selective. Chi-square analysis on the number of selective neurons during the CS and
TI confirmed more selective encoding of the stimulus during the TI (X2(3, n = 2398) = 563.743,
p < .001). I then looked at the proportion of selective neurons based on MI estimates for
selectivity to the Relationship, Identity, and Conjunction of the two during the TI across the three
learning periods. Results support the previous analysis, revealing no differences in the proportion
of neurons selective for the stimulus features across the different learning stages (X2(6, n = 2398)
= 10.306, p = .112) (Figure 10).
Using both mean comparison and mutual information I have shown that PrL neurons firing rates
exhibit more selective activity during the TI than the CS, similar to previous reports (Takehara-
Nishiuchi & McNaughton, 2008; A. P. Weible, Weiss, & Disterhoft, 2003). Both estimates of
selectivity also reveal no differences at the single neuron level in the proportion of selective PrL
neurons from periods prior to learning the association, during its gradual acquisition, and three
weeks after stable memory expression.
65
Figure 10. Mutual Information defined Selectivity.
A) scatterplots showing neurons (blue-dots) mutual information for the Identity (y-axis) and the Relationship (x-axis) of the stimulus during CS-presentation (CS) and during the inter-stimulus interval (Trace Interval) across three stages of learning (Before, During, 3w-Post). Again more selective activity is observed during the trace interval compared to the CS-period. B) Proportion of neurons whose corrected mutual information during the trace interval for the Identity, Relationship and their Conjunction (RxI) was greater than 0.4 across three stages of learning. No differences in the proportions across the learning stages was observed.
3.2.4 Population activity patterns
Selectivity at the single neuron level informs us of the type of information that is encoded
within a brain region. Here we see a diversity of single neuron response profiles within the prelimbic
mPFC (PrL), from responsive regardless of the identity and relational features of a stimulus to highly
selective to a specific stimulus in a specific relational condition. However neurons do not function
individually in the brain, they function as part of a network. Therefore it can be informative to look at
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activity at the population level. A previous study recording from many single neurons from the same
brain region in trace eyeblink conditioning also revealed no changes in the proportion of memory
selective neurons across time, but at the population level an increase in memory selective activity
was observed over a time-frame consistent with the consolidation period (Takehara-Nishiuchi &
McNaughton, 2008). Here I looked at selectivity at the population level of activity and whether
differences could be observed across time in the representation of different features of the memory
association. As a first course of investigation I constructed firing rate population matrices containing
the firing rates of all neurons during a 2-second period centered on the CS-onset. Normalized firing
rates averaged across trials from the Tone-Paired (TP) condition were sorted based on change in
firing rate during the trace interval (TI) relative to baseline in order from a maximal increase to a
maximal decrease (Figure 11). Taking this same order of neurons, matrices were created containing
the firing rates during the Tone-Alone (TA) condition and Light-Paired (LP) condition.
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Figure 11. Prelimbic Neuron Population Matrices.
Populations of neurons recorded from the prelimbic region before acquisition (Before, left), during learning (During, middle), and 3 weeks after asymptote (3w-Post, during) in the Tone-Paired condition (top) were sorted based on their change in firing rate after the CS relative to baseline from the largest decrease (cell #1) to the largest increase. This same order of neurons was then plotted during the Tone-Alone condition (middle) and Light-Paired condition (bottom). Here you can see the similarity between the conditions with the same stimulus, different relationship (Tone-Paired vs. Tone-Alone) and a different stimulus, same relationship (Tone-Paired vs. Light-Paired) change across the learning stages.
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The correlation between these matrices provides an indication of the similarity of activity across
neurons for the same stimulus in a different relational condition, or for a different stimulus with the
same relational features. Figure 11 displays this data for periods Before, During and 3w-Post learning
where it can be seen that the similarity of the population activity between TP and TA becomes less
similar as the animals progress through the learning stages to the 3w-Post time period. However the
similarity between TP and LP remains high throughout with a slight increase at the 3w-Post period.
To assess the evolution of these changes I then calculated the correlation between the TP and the
TA/LP matrices across all time periods including the 1 week and 2 week Post learning periods. The
correlation between TP and TA/LP was significant at all times-points (Table 1). I subtracted the
correlations with TA from LP to provide an indication of the changes in the difference between the
two across time. This revealed a pattern where correlations in population activity were different
during the first few days of conditioning but became more similar as learning progressed (During &
1w-Post). However by 2 and 3 weeks after learning (2w/3w-Post) this correlated activity began to
again separate and then became maximally different (Figure 12). Using Steiger’s Z-test of correlated
correlations within a population (Steiger, 1980), I directly compared correlations between TP and
TA/LA at each time-period. With a Z-critical set at 2.58 for p < .01 to correct for multiple
comparisons, the correlation between TP-LP was significantly greater than TP-TA for all time-points
except 1w-Post learning, corroborating the observed differences (Figure 12).
Table 1. Correlations between population firing rates across conditions
Before During 1w-Post 2w-Post 3w-Post
Tone-Paired
vs.
r Z r Z r Z r Z r Z
Tone-Alone 0.654* 3.843*
0.727* 3.435* 0.754* 1.4 0.642* 4.388* 0.616* 4.94*
Light-Paired 0.815* 0.802* 0.798* 0.808* 0.828*
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Neurons (n) 156 461 193 186 165
Note: Pearson correlation (r) and Steiger’s Z. * p < .05
Figure 12. Matrix Correlation Differences Between Relational and Identity Features.
The difference in correlation between the Tone-Paired/Light-Paired and Tone-Paired/Tone-Alone population matrices. Difference in Pearson r (bars) and Stieger’s Z value of correlated correlations reveal that conditions sharing the same relational features are more similar than conditions sharing the same stimuli early in learning (Before) and after the consolidation phase 2 weeks and 3 weeks after asymptote (2wP & 3wP). During acquisition (During) and in the initial week after asymptote (1wP), the population activity became more similar. Dotted line represents Stieger’s Z critical value at p < .01. * p < .01.
3.2.5 Decoding Prelimbic population activity
I show that activity appears to become less similar at the population level for the same stimulus when
its relational features differ over the same time-course that the consolidation of this memory is
presumed to take place (Takehara-Nishiuchi & McNaughton, 2008). I next investigated whether a
supervised learning algorithm could be trained with population firing rate data to accurately predict
trial conditions given only population activity. I used a machine learning classifier to decode
prelimbic neuron population activity, the successes and failures of which would indicate the amount
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of information represented for the various memory features and how their representation may change
with learning and consolidation. Firing rates from a population of neurons were fed into the Support
Vector Machine (SVM) classifier using 10 randomly selected trials to trial the classifier, and 10
independent trials to test the trained model from each condition in a multi-class classification across
the 4 conditions. Accurate prediction was also observed using 19 training trials and 1 test trial, and
using 15 training and 5 test trials, however the 10-10 method produced more consistent and less
variable results.
Figure 13. Support Vector Machine Classification Accuracy.
A) Condition classification accuracy from a population of neurons recorded during the 3 week period after asymptotic responding (Real, red line) and accuracy when trial identities were shuffled during model training (black line, Shuffled) with the support vector machine decoder. Accuracy was calculated in 200 msec windows with a 50% overlap for a 2-second period centered on the onset of the
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conditioned stimulus (CS). Accuracy can be seen to increase after the CS-onset and peak during the inter-stimulus interval. B) Confusion matrix showing the probability of accurate classification for each condition with the predicted trial condition along the x-axis and the actual trial condition along the y-axis. All entities along the diagonal indicate perfect classification whereas entities off the diagonal indicate errors. Here we see the presence of some errors confusing Tone-Alone and Light-Alone trials as well as Tone-Paired and Light-Paired trials indicating less accurate decoding of the stimulus Identity and more accurate decoding of the stimulus Relationship from a population of neurons at this time-point.
Figure 13B shows the prediction accuracy from a population of neurons (n = 156) recorded during
the 3w-Post learning period (Real) compared to the accuracy when the identities of the trials were
shuffled (Shuffled). In this example the trained model was observed to be overall very accurate
(maximum = 97.5% classification accuracy from 200 repetitions). When trial ID’s were shuffled
performance in accuracy was at the chance level for four conditions 1/4 = .25 (shuffled average =
25.52 % correct). It also reveals that classification performance increases during the CS and then
peaks and remains high throughout the TI (average 80.63% correct during 500 ms TI vs. average
48.12% during 500 ms Pre-CS period). The accuracy of all time-points from CS-offset to US-onset
were significantly higher than the chance level of prediction as revealed by paired sample t-tests (all
p’s < .0001). This shows that features of the memory, including the stimulus relational features and
the stimulus identity are encoded by prelimbic neuron population firing rates during the stimulus free
trace interval between the CS and US.
Next I looked at trial specific errors using a confusion matrix to show the proportion of trials in
which condition x was classified as condition y across the 200 resamplings.
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Figure 14. SVM Classification Accuracy changes for Stimulus Features Across Time.
A) Percentage of accurate classification for the different features of the task, Paired vs. Alone (Relationship), Tone vs. Light (Identity) and the conjunction of the two (RxI) across the different learning stages. Accuracy in classification of the stimulus relationship gradually increased from before acquisition (Before) to the acquisition period (During) divided in first and second halves (Early and Late, respectively), and peaking 3 weeks after asymptote (3w-Post). The opposite was observed for classification of the identity of the stimulus, which was high Before and dropped significantly to 3w-Post. Classification of the conjunction of Relationship and Identity (RxI) was low initially, peaked during Early learning and then dropped by the 3w-Post period. * p < .01 using the z test to compare chi-square cells. Error bars indicate 99% Clopper-Pearson confidence intervals. n = 156 for all time-periods.
Figure 13 shows the confusion matrix from the concatenated firing rates of a population of neurons
from the prelimbic mPFC recorded 3 weeks after learning. Here I show accurate classification in
each condition (entities along the diagonal) and the minor presence of errors, which appear specific
to confusion of the stimulus identity, where tone trials are mistaken for light trials and vice-versa. To
quantify this, the proportion of correct classifications was calculated between conditions differing in
relational features (Relationship: Tone-Alone vs. Light-Alone + Tone-Paired vs. Light-Paired),
conditions differing in stimulus features (Identity: Tone-Alone vs. Tone-Paired + Light-Alone vs.
Light-Paired) and the conjunction of relationship and identity (RI: Tone-Alone vs. Tone-Paired vs.
Light-Alone vs. Light-Paired). This quantification reveals the information represented about the
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stimulus identity, stimulus relationship, and their conjunctive representation within population firing
rates during the TI. I then ran the SVM analysis on separate groups of neurons recorded across the
learning stages to look at differences in these representations across time. Because SVM can be
highly dependent on the number of inputs, the number of neurons in a population across the different
stages was fixed to the lowest number sampled (n = 156, Before Learning). From all other periods
neurons were randomly drawn from the total recorded to match this set. For the During learning
period, in which many more days are included and therefore a much larger population of cells (n =
471), I divided this period in half and defined an Early (n = 235) and Late (n = 236) phase containing
neurons recorded during days in the first and second half of the During learning period, respectively.
This was done to reduce the variability of classification based on random sampling. This analysis
reveals a change in the representation of different types of information at the population level of
neuronal activity across time (x2(10) = 73.93, p < .001). From the chi-square, specific components
were compared using the z test (Marascuilo & Serlin, 1988; Sharpe, 2015) with p < .005 to correct
for multiple comparisons.
In Figure 14 I show in the proportion of correctly classified cases that decoding relational stimulus
features (Relationship) increases over time, from Before to Early learning (z = 6.15, p < .001) and
from Early learning to 3w-Post learning (z = 14.27, p < .001). Conversely, decoding of the stimulus
itself (Identity) becomes less accurate over time, from Before to Late learning (z = -10.11, p < .001)
and from Late to 3w-Post learning (z = -8.84, p < .001). These results suggest that at the population
level, information about the stimulus relationship gradually increases with learning and remains high
after memory consolidation, whereas specific information about the stimulus identity (Tone or Light)
while initially high, gradually decreases with learning and consolidation. The representation of the
conjunction between the stimulus and relationship shows an initial increase from Before learning to
During learning (Early z = 5.03, p < .005) but returns to a similar level as Before learning at all other
time-points.
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With SVM I show that representations at the population level change throughout the course of
learning and the memory’s presumed consolidation. Prelimbic neuron populations contain more
information about the CS-US relationship and less information about the particular CS as learning
progresses and the memory becomes consolidated. This supports the results from the population
firing correlations wherein I showed that activity between the two conditions sharing the same
relational features are more similar than the activity between two conditions sharing the same
stimulus, and that this difference becomes progressively larger as the memory consolidates. Here,
using the SVM classifier, the amount of information being represented was interpreted based on the
performance of the classifier to accurately predict trials, given only the firing rates of a population of
neurons in those trials. In the days prior to acquisition of the memory association, the classifier was
more accurate in decoding the identity of the stimulus (Tone vs. Light conditions) than the
relationship of the stimulus (Alone vs. Paired conditions). In the later stages of learning and by 3
weeks post learning the pattern flipped and the classifier was more accurate in decoding the
relationship of the stimulus than the identity. These findings may reflect a network property of the
prelimbic cortex with population activity becoming more generalized (i.e. less specific information
about the CS details) as the memory becomes firmly established in the prelimbic memory network.
3.2.6 Contextual encoding of a consolidated associative memory
Following the 30+ day conditioning procedure, several animals underwent a modified version of the
conditioning paradigm using the same stimulus across two sessions (Tone) but modifying the
conditioning context in one session (Context B). Behaviourally, the conditioned response (CR) rates
remained high during the Paired conditions and low during the Alone conditions and did not differ
between contexts or across days (Figure 15). Repeated measures ANOVA revealed a main effect of
condition (F(3,18) = 51.429, p < .001), and no effect of Day or a Day by Condition interaction.
Context had no effect on CR in Alone or Paired trials and CR percentage was significantly higher in
Paired vs. Alone trials (Context A: t(8) = 8.214, p < .001; Context B: t(8) = 8.454, p < .001).
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Figure 15. Context Manipulation Paradigm and Behavioural Results.
A) Design of context manipulation carried out over 3 days after completion of the paradigm previously described. Using the same design as before 20 CS-Alone trials and 80 CS-paired trials, the same stimulus (Tone) was used in both sessions however the environmental context was changed between the two sessions (Context A, original & Context B). B) Conditioned response rates in each of the 4 conditions over the 3 days, Context-A Tone-Alone (Cxt A-TA, magenta), Context-A Tone-Paired (Cxt A-TP, red), Context-B Tone-Alone (Cxt B-TA, turquois) and Context-B Tone-Paired (Cxt B-TP, blue). Conditioned response rates remained high in the Paired conditions versus Alone conditions, however there was no difference in responding between the two contexts.
Mean comparison of firing rate changes relative to baseline again revealed that a majority of neurons
are responsive during the trace interval (TI) (75% Responsive vs. 25% Nonresponsive). Looking at
the selectivity of Responsive neurons I observed the presence of context selective neurons,
suggesting that the context manipulation was effective, but a much smaller proportion than those
selective for the stimulus relationship was observed (Context 13.56%; Relationship 28.81%). I
compared this distribution of selectivity to the distribution of selectivity several days earlier at the
3w-Post time-point in the original conditioning paradigm. By equating Context selective to Identity
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selective response proportions, I observed no difference between the two (X(4) = 3.64, p = .456),
suggesting that the change in context had not substantially changed the representation (Figure 16).
Figure 16. Single Neuron and Population Selectivity for Contextual Features.
A) Proportion of responsive (left) and each type of selective (right) prelimbic neurons recorded during context manipulation sessions. A similar ratio of Responsive to Non Responsive neurons to the previous conditioning paradigm. Of responsive neurons, a greater proportion of neurons was observed to be selective for the Relationship (Tone vs. Paired) of the stimulus than the Context (Context A vs. Context B). B) Populations of neurons recorded during the three context manipulation days in the Context A
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Tone-Paired condition were sorted based on their change in firing rate after the conditioned stimulus (CS) relative to baseline from the largest decrease (cell #1) to the largest increase. This same order of neurons was then plotted during the Context-A (CxtA) Tone-Alone condition (bottom left) and Context-B (CxtB) Tone-Paired condition (bottom right). Here you can see the similarity between the conditions with the same relationship and different context (CxtA Tone-Paired vs. CxtB Tone-Paired) compared to conditions with the same Context and different Relationship (CxtA Tone-Paired vs. CxtA Tone-Alone).
I then looked at this activity at the population level. First, firing rate population matrices, as
described in the previous section, revealed that population activity was more similar (p < .01)
between Tone-Paired in Context A and Tone-Paired in Context B (r = .738) than between Tone-
Paired in Context A and Tone-Alone in Context A (r = .534) (see Figure 16). Suggesting that there is
stronger encoding of the relationship of the stimulus than the context, as observed at the 3w-Post
time-point for the stimulus identity.
I also ran this population of neurons in the SVM classifier to look at classification accuracy across
the 4 conditions with context being a variable. In the confusion matrix in Figure 16 I show accurate
classification from this population of neurons, however it reveals the presence of specific errors.
Errors appear to be made by the model in classifying activity from Context A with Context B and
vice versa. Quantifying the proportion of correct classification for the Relationship, Context, and
their conjunction revealed very accurate decoding of the stimulus relationship similar to what I
observed at the 3w-Post time-point. Decoding of the Context and the conjunction of Context and
Relationship were both significantly less accurate (X2(2) = 43.79, z=-17.78, -22.55, respectively, p’s
<.001). This suggests that at this time-point when the population encoding of this memory appears
more generalized, the encoding of a new feature, Context, may become embedded in this already
generalized memory code. However because I did not run this context manipulation prior to
completion of the original memory paradigm it is possible that the level of contextual encoding
observed is simply intrinsic to the prelimbic network and not contingent upon a consolidated
representation. Evidence that this alternative interpretation may not be the case can be found by
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closer inspection of the confusion matrix data. The errors made in Context classification are not
uniform across all conditions. Quantification of Context classification errors across the four
conditions reveals that errors seem to be specific to the CS-Paired conditions. The model confuses
Context-A: Tone-Paired with Context-B: Tone-Paired trials but makes very few errors between
Context-A: Tone-Alone and Context-B: Tone-Alone trials (see Figure 17). This suggests that the
population firing rates of these prelimbic neurons do convey accurate contextual information as the
model is accurately able to classify trials between the two contexts during the CS-Alone conditions.
It is possible that given the Context is not adding any useful or relevant information for this firmly
embedded CS-US association, contextual information is less accurately encoded in this
representation.
Figure 17. SVM decoding of Contextual Features.
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A) Confusion matrix shows accurate decoding as classification probability values along the diagonal with the predicted trial condition along the x-axis and the actual trial condition along the y-axis. Errors can be observed confusing Context A and B CS-Alone trials and Context A & B CS-Paired trials. B) Quantification of correct classification of the relational features (Relationship, Alone vs. Paired), contextual features (Context, A vs. B) and the conjunction of the two RxC, indicates better classification of the relational than contextual task features. C) Examination of specific contextual classification errors reveals few errors in classification of Context A and B in CS-Alone conditions, and greater percentage of errors in classification of Context A and B in CS-Paired conditions.
3.3 Discussion
3.3.1 Summary
Here I report the activity of multiple single neurons in the rat prelimbic medial prefrontal cortex
recorded across several weeks during a trace eyeblink conditioning procedure in which the stimulus
identity (Tone or Light) and stimulus relationship (Alone or Paired) differed across trials. Comparing
firing rates across these four conditions demonstrated the selectivity of these neurons for the various
features of the memory. A diversity of selectivity was observed, from neurons that are responsive
during the task but do not show selectivity for particular features, responding to both stimuli
regardless of whether it was predictive or not (Paired vs. Alone conditions), to neurons that were
selective to a particular stimulus in a particular condition (e.g. Tone-Paired). The proportions of these
different types of responsive neurons were calculated and were not observed to change at the single
neuron level from a period prior to acquisition of the memory association to a period 3 weeks after
the association was firmly expressed. However, changes were observed when I looked at the activity
at the population level. I showed that the population of prelimbic neurons is initially highly selective
for the particular stimulus features and less selective for the relational features. As the memory
association is gradually acquired this difference becomes less pronounced. The population of neurons
recorded 3 weeks after stable memory expression show the inverse relationship, being less selective
for the particular stimulus features and highly selective for the relational features. Finally, when a
new dimension was added to this stable memory through the manipulation of the context, these
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neurons did not show a rebound in highly selective encoding of this feature and instead displayed
less selective representations of this new information.
3.3.2 Single Neuron Selectivity
The mPFC is a brain region that has been extensively studied and in recent years its role in memory
has been advanced by many groups (Frankland & Bontempi, 2005, 2006; Insel & Takehara-
Nishiuchi, 2013; Szu-Han Wang & Morris, 2010; Weiss & Disterhoft, 2011).Whereas its role in
memory retrieval, especially at time-points remote to its formation, is convincing, its role in memory
formation is still fairly nebulous (Bero et al., 2014; Einarsson & Nader, 2012; Takehara-Nishiuchi et
al., 2006). It is also still very much unclear what type of information this region encodes as a memory
trace, how selective this region is to the specific features of a memory event and how this changes
with learning and memory consolidation. I observed the presence of neurons exhibiting a diversity
of selective responsiveness, the proportion of which did not change from the first few days of
conditioning to 30+ days later. The same result was obtained using both mean comparison and
mutual information. A similar result at the single neuron level has also been described previously,
that the proportion of memory selective neurons in the prelimbic cortex does not change throughout
learning, consolidation, and post-consolidation retrieval (Hattori et al., 2014; Takehara-Nishiuchi &
McNaughton, 2008).
3.3.3 Population level encoding
Although as the single neuron level, proportions of selective neurons do not appear to differ what
does appear to change is the magnitude of their response with learning and consolidation, and thus
the relative activity of a population of memory selective neurons increases (Hattori et al., 2014;
Takehara-Nishiuchi & McNaughton, 2008). I found a similar finding here, whereas no change was
observed in the percentage of selective neurons across time, at the population level differences began
to emerge. The activity of a population of neurons during the Tone-Paired condition was similar in
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both the Tone-Alone and Light-Paired conditions during the learning of the association, but by 3
weeks post learning, Tone-Paired activity was more similar to Light-Paired than Tone-Alone activity.
This suggests that at this 3 week post learning period, the population response is more selective for
the relationship of the stimulus (Alone vs. Paired) than for the particular physical features of the
stimulus (Tone vs. Light). This 3 week post learning time-point overlaps with the time point after
learning where prefrontal lesion and inactivation produces major impairments in memory retrieval
(Frankland et al., 2004; Maviel et al., 2004; Takehara et al., 2003; Takehara-Nishiuchi et al., 2005)
and after the time-period where mPFC plasticity is necessary for later memory retrieval (Takehara-
Nishiuchi et al., 2005, 2006; Teixeira et al., 2006; Gisella Vetere et al., 2011). To further elucidate
the differences in population activity across time I used a machine learning algorithm, support vector
machine (SVM). The algorithm was consistently accurate across all time-points, however differences
emerged in the classification of specific trial features. Correct classification of the identity of the
stimulus was very high early in the conditioning paradigm and gradually decreased throughout
learning and remained lower at 3 weeks post learning. Accurate classification of the relationship of
the stimulus followed the inverse, lower initially and gradually increasing up to the 3 week post
period. Correct classification of the conjunction of the identity and relationship peaked during the
learning phase and then decreased by 3 weeks post learning. These results imply that at the
population level, prelimbic neurons are initially highly selective for discrete stimulus features and as
the association is formed, the population becomes less responsive for these specific features and
becomes more generalized. At the same time, their response for the relevant more general relational
features of the stimulus increases.
3.3.4 Implications of the varying degrees and changes in selectivity observed
It was initially surprising to find the existence of neurons in the prelimbic mPFC that display such
highly selective activity for the conjunctive representation of a particular stimulus in a particular
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condition. The mPFC is hypothesized to be important for schema generation (Preston & Eichenbaum,
2013; Richards et al., 2014; van Kesteren et al., 2012; Wang et al., 2012), encoding rules (Rich &
Shapiro, 2009), and representing abstract features (Wallis et al., 2001). The changes in population
representations reported here generally support this view, showing that over the same time course
that the prelimbic mPFC becomes necessary for the retrieval of this memory, prelimbic population
activity becomes less selective for detailed physical stimulus properties and more selective for their
relational properties. However, while this pattern of changes is apparent, even at the remote time-
point assessed here, single prelimbic neurons still displayed highly selective stimulus responses and
the population could still be accurately decoded for both stimulus and relational properties. It is
unlikely that these results can be explained by the conditioning paradigm, in which training/retrieval
of memory took place daily over this entire period. A previous study directly compared prelimbic
responses in animals that were trained and then tested 4 weeks after learning versus animals that
underwent daily conditioning over the same time-period and revealed no differences in neuron
selectivity between the two cases (Takehara-Nishiuchi & McNaughton, 2008). Therefore the current
findings highlight an interesting property of the prelimbic cortex, a network of neurons containing
both highly selective and highly generalized responses within a brain region.
That the prelimbic regions contains both highly selective and generalizing neurons seems to counter
the hypothesis of the transformation of memory through the process of consolidation, wherein the
memory loses its discrete details, becoming more of an abstraction (Winocur et al., 2010). This
process is proposed to take place gradually, as related information becomes integrated within
knowledge frameworks (McClelland et al., 1995). However the population activity of the prelimbic
region does identify a possible neural correlate of this schematization or generalization of associative
memory that takes place within the window of presumed consolidation. Here the population activity
becomes less selective for the discrete stimulus features, generalizing across irrelevant dimensions of
information. At the same time the population becomes more selective for the relevant relational
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features of the stimuli. This suggests that the population contains the capacity to become more or less
selective for particular features of the memory over the course of consolidation driven by the
meaning and relevance of the features.
I observed a greater proportion of neurons in this network to be either non-selective or selective for
the relational stimulus features, than those that were selective for a particular stimulus or the
conjunction of a stimulus and its association. Similarly in primates, a majority of prefrontal neurons
were shown to be responsive to the rule of the task (Wallis et al., 2001). However, although not
highlighted, the same study does report the existence of prefrontal neurons selective for specific
stimuli, and the conjunction of a specific stimulus and a rule (Wallis et al., 2001), as was observed
here. The function of such highly selective cells is not clear, but I propose that this property may
support the formation, consolidation and maintenance of event memories within this network. It has
been demonstrated that incorporating new information into an existing long-term memory or schema,
occurs rapidly and requires the mPFC (Richards et al., 2014; Tse et al., 2007; Wang et al., 2012). It is
possible the highly selective activity observed here may support the incorporation of new information
into firmly embedded more generalized network activity by allowing the accurate comparison of new
information with previous experience.
Similarly it has been proposed that the mPFC is always involved in memory retrieval, playing a key
role in the representation of context, and in the authors’ case, context referring to any information
relevant for the current experience to guide adaptive behaviour (Euston, Gruber, & McNaughton,
2012). They hypothesize that for the retrieval of recently acquired memory, the role of mPFC is to
represent context, events and responses whereas the hippocampus stores the index linking these
features together. However, consolidation reorganizes these networks, and the mPFC acquires a more
important role in the storage of the memory, both representing and storing context-event-response
indices for remote memory retrieval (Euston et al., 2012). The theory accounts for the discrepancy in
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behavioural impairments from mPFC disruption at recent and remote time-points by suggesting that
compensation from other prefrontal regions and stronger hippocampal indices at the recent time-
point may permit memory retrieval without the mPFC, but a mPFC consolidated memory would
always require the mPFC for remote retrieval (Euston et al., 2012). Alternatively, according to this
model, where the mPFC both represents and stores contexts and events for remote memory, here the
presence of highly selective cells may represent mPFC indices whereas the presence of more
generalized activity may serve as the contextual representations.
However the evidence presented here shows the presence of both types of selectivity in the initial
stages of memory acquisition, and if these highly selective neurons represent cortical indices, they
are not gradually acquired as is suggested in the model. It is also possible that these neurons reflect
hippocampal computations in the initial acquisition of the memory, and their role in indexing or
representing cortical activation patterns is acquired through gradual consolidation. Both of these
postulates remain hypothetical, but present avenues of future investigation.
Here I show that as the memory is strengthened and consolidated the mPFC population code
becomes more generalized, less selective for behaviourally irrelevant features (Tone vs. Light) and
more selective for behaviourally relevant features (Alone vs. Paired). This generalization likely
reflects the extraction of the relevance among overlapping experiences. I also report that when
adding new information to the well-established associative memory, the network appears to encode
this information in a more generalized way, matching the present existing encoding strategy.
Following 30+ days of the trace eyeblink paradigm, I ran several animals in a similar procedure that
used the same stimulus for both sessions but manipulated the conditioning context. Here I showed
less selective encoding of the context specifically in the CS-Paired conditions. However because I
did not run a similar procedure before the original conditioning paradigm I cannot rule out that the
data observed during context manipulation is not simply the default code by which features of the
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environmental context are represented within the prelimbic region. I propose that given contextual
decoding errors were specific to the CS-paired conditions and I observed accurate decoding of
environmental contextual features in the CS-Alone conditions, information about the environmental
context can be represented by highly selective prelimbic activity. This suggests that the observed
results were not simply a default property of the prelimbic region but were a function of the existing
coding strategy. Again this may represent the extraction of relevance among the prelimbic ensemble.
These results fit the views of Euston et al. (2012) in that the extraction of information has less to do
with the particular mode of the information and more to do with its relevance for guiding adaptive
responding. Here the prelimbic population code became less selective for the identity of the stimuli
because the identity did not provide relevant information, and became more selective for the meaning
of the stimulus within a session. Changing the environmental context occurred when this pattern was
already established, and it is possible that the prelimbic code quickly recognized that this new
environment was not adding any new information to the relevance of the stimuli. Despite a new
environment, the same relationship existed within a session, first the stimulus was not relevant
(Alone trials), then suddenly it became relevant (Paired trials). Accordingly, if the relevance of the
stimulus did depend on the particular identity, or environmental context in which it was presented, I
predict that the medial prefrontal code would not generalize across these dimensions. This suggests
that the prefrontal code is selective for meaning, i.e. whether the current situation is relevant or not, a
proposition supported by findings that prefrontal neurons can develop selectivity to cues signaling
both reward (Mulder, Nordquist, Örgüt, & Pennartz, 2000; Peters, O’Donnell, & Carelli, 2005) and
punishment (Gilmartin & McEchron, 2005; Takehara-Nishiuchi & McNaughton, 2008). This
contrasts sharply from hippocampal encoding, which is selective for contextual details regardless of
their behavioural relevance (Wilson & McNaughton, 1993).
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These results together describe encoding properties of the prelimbic mPFC and highlight changes in
prelimbic representations that may reflect the reorganization of long-term memory networks during
the process of memory consolidation.
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Chapter 4
Prelimbic-cortical communication shaping changes in the selectivity of the prelimbic ensemble code with consolidation
4.1 Introduction
In the previous chapter I presented evidence that with learning and consolidation, the prelimbic
ensemble code gains a stronger selectivity for relational stimulus features while losing the
selectivity for physical stimulus features. Here, I sought to gain some insight into the network
mechanisms underlying these observed changes in encoding.
Over the same time-course in which I showed changes in prelimbic memory encoding, our lab
has previously shown that local field potentials (LFPs) in the lateral entorhinal (LEC) and
prelimbic medial prefrontal cortices (PrL mPFC) exhibit patterns of oscillatory activity
suggesting that their interaction is strengthened and becomes correlated with memory expression
over the course of learning and consolidation in trace eyeblink conditioning (Takehara-Nishiuchi
et al., 2012). This, along with the necessity of the LEC for the expression of old, consolidated
memory (Morrissey et al., 2012) suggest that the interaction between the LEC and mPFC may
play a critical role in memory consolidation and expression. Moreover, anatomically, the LEC
sits as the key transitional structure between the neocortex and hippocampus, and through its
reciprocal connectivity with the mPFC, may serve a similar function between the neocortex and
mPFC (Jones & Witter, 2007; Menno P. Witter, 2007). The LEC may, therefore, be optimally
positioned as an intermediary between a hippocampal driven memory acquisition network and a
mPFC driven consolidated memory network (Insel & Takehara-Nishiuchi, 2013; Takehara-
Nishiuchi, 2014). Together, these findings raise the possibility that the incoming inputs from the
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lateral entorhinal cortex provide neurons in the prelimbic cortex with necessary information to
generate and shape the memory-selective ensemble code during learning and consolidation.
Although our previous studies support the critical involvement of the LEC in consolidated
memory, the LEC is one of many regions that project to the prelimbic region. Therefore, it is
possible that afferent regions other than the LEC may also contribute to the shaping of prelimbic
ensemble codes with consolidation. For example, the ventral hippocampus (vHPC) sends a direct
mono-synaptic connection to the prelimbic region, and it has been shown in several studies that
their interaction increases during states of high anxiety (Adhikari et al., 2010, 2011), but also
during tasks requiring mnemonic demands, such as spatial working memory (Spellman et al.,
2015). Based on these findings, one theory posits that the connection between the ventral
hippocampus (vHPC) and mPFC is important for memory consolidation and to guide dorsal
hippocampal-mPFC communication in the development of mPFC memory representations
(Preston & Eichenbaum, 2013).
Another important afferent region is the perirhinal cortex (PER), which maintains very similar
patterns of connectivity with the prelimbic region as the LEC, showing strong direct reciprocal
connectivity with the prelimbic and most of the medial prefrontal cortex, but very little with the
caudal anterior and posterior cingulate areas (Hoover & Vertes, 2007; Jones & Witter, 2007).
The PER provides the LEC with the most dense input of all cortical regions (Kerr et al., 2007).
The PER has also recently been observed to be important in trace eyeblink memory acquisition
(Suter et al., 2013). Fewer studies have investigated the role of the PER in memory
consolidation, however plasticity in and communication between the PER and mPFC was shown
to be necessary for long-term memory retrieval in conjunctive recognition memory (Barker &
Warburton, 2008). In addition, disruption of the PER was also shown to impair fear context
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memory up to 100 days after training, suggesting that similar to the LEC, the PER may continue
to play an active role in the retrieval of consolidated memory (Burwell, 2004). Recordings of
these regions have also revealed that medial prefrontal-perirhinal learning dependent interactions
promotes enhanced perirhinal-entorhinal communication, an important pathway for the transfer
of hippocampal activity to neocortical sites (Paz et al., 2007).
Here I sought to systematically examine the modulation of prelimbic neuron firings by these
afferent regions by looking at the phase-locked firing of prelimbic neurons to local field potential
oscillations in these afferent regions. Previous studies reveal that oscillatory neuronal activity
reflects fluctuations in the excitability of local neurons (that is, fluctuations in membrane
potential shifts), thereby affecting the timing of local neurons’ firing (i.e., their output) as well as
their sensitivity to incoming signals (Buzsaki, 2004; Siapas et al., 2005; Sirota et al., 2008).
Therefore, if a prelimbic neuron is modulated by incoming inputs from an afferent region, it
should preferentially fire when the oscillation in the afferent region reaches the depolarized state
in which afferent neurons are most likely to fire. Based on this, I quantified the degree of
modulation on each neuron by examining the phase-locked firing to theta oscillations in the
lateral entorhinal, perirhinal cortex, and ventral hippocampus. Theta oscillations were targeted
because of our observation of learning related interactions between the LEC, mPFC, and dorsal
hippocampus at this frequency (Takehara-Nishiuchi et al., 2012) in addition to other studies
reporting phase-locked firings of mPFC neurons to ventral hippocampal theta oscillations
(Adhikari et al., 2011).
I first categorized prelimbic neurons based on the particular afferent region that modulated their
firing activity. This analysis revealed that individual prelimbic neurons show preferential phase-
locked activity to particular afferent regions. Based on this finding, I categorized neurons into
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“functional cell types”, such as LEC-, PER- and vHPC-modulated cells. Subsequently, I then
performed the same sets of analysis used in the previous Chapter to these cell types separately, to
examine whether memory representations and its changes with consolidation differ depending on
the functional cell type.
4.2 Results
Animals were rats described in Chapter 3 with functioning tetrodes located in the prelimbic
medial prefrontal cortex and functioning LFP electrodes located in the lateral entorhinal cortex
(LEC, n=4), perirhinal cortex (PER, n=4), and ventral hippocampus (vHPC, n=4) (see Figure
18).
Rats underwent daily conditioning in the trace eyeblink paradigm for 30+ days (see Chapter
2.5.1 section for a clear overview of the procedure) with activity recorded throughout from
tetrodes and LFP electrodes (see Chapter 2.5.3). The paradigm used two different conditioned
stimuli (Tone & Light) in two different relational conditions (Alone & Paired) to allow
comparisons across 4 conditions that differed in their combination of the identity and
relationship of the stimulus (Tone-Alone; Tone-Paired; Light-Alone; Light-Paired).
4.2.1 Prelimbic neuron phase-locking to the rhinal cortices and hippocampus
To examine the coupling of prelimbic neuron spiking activity and local field potential (LFP)
activity from the various brain regions I looked at the phase-modulated firing (phase-locking) of
prelimbic (PrL) neurons to theta oscillations recorded from the lateral entorhinal cortex (LEC),
perirhinal cortex (PER) and ventral hippocampus (vHPC). By combining spike and LFP data
together I can assess how the activity of individual neurons within the PrL is modulated by the
oscillatory activity in other brain regions involved in this task. This phase-locking of neurons to
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oscillations has been a proposed mechanism for the coordinated communication between
connected brain regions (Vinck et al., 2012) and may be important for cell assembly formation
(Canolty et al., 2010; Singer, 1999) and information coding mechanisms. This analysis allowed
me to quantify the proportion of PrL neurons that are significantly phase-locked to theta
oscillations in the three brain regions sampled, and the degree to which they are modulated.
Looking specifically at the interval between the CS and US, I observed phase-locking in PrL
neurons to all three brain regions (see Figure 18).
Similar proportions of prelimbic neurons phase-locked to theta oscillations in each region were
observed (LEC: 26.8%; PER: 27.4%; vHPC: 25.3%, see Figure 18). It was possible that there
was considerable overlap between regions and many of the same cells were showing significant
phase-locking to more than one region. To look at selectivity directly, instead of comparing
significant versus non-significant, I quantified the degree of phase-locking with the Rayleigh z
value. This is the log transformed mean resultant vector length (MRL). MRL is the sum of the
vectors representing the phases at which each spike occurred, divided by the number of spikes.
This value quantifies the degree to which a cells spikes occur at a particular phase of the
oscillation, or the strength of locking. Therefore I calculated the mean Rayleigh z value of
significantly locked cells for each region, and that cell’s Rayleigh z value with the other two
regions. For example, if cell 1 was significantly phase-locked to theta oscillations in region A, I
took the Rayleigh z value of cell 1 with region A as well as the Rayleigh z value of cell 1 with
regions B and C. Likewise, if cell 2 was significantly phase-locked to theta oscillations in region
B, I took the Rayleigh z value of cell 2 with region B, as well as region C and A. This was done
for all cells that displayed significant phase-locking with each region and the averages were
plotted. These data are represented in Figure 19, which reveals a high degree of specificity for
which regions theta oscillations a particular cells firing activity was modulated by. PrL neurons
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phase-locked to LEC theta showed on average significantly weaker modulation to PER and
vHPC theta. The same pattern was observed for PrL neurons phase-locked to PER theta and
vHPC theta.
Together these data show that the activity of neurons in the PrL are modulated by the oscillatory
activity in the LEC, PER, and vHPC in this task. Additionally, individual PrL neurons display a
preference for which region’s oscillatory activity they are phase-locked to. This leads to the
possibility that fairly distinct populations of neurons within the PrL synchronize with different
cortical structures. If this is the case, these distinct populations may be encoding different
information relevant for the task.
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Figure 18. Histology and examples of prefrontal locking and the proportion of locked cells.
A) Representative example of local field potential (LFP) electrode tips in their target locations (left column) and illustrations of all verified electrode positions in the lateral entorhinal cortex (LEC, top), the perirhinal cortex (PER, middle), and the ventral hippocampus (vHPC, bottom). On the right are example LFP traces from each region (black) overlayed with the filtered theta (7-11 Hz) trace (blue line) from a 2 second period centered on the onset of the conditioned stimulus (CS). Black bar indicates the stimulation from the unconditioned stimulus (US) during which data was not analyzed due to noise artifact from the shock. On the far right are three representative examples of different individual prelimbic neurons that were significantly phase-locked to theta oscillations from the three regions as demonstrated by spike counts across the different phases of the oscillations. B) The proportion of neurons that were significantly phase-locked to each of the three regions theta oscillations.
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4.2.2 Prelimbic phase-locking is not feature specific
To assess whether the different populations of neurons were differentially encoding task relevant
information, I looked at patterns of phase-locking in individual neurons categorized by their
selectivity as described in detail in Chapter 2.6.3. These categories defined responsive cells
selectivity to the different task features in the 4 conditions (1-Alone: CS only trials; 2-Paired:
CS-US trials; 3-Tone: Tone CS trials; 4-Light: Light CS trials) resulting in 4 types of selectivity
(1-Non-selective: Alone=Paired & Tone= Light; 2-Identity-selective: Alone=Paired & Tone ≠
Light; 3-Relationship-selective: Alone ≠ Paired & Tone = Light; 4-Conjunctive RxI: Alone ≠
Paired & Tone ≠ Light) plus cells that were not responsive (Non-responsive). Taking the
populations of cells phase-locked to each region, I calculated the proportions of the different
types of selectivity observed within these populations. For example, of all cells phase-locked to
region A, how many showed Identity-selective activity out of all Identity-selective cells? These
analyses revealed no differences in the type of information neurons phase-locked to the different
regions were selective for (see Figure 19). The only categories whose proportions were
significantly greater than Non-responsive cells were found to be locked to the LEC (Non-
selective, Identity, and Conjunctive) and PER (Conjunctive). However there were no differences
among the selectivity of responsive cells or between proportions locked to the different regions.
Here we do not see any differences in phase-locking based on single neurons selectivity for task
features, and at least at the single neuron level, neurons coding a particular feature of the task
were no more likely to be phase-locked to a particular cortical region.
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Figure 19. Prelimbic selectivity of phase-locking to specific regions and type of information
encoded by these cells.
A) Left column, each row displays that the averaged Rayleigh z value of prelimbic neurons significantly phase-locked to theta oscillations in a specific region (lateral entorhinal cortex LEC-modulated, top; perirhinal cortex PER-modulated, middle; ventral hippocampus vHPC-modulated, bottom) were
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significantly greater than the averaged Rayleigh z value of each of these cells phase modulation to the other two regions. Dotted lines indicate the typical threshold for Rayleigh z values critical values at p < .05 (z = 2.9) and p < .01 (z = 4.5). Error bars represent standard error B) Right column, shows the proportion of prelimbic neurons selective for the different task features that were phase-locked to the different theta oscillations (lateral entorhinal cortex LEC, top; perirhinal cortex PER, middle; ventral hippocampus vHPC, bottom). Error bars represent 95% Copper-Pearson confidence intervals. Red dotted line signifies the cut-off for difference compared to the proportion of phase-locked neurons that were Non-responsive in the task.
4.2.3 Regional phase-locking does not change across the learning stages
Though these previous analyses looked at all neurons recorded across learning, consolidation and
post-consolidation retrieval, it is possible that differences may emerge when looking at specific
time-points along this continuum of the memories existence. This would offer potential
mechanisms through which PrL representations change as the memory becomes consolidated
within the PrL network. Such differences may be related to the shift I observed in encoding
within the PrL population reported earlier. Differences in phase-locking across time was assessed
by calculating the proportion of neurons phase-locked to theta oscillations in the different
regions across the behaviourally defined learning periods reported earlier (see Chapter 2.6.1).
Figure 20 displays the proportion of total neurons recorded in each phase of learning, from prior
to acquisition of the association (Before), to during learning (During), to 3 weeks after stable
responding (3w-Post), that were significantly phase-locked to each region. Chi-Square analysis
revealed no differences across time, although there does appear to be a slight decrease in PrL
neurons locked to LEC and PER theta at the 3w-Post time-point compared to during acquisition.
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Figure 20. Phase-locking across learning stages.
The proportion of prelimbic neurons phase-locked to the lateral entorhinal cortex (LEC), perirhinal cortex (PER) ventral hippocampus (vHPC) calculated at three behaviourally defined periods of learning. There were no differences in the proportions of neurons locked to the different regions between all days prior to displaying a conditioned response (CR) in 30% of trials (Before), to all days in between Before and when the rat displayed a CR in 60% of trials for 2 consecutive days (During), to the period encompassing three weeks following the end of the During stage (3w-Post). Error bars indicate 95% Clopper-Pearson confidence intervals.
4.2.4 Decoding of region-specific phase-locking populations across time
Earlier I reported that at the single neuron level, the proportion of neurons selective for specific
task features does not change across the different learning and retrieval stages. However at the
population level, differences began to emerge. Therefore it was not entirely surprising that at
least proportionally, differences were not evident in the selectivity of phase-locked neurons.
However the possibility still remains that differences may emerge when taking these region-
specific phase-locked neurons as a population. Here I used a supervised machine learning
algorithm, support vector machine (SVM), to decode the information contained within
populations of cortical theta phase-locked prelimbic neurons separated by regional modulation
(LEC-modulated, PER-modulated, vHPC-modulated) and the learning stage in which they were
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recorded. Populations of neurons phase-locked to LEC, PER, and vHPC theta recorded from
different stages of the memory were fed independently into the SVM decoder and the ability of
the decoder to correctly classify trial conditions given only the information contained within
these different populations was assessed. Because a fair sample size is required to obtain
consistent classification with this data (n > 50 from earlier analyses), time-periods of learning
had to be divided more liberally than in previous analyses to ensure enough neurons were being
sampled from each time-point (fewer neurons from Before period) to achieve accurate decoding
and to prevent significant sample-bias when down-sampling from larger population time-periods
(many neurons from During period). In previous SVM analyses ( see Chapter 3.2.5) the During
learning period was divided in half with Early learning comprising the first half, and Late
learning comprising the second half. The Early period was more similar to the Before period, and
the Late period was more similar to the 1w-Post period (1 week after asymptote). Similarly, data
from 2 and 3 weeks after asymptote (2w-Post & 3w-Post) produced similar patterns of results
(see Figures 12 & 14). Therefore I combined the Before and the Early phase of the During
learning stage into one population (Early). I combined the Late phase of During learning with
1w-Post into another population (Late), and I combined the 2w-Post and 3w-Post into a third
population (Post). I then ran SVM on these three time-periods with data from prelimbic neurons
phase-locked to the different regions with the same number of neurons selected from each region
for each time-point (n = 80).
Figure 21 reveals the quantification of the accuracy of classification from these populations for
the different features of the task (Relationship, Identity, Conjunction RxI) visualized across
learning stages (Figure 21, A) and across regions (Figure 21, B) relative to the chance level of
prediction (50% Relationship/Identity; 25% for RxI).
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First, separate chi-square analyses were run on each type of information, Relationship (X2(6) =
56.459, p < .001), Identity (X2(6) = 61.165, p < .001) and RxI (X2(6) = 113.472, p < .001), and
were all significant. Post-hoc analyses compared individual proportions within each region
across time. For classification of the Relationship, LEC- and PER-modulated cells significantly
decrease from Early to Late and Post learning (LEC z’s = -6.78 & -11.11, p’s < .001,
respectively; PER z’s = -6.03 & -10.67, p’s <.001, respectively). In contrast, neurons phase-
locked to vHPC theta significantly increase Relationship classification accuracy Post learning
compared to Early and Late learning (z’s = 23.63 & 24.32, p’s < .001, respectively).
For classification of the Identity, LEC- and PER-modulated cells significantly decrease from
Early to Late learning and remain lower during Post learning (LEC z’s = -26.56 & -13.43, p’s <
.001, respectively; PER z’s = -7.76 & -7.90, p’s <.001, respectively). vHPC-modulated cells also
significantly decrease in Identity classification accuracy from Early to Late learning (z = -5.01, p
< .001).
For the conjunction of Relationship and Identity the exact same patterns was observed as for
Identity classification, with LEC- and PER-modulated neurons decreasing in accuracy from
Early to Late learning, and remaining lower Post learning (LEC z’s = -34.86 & 24.62, p’s <.001,
respectively; PER z’s = -6.41 & -7.27, p’s <. 001, respectively). vHPC-modulated neurons
classification accuracy of the RxI conjunction also decreased from Early to Late learning (z = -
6.31, p < .001) but return to Early learning levels by Post learning.
These results compared the selectivity of the population code across memory stages in each
functional cell-type, and observed that with learning and consolidation, the selectivity for the
physical features of the stimuli (their identity), was reduced. In parallel, the selectivity for the
relational stimulus features (their relationship) was reduced in LEC- and PER-modulated cells
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while it was increased in vHPC-modulated cells. The selectivity for specific trial types, the
conjunction of the stimulus relationship and its identity, was also reduced in the LEC- and PER-
modulated cells while it was maintained and strengthened in vHPC-modulated cells.
These results suggest that the enhanced selectivity for relational features was driven by sub-
groups of prelimbic neurons whose firings are modulated by the activity of the ventral
hippocampus. In contrast, the weakened selectivity for physical stimulus features was observed
in all functional cell types, indicating that it may result from local network changes rather than
the interaction with specific afferent regions.
Next, separate chi-square analyses were run on each time-period, Early (X2(4) = 15.645, p =
.004), Late (X2(4) = 65.468, p < .001) and Post (X2(4) = 14.905, p = .005), and all were
significant. Post-hoc analyses compared selectivity between neurons phased-locked to the
different regions. For the Early phase of learning, LEC- and PER-modulated cells were both
more selective for the Relationship of the stimulus than vHPC-modulated cells (z’s = 14.55 &
18.15, p’s < .001), whereas LEC-modulated cells were more selective than both PER- and
vHPC-modulated cells for the Identity of the stimulus (z’s = 9.64 & 12.53, p’s < .001). LEC- and
PER-modulated cells were similarly more selective for the conjunction of Relationship and
Identity (RxI) than vHPC-modulated cells (z’s = 21.59 & 16.64, p’s < .001).
Comparison across the Late learning phase again revealed higher accuracy among LEC- and
PER-modulated cells for the stimulus Relationship versus vHPC-modulated cells (z’s = 7.76 &
5.64, p’s < .001). However here, among LEC-modulated cells, selectivity for the Identity was
significantly lower than PER- and vHPC-modulated cells (z’s = -14.11 & -10.39, p’s < .001).
PER-modulated cells represented the highest selectivity for the RxI conjunction compared to
LEC- and vHPC-modulated cells (z’s = 19.93 & 10.52, p’s < .001).
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Finally, at the Post learning period vHPC-modulated cells were more selective than LEC- and
PER-modulated cells for the Relationship (z’s = 9.62 & 5.10, p’s < .001), Identity (z’s = 9.54 &
5.49, p’s < .001) and their conjunction (RxI, z’s = 21.12 & 8.02, p’s < .001).
These results compared population selectivity across functional cell-types in each stage, and
reveal that during the early stages of learning, selectivity for the stimulus relationship was higher
in the LEC- and PER-modulated cells than in vHPC-modulated cells while the selectivity for
identity was the highest among LEC-modulated cells. Therefore the highest accuracy rate in
decoding specific trial types (RxI) was observed in LEC-modulated cells, followed by PER-and
then vHPC-modulated cells. During the late stages of learning, the selectivity for relationship
remained higher in the LEC- and PER-modulated cells than in the vHPC-modulated cells. The
selectivity for the stimulus identity was observed to significantly drop in LEC-modulated cells
but remained in PER- and vHPC-modulated cells. This resulted in a higher accuracy rate in
decoding specific trial types (RxI) in the PER-modulated cells than the other cell-types.
After consolidation, the selectivity for relationship significantly increased in the vHPC-
modulated cells, making them more selective than the other cell types, whereas the selectivity for
identity was reduced in general across the three cell-types. This resulted in a higher accuracy rate
in decoding specific trial types (RxI) in the vHPC-modulated cells, followed by PER- and then
LEC-modulated cells.
Together these results highlight network changes across learning and consolidation that appear to
support the changes in population encoding revealed in Chapter 3 across time.
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Figure 21. SVM Classification Accuracy for Stimulus Features Across Time as a Function
of Functional Cell Types.
A) Percentage of accurate classification for the different features of the task, Paired vs. Alone (Relationship), Tone vs. Light (Identity) and the conjunction of the two (RxI) across the defined learning stages. Accuracy in classification of the stimulus Relationship (top left) increased from Late (medium grey) to Post learning (dark grey) in neurons phase-locked to ventral hippocampal theta oscillations (vHPC). In contrast accuracy in classification of the stimulus Identity (top right) decreased from Early (light grey) to Late learning in neurons phase-locked to the lateral entorhinal cortex (LEC) and perirhinal cortex (PER). Classification accuracy of the conjunction of Relationship and Identity (RxI) mirrored these findings, decreasing from Early to Late learning in LEC- and PER-modulated neurons, with no change from Early to Post learning in vHPC- modulated neurons but a decrease during Late learning. B) Percentage of accurate classification for the different features of the task across the defined functional cell types. During Early learning (left), LEC-(green) and PER-modulated(blue) cells were more selective for the Relationship of the stimulus. LEC-modulated cells were highly selective for the Identity and the conjunction of the two (RxI). By Late learning (middle) LEC- modulated cells lost selectivity for the Identity, and PER- modulated were the most accurate for specific stimulus features (RxI). During Post learning periods (right), vHPC-modulated (purple) cells were more selective for all features, particularly the Relationship. * p < .005 using the z test to compare chi-square cells. Error bars indicate 99% Clopper-Pearson confidence intervals. n = 80 for each cell type and time-point.
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4.3 Discussion
4.3.1 Summary
Here I report theta modulated activity in single prelimbic medial prefrontal cortex (PrL mPFC)
neurons to oscillations in the lateral entorhinal (LEC), perirhinal (PER), and ventral
hippocampus (vHPC) throughout the learning, consolidation and retrieval of an associative
memory in trace eyeblink conditioning (Figure 18). I reveal the presence of selective populations
of PrL neurons modulated by theta oscillations in these different afferent regions. Based on this
finding, neurons were categorized into distinct functional cell types created from their
preferential phase-locking as LEC-modulated, PER-modulated, or vHPC-modulated (Figure 19).
The proportions of each cell type (Figure 18) and the types of selectivity of single neuron activity
within each type were comparable (Figure 19). However the population activity of these distinct
cell populations revealed changes in selectivity for particular features of the memory throughout
learning and consolidation (Figure 20). This was assessed by the ability of a machine learning
algorithm to accurately classify trial conditions given only the firing activity of discrete sets of
populations of single neurons. Here accurate classification implied that enough information
about the various task features was present within the population spiking activity, whereas errors
implied missing information, and taken together they reveal a way of assessing the
representations actually contained within these networks.
These findings highlight a potential dynamic through which prelimbic representations of the
associative memory evolve over its learning and consolidation in a long-term network. Neurons
phase-locked to LEC and PER theta oscillations show very accurate classification in the Early
phases of learning for Relational and Conjunctive features of the stimulus, with LEC-modulated
cells also strongly selective for features of the stimulus Identity. In contrast neurons phase-
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locked to vHPC theta oscillations provide more accurate classification of the stimulus
Relationship at the Post learning period, overlapping with the point at which this memories
consolidation is presumed to be stabilized. These changes were not observed when looking at
single neuron selectivity proportions, but emerged when taking the population activity from
neurons phase-locked to particular regions. These interactions could serve as a potential
mechanism through which this memory becomes more generalized over-time, more selective for
the predictive features of the stimuli and less-selective for the particular details of the stimuli by
changes in patterns of communication with the ventral hippocampus and rhinal cortices.
Together these findings point to a network dynamic that may shape PrL memory representations
as part of a long-term memory framework.
4.3.2 Functionally defined cell types in the prelimbic cortex
Phase-locking of individual neurons spiking patterns to the oscillations of a different brain region
has been proposed to support the flexible routing of information across regions by coordinating
spike time and aiding in the creation of functional cell assemblies (Buzsaki, 2004; Fell &
Axmacher, 2011; Fries, 2005; Harris & Gordon, 2015; Harris, Csicsvari, Hirase, Dragoi, &
Buzsaki, 2003; Singer, 1999). Here I report that the spiking activity of many neurons in the PrL
is correlated with the phase of ongoing oscillations in the LEC, PER and vHPC during learning
and retrieval. Synchronized mPFC spiking patterns to hippocampal and ventral hippocampal
theta oscillations has been well established (Adhikari et al., 2010, 2011; Siapas et al., 2005), and
the proportion of phase-locked PrL neurons reported here is similar to what has been described
(Siapas et al., 2005). A similar proportion of PrL neurons were found to be phase-locked to the
three targeted brain regions which raised the possibility that the same cells were locked to theta
oscillations in all three regions. However looking at the degree of phase-locking revealed what
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appeared to be distinct populations of PrL neurons preferentially phase-locked to different
regions. This finding suggested that prelimbic neurons could be categorized into distinct
functional cell types based on their preferential phase-locking to theta oscillation in a particular
afferent region.
Previous studies have categorized cells for example based on differences in neurotransmitter
release or the expression of particular proteins. To my knowledge, this study is the first to
categorize prelimbic neurons based on which afferent region they are modulated by. This
complements recent studies that define the function of cells based on their projection targets
(Courtin et al., 2014; Rajasethupathy et al., 2015; Senn et al., 2014). It is important to note that
our approach here was to categorize neurons based on the incoming inputs, whereas these other
works categorize neurons based on their output targets. Therefore, here we are examining how
the local computation/representation is modulated by incoming inputs, rather than how prefrontal
computations affect the efferent targets.
When looking at these defined sub-populations, there did not appear to be differences in terms of
the type of information they were selective for when looking at the proportions of individual
neuron selectivity. There was also no change in the relative proportions of phase-locked neurons
to each region across learning, consolidation and over-training. However the population activity
of these different ensembles were observed to represent varying degrees of selectivity for the
different task features that changed across the different learning stages.
The fact that at the single neuron level, the proportions of cells phase-locked to the different
regions did not change across the learning stages was initially surprising, given for example the
evidence for prefrontal-entorhinal interactions in long-term retrieval of event memories and
possibly their consolidation (Paz et al., 2007; Takehara-Nishiuchi et al., 2012). However there is
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likely more dynamic and complex interactions taking place between these regions than can be
captured simply by the number of neurons observed to be phase-locked, as was discovered in the
previous Chapter when looking at selectivity. The previous finding that LEC-mPFC interactions
increase over consolidation also revealed that they still interact even early in learning,
demonstrated through theta synchronization (Takehara-Nishiuchi et al., 2012). However it was
the relative strength of this synchrony during trials in which the memory was expressed
compared to those in which it was not that increased with learning, and not simply their
interaction in general (Takehara-Nishiuchi et al., 2012). Therefore this does not necessarily
expect that the proportion of neurons phase-locked to LEC theta would change across time.
That the proportions of neurons selective for particular features of the task did not differ within
populations of prelimbic neurons phase-locked to the different regions was also not particularly
surprising for the same reason. I showed in Chapter 3 that despite no changes in the proportion of
selective neurons across time, changes in encoding at the population level were evident. Here,
despite similar proportions of neurons selective for different task features, the population activity
of these different functional cell types did differ, both between groups and across time. The
population of LEC-modulated cells was shown to contain highly accurate information about the
identity of particular stimuli as well as their relational meaning in a given trial in the very early
stages of learning. This selectivity was lost by the later stages of learning and post-consolidation,
particularly for the physical identity features. In contrast, populations of cells modulated by
vHPC theta oscillations (vHPC-modulated) contained much weaker selectivity for these features
very early on in learning, but acquired strong selectivity for the relational features of the stimuli
after the consolidation of the memory. Populations of PER-modulated cells showed similarities
to both LEC- and vHPC-modulated cells, like LEC-modulated cells they appeared to contain
initially high selectivity for relational features which decreased with time, but their selectivity for
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identity features more resembled that of vHPC-modulated cells, decreasing slightly with learning
and then remained at a stable level. These changes may reflect the shifting networks responsible
for the retrieval of the memory, particularly as hippocampal output becomes less important for
the memory retrieval across time.
Looking at the encoding of discrete memory features, I observed that the information about
specific conditions (memory with specific details) represented within the activity of different
phase-locked cell populations shifted among these populations across time. Specifically, the
ensemble of LEC-modulated cells could be accurately decoded for specific trial types in the early
days of the learning paradigm, with high selectivity for both the Relationship and Identity of the
stimuli. By the post-consolidation phase, the population of vHPC-modulated cells became the
most selective for specific trial conditions, driven mainly by their increased selectivity for the
Relationship of the stimuli. During the transition period between these two stages, LEC-
modulated cells had significantly decreased the selectivity for both Identity and Relationship
features while vHPC-cells had yet to acquire stronger Relationship selectivity. Here PER-
modulated cells showed the highest selectivity among the three cell-types despite lower
selectivity for the Identity and the Relationship compared to the early phase.
It is important to note the high accuracy rate across regions at the different time-points was
achieved by different sources. The high accuracy rate of LEC-modulated cells was driven by
their selectivity for both relational and physical features while that of vHPC-modulated cells was
driven by the selectivity for relational features alone.
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4.3.3 Implications of the role of each afferent input during memory acquisition and retrieval
The prelimbic mPFC is shown to be involved in event memory from its initial formation,
however the ensemble code for an association is observed to develop gradually over-time
(Hattori et al., 2014; Takehara-Nishiuchi & McNaughton, 2008). The information contained
within populations of prelimbic (PrL) neurons phase-locked to the different regions of the
hippocampal system targeted here, the lateral entorhinal (LEC), perirhinal (PER) and ventral
hippocampus (vHPC), was observed to change across behaviourally defined periods of learning
and retrieval.
The present study demonstrated that the input from each afferent region may evoke prelimbic
ensemble activity which contains differing levels of selectivity for specific features of experience
at different time-points. This point may provide insight into neurophysiological underpinnings of
changes in memory representation through memory consolidation. Previous studies show that
specific memory is mediated by the hippocampus, but that more generalized memory is mediated
by the neocortex (see McClelland et al., 1995; Winocur et al., 2010). Our study overall indicates
that both specific and generalized representations exist within the ensemble code in the prelimbic
region, though the specificity is mainly driven by the behaviourally relevant features.
In the initial days of acquisition in this paradigm, a time-point when hippocampal computations
are essential to the formation of the memory (Moyer et al., 1990; Weible et al., 2006; Weiss et
al., 1996; Weiss et al., 1999), LEC-modulated prelimbic ensemble activity is highly selective to
all task features. This may reflect highly selective dorsal hippocampal encoding and output
through the entorhinal region during the initial formation of the memory association. As the
association becomes firmly and stably acquired, the particular features of the stimuli become less
relevant, as they do not contain any unique predictive information about the outcome. At this
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time-point LEC-modulated cells sharply decline in the information about the particular stimuli
encoded in their population activity, mirroring the decreased selectivity observed overall within
the prelimbic population reported in Chapter 3. Although decreased, identity information is still
maintained in PER- and vHPC-modulated cells at this time, as is relational selectivity by LEC-
and PER-modulated cell groups. This time-point represents a very important stage in the
memory’s representation. It overlaps with what can be described as a transitional period of the
memory networks responsible for retrieval of the memory as retrieval becomes less dependent on
hippocampal function and more dependent on mPFC functioning (Takehara et al., 2003).
In the several weeks following this period, the memory is presumed to be consolidated within the
mPFC network, as hippocampal dysfunction has no impact on retrieval, and mPFC dysfunction
has devastating effects (Kim et al., 1995; Oswald et al., 2010; Kaori Takehara et al., 2003). The
disruption of NMDA-receptor mediated plasticity mechanisms within the prelimbic region also
no longer has any effect on memory expression at this time-point (Takehara-Nishiuchi et al.,
2006). Here the major change observed was an increase in selectivity of vHPC-modulated cells
to the relational features of the memory, while again selectivity of stimulus identity features
remained relatively lower within all three cell populations. These changes appear to reflect the
overall encoding of the prelimbic ensemble which becomes more generalized for specific
stimulus features at this time-point and more selective for their relevance.
It is therefore possible that the specific representation of experience here was driven by the
incoming inputs from the lateral entorhinal cortex early in learning, and maintained thereafter by
perirhinal input. Whereas the acquired generalized representation was driven by decreased
selectivity from lateral entorhinal input as the hippocampus becomes less engaged in the retrieval
of the memory. Conversely, the input from the ventral hippocampus may serve to sharpen the
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selectivity for the behaviorally relevant dimension of the memory after the consolidation process
has been completed.
4.3.4 Differences in selectivity between functional cell-types
I show that mPFC neurons phase-locked to ventral hippocampal theta during over-training
become more selective for the relational features of the stimuli. At this same time-point the
prelimbic population code displayed a similar pattern, suggesting that this population of vHPC-
modulated PrL cells may be driving this shift. This population of neurons seem to be important
for sharpening PrL selectivity to behaviourally relevant task features, distinguishing Alone from
Paired trials. The activity of ventral hippocampal neurons has been implicated in the formation
of schema like representations, generalizing across related events but distinguishing between
relevant contexts (Komorowski et al., 2013). Such input to the mPFC could presumably shape
activity towards the relevance of the stimuli (relational features) as opposed to their discrete
details. Considerable evidence also links ventral hippocampal activity to anxiety (reviewed in
Bannerman et al., 2004), ventral hippocampal-prefrontal synchrony has been observed during
anxiety behaviours (Adhikari et al., 2010) and is implicated in signaling the salience of a context.
Here the vHPC may be providing an anxiety signal to focus attention to the stimulus during the
behaviourally relevant Paired trials. However anxiety should be reduced rather than heightened
when the shock becomes predictable. So this explanation does not fit the finding of a
strengthened selectivity for the relationship of the stimulus from vHPC afferents after the
consolidation of the memory.
On the other hand, the Komorowski et al. (2013) findings demonstrate that vHPC neurons form
generalized representations across meaningfully distinct spatial contexts, suggesting this activity
is not merely an anxiety signal but may generate contextual representations signaling meaning.
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In the task employed in my thesis, the context may be the temporal order of the predictive value
of the CS, not predictive in the first 20 trials, and suddenly predicting the US in the latter 80
trials.
In contrast to vHPC-modulated population activity, neurons phase-locking to the rhinal cortices
(LEC and PER) became more generalized for all task features with over-training, but were
highly selective early in learning. However there were also differences observed between these
neuron populations. LEC-modulated cells were selective for all task features early in learning,
whereas PER-modulated cells did not show this same level of selectivity, particularly for the
Identity and Conjunctive trial features (RxI). It is possible that the high selectivity among LEC-
modulated cells directly reflects dorsal hippocampal computations and output during this early
phase of acquisition. The lateral entorhinal cortex receives direct projections from these
hippocampal computations through CA1 output targeting the deep entorhinal layers. This same
layer of entorhinal cells then forms the major source of projections to the PrL cortex (Hoover &
Vertes, 2007; Insausti et al., 1997; van Strien, Cappaert, & Witter, 2009). The perirhinal cortex
on the other hand, while receiving some direct hippocampal output, receives most of this output
indirectly through strong connectivity with the lateral entorhinal cortex (Burwell & Amaral,
1998b). Perirhinal-prelimbic afferents may therefore contain less detailed information than
lateral entorhinal-prelimbic afferents in this early stage of acquisition. These lateral entorhinal-
prelimbic afferents may therefore be important for prelimbic acquisition of the association, as it
is proposed that prelimbic memory representations extending beyond the working memory
capacity require hippocampal input during encoding (Euston et al., 2012). As the association is
gradually acquired and stabilized, hippocampal output becomes less important (Takehara et al.,
2003), and here I show that prelimbic representations become more selective for meaningful task
dimensions and more generalized for regularities among non-relevant dimensions. The decreased
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selectivity among all cell groups at this time-point reflects this, and particularly the decreased
selectivity of LEC-modulated cells may directly represent this transition in the network.
However perirhinal afferents may be less affected by this shift than LEC afferents and continue
to provide trial specific information.
Together I reveal changes in the feature encoding of populations of PrL neurons phase-locked to
different connected structures that mirrors the changes in the general prefrontal population code
over the course of learning and consolidation.
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Discussion
5.1 Summary
The role of the medial prefrontal cortex (mPFC) in the consolidation, long-term storage, and
retrieval of event memory in the brain has become an important area of focus in the neuroscience
of memory. Evidence of the importance of this brain region for the retrieval of old but not newly
acquired memories highlighted this collection of structures as a unique node in the episodic
memory network (Frankland et al., 2004; Maviel et al., 2004; Takehara et al., 2003). The mPFC
has also been implicated in incorporating new similar information into existing memory
frameworks (Wang et al., 2012) suggesting that this region may help form and update schema-
like mental representations of our past, and how the present fits with these existing
representations. However, what exactly is being encoded within the mPFC is not clear, neither is
our understanding of how these representations evolve in the brain throughout memory
consolidation. Put simply, part 1 of my thesis attempted to address what features of a memory
are actually represented by mPFC activity, and how the encoding of these features changes over
the course of the memory’s incorporation into a stable long-term network.
Part 2 addressed a related but different outstanding problem. Namely, how does the mPFC
acquire these representations, and what drives their evolution? To attempt to examine this
problem I looked at communication between the mPFC and several connected brain regions also
implicated in the episodic memory network, the lateral entorhinal cortex (LEC), the perirhinal
cortex (PER) and the ventral hippocampus (vHPC).
Using the trace eyeblink conditioning paradigm in which the identity of the stimuli (Tone or
Light) and relationship of the stimuli (Alone or Paired with US) was manipulated (Figure 4), I
revealed at the single neuron level the existence within the prelimbic mPFC (PrL) of both highly
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selective neurons, responsive to discrete features of the memory task, and highly generalized
neuronal responses (Figure 7). The proportions of this mixture surprisingly did not change over
the course of acquisition of the memory association or after several weeks of over-training
(Figure 9). Taking the activity of these single neurons together as a population however, I was
able to uncover differences in encoding across the time-course of acquisition and consolidation. I
first revealed that the population of activity became less similar during trials in which the
identity of the stimulus was the same (Tone-Alone and Tone-Paired) after the consolidation of
this memory compared to during its acquisition (Figure 11; Table 1). Further, using populations
of PrL neurons recorded at different stages of the memories acquisition and consolidation I was
able to train a supervised machine learning algorithm to accurately classify trial conditions
(Figure 13). Whereas accuracy overall did not change across the learning stages, patterns of
decoding did change. Specifically, the information represented in these populations became less
specific for the particular stimuli and more selective for their relational properties over the same
time-course that the consolidation of this memory has been observed (Takehara-Nishiuchi et al.,
2005, 2006). Together these data highlight characteristics of the PrL that could serve to support
its role in the long-term storage and maintenance of event memories in the brain; a structure
whose population responses become more generalized for overlapping and pertinent memory
features over-time but also still maintains discrete details of the memory within the network.
Secondly, by combining this data with the local oscillatory dynamics of other connected brain
structures, I was able to identity changes in the patterns of communication between these regions
and the PrL that may shape or reflect the changes in encoding just described. Through
simultaneous recordings of local field potentials from the LEC, PER and vHPC I was able to
observe distinct populations of PrL neurons modulated by these distant oscillations (Figure 18).
Similar to previous reports, I confirmed the presence of a population of PrL neurons phase-
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locked to theta oscillations in the vHPC during memory encoding (Adhikari et al., 2011) and also
reveal the presence of fairly distinct populations of PrL neurons modulated by LEC or PER theta
rhythms. The proportions of these populations (LEC-modulated, PER-modulated, vHPC-
modulated) were fairly similar and did not appear to change across time. However, looking at the
population activity of these different cell groups across time did reveal changes that may reflect
network interactions relevant to the consolidation of this memory. PrL neurons modulated by
rhinal theta rhythms (LEC and PER), but the LEC in particular, were highly selective for discrete
details of the memory during the early stages of its acquisition. However, by the time this
memory was well acquired and several weeks later, this population became far less selective.
Likewise, populations phase-locked to ventral hippocampal theta rhythms (vHPC-modulated)
though less selective overall, became more selective for behaviourally relevant features in the
several weeks following stable memory expression.
5.2 Conclusions
A greater understanding of the features of a memory encoded by the mPFC and how they change
through the process of memory consolidation are important aspects of our unraveling of the
networks and process by which memories are stored and maintained in the brain. Here I advance
this front by showing the level of detail represented within the prelimbic mPFC (PrL) and how it
changes throughout learning and over-training. I also highlight a potential process by which
these changes may take place, by observing differences in encoding across time in populations
linked to distinct parts of the memory network.
5.2.1 Role of medial prefrontal cortex in memory
The mPFC has emerged as a key node in the long-term memory network and there are many
different theories of how it fulfills this function, and of its role in the brain in general. For
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example, much of the focus of the role of the mPFC in the brain, particularly in human and
primate studies focus on its role in decision making in various shapes and forms (for e.g. conflict
monitoring: Botvinick, Cohen, & Carter, 2004; error detection: Holroyd, Coles, & Nieuwenhuis,
2002; self-regulation: Posner, Rothbart, Sheese, & Tang, 2007). Findings of its role in memory
requires the difficult task of incorporating these different properties together to form a coherent
theory of mPFC functioning. Many of these theories have overlapping features that seem to
converge on the idea of the medial prefrontal cortex providing adaptability. Whether it be
through emotional outcome expectancy (Fellows, 2007) or the maintenance of goal
representations relevant to the current situation (Miller & Cohen, 2001) a common theme is the
top-down control of behaviour given the present context. The link between the role of the mPFC
in memory and its role in decision making does not require far-reaching connections given that
proper decision making requires some knowledge of past experience (or memory) to properly
guide behaviour in the current situation.
The role of the mPFC in memory has often been confined to the long-term maintenance of
memory, or permitting the retrieval of remotely acquired memories given evidence of more
severe retrieval impairments from mPFC disruption at remote versus recent time-points
(Frankland et al., 2004; Maviel et al., 2004; Takehara et al., 2003). However it has also been
proposed that the mPFC is an active participant in all stages of learning and memory providing
contextual meaning and comparing and integrating new events with previously acquired
representations of experience (Euston et al., 2012; Preston & Eichenbaum, 2013). This is
supported by the recent finding that a direct medial prefrontal-hippocampus projection can
trigger the retrieval of a recently formed memory of a dangerous place (Rajasethupathy et al.,
2015). The medial prefrontal cortex is also proposed to play an essential role in the consolidation
of long-term memory throughout neocortical networks and to become an essential node in the
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long-term retrieval of memories that no longer require the hippocampus (Frankland & Bontempi,
2005; Insel & Takehara-Nishiuchi, 2013). Here it is typically suggested that the medial
prefrontal representation is similar to that suggested of cortical memory representations in
general, existing in a more abstract form containing gist-like details of the experience (Euston et
al., 2012; McClelland et al., 1995; Winocur et al., 2010).
Previous investigations of memory representations in the mPFC throughout learning and
consolidation reveal that these neurons gradually acquire activity selective for a memory
association (Hattori et al., 2014; Takehara-Nishiuchi & McNaughton, 2008). Whereas
investigations simply of the features of memory encoded by this region suggest that they are
predominantly encoding task rules or categorical features (Rich & Shapiro, 2009; Wallis et al.,
2001). However no study has looked at the evolution of the encoding of different memory
features across learning and consolidation. Here I reveal prelimbic neurons exhibiting an array of
varying selectivity for task features, the proportions of which are similar from very early on in
learning to several weeks after stable memory expression. This suggests that the content of
medial prefrontal representations in single neurons does not change across learning and
consolidation. This also supports the notion that mPFC computations are important in memory
acquisition. However the presence of neuron responses highly selective for particular task
features suggests that representations in this region are not merely abstractions.
The mPFC is thought to be involved in schema generation and has been proposed to be involved
in maintenance through the updating of existing knowledge structures in the presence of new or
conflicting information (McKenzie & Eichenbaum, 2011; Richards et al., 2014). I propose that
the selectivity within single neurons observed across time in the current study supports this
function allowing the mPFC to accurately identify conflicts between new and old information
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and to decipher the relevance of current experience within the context of information provided
by past experience.
In the Euston et al. (2012) model, mPFC memory functions change across the consolidation
phase of the memory, initially representing contextual meaning and later both representing
meaning and also storing cortical indices of the memory. Here I show that despite conserved
proportions of neurons with varying degrees of feature selectivity across time, the population
activity of prelimbic neurons does appear to change across consolidation relevant time-periods.
Specifically, the population activity became more generalized for task irrelevant features and
more selective for meaningful features. This finding fits better with the notion of a more abstract
memory representation in the mPFC that seems to be captured by looking at the network
computations within this region. Whereas the presence of highly selective neurons remain, in a
task in which the features go unchanged and no new information is available, the global activity
of these neuron populations highlight the meaningful dimensions and become less discriminating
of non-informative features. This is highlighted by the fact that when new information was
eventually added in the form of a change of environmental features, because this information did
not change the underlying representations, this information was represented within the existing
generalized dimension. I propose that had this new information changed the underlying
representation and provided contextual meaning to the association, the prelimbic code would
have rapidly acquired highly selective activity for this feature. This supports the crux of theories
of mPFC function, wherein memories are represented within meaningful contexts to guide
behaviour in the current situation and support adaptability. In my data the prefrontal population
acquires selectivity for meaningful and predictive features but also maintains individual
selectivity for discrete event features. This would permit that given a sudden change in the
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underlying representations, for example if suddenly light but not tone predicted the shock, the
population could rapidly adapt, ramping up its selectivity for the identity of the stimulus.
Both of the proposed functions of the mPFC in memory by the Euston et al. (2012) model are
proposed to depend on interactions with the hippocampus. With hippocampal-prefrontal
interactions initially enabling rapid mPFC associative learning to permit the formation of the
memory (Wise & Murray, 2000), and later through reply mechanisms (Peyrache, Khamassi,
Benchenane, Wiener, & Battaglia, 2009), driving the consolidation of the memory to permit the
long-term storage of cortical indices (Euston et al., 2012). In my experiment, I revealed that
distinct populations of prelimbic neurons modulated by inputs from the lateral entorhinal cortex
show highly selective activity early in learning that becomes more generalized with learning and
consolidation that was not observed in populations modulated by the perirhinal and ventral
hippocampal inputs. This initial selectivity and later generalization may reflect the interactions
proposed, with the entorhinal cortex providing immediate access to rapid hippocampal
computations creating highly selective encoding in prelimbic neurons and more generalized
patterns as the hippocampus is gradually disengaged. However in this case rather than simply
highly selective population activity, I would expect that a large proportion of entorhinal
modulated prelimbic neurons would show highly selective activity early in learning as opposed
to the varying selectivity observed that was comparable to other phase-locked populations.
Therefore selectivity at the single neuron level may also be acquired through other means,
possibly through connectivity with midline-thalamic regions (Vertes, Hoover, Szigeti-Buck, &
Leranth, 2007) or other connected regions of the cingulate (Hattori et al., 2014), whereas these
hippocampal inputs instead may direct these cells activity into functional ensembles.
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Similar to the schematic theory of mPFC memory function, it has also been proposed that a key
feature of mPFC memory storage is that it creates a more efficient index code of the memory
patterns across the neocortex (Frankland & Bontempi, 2005; Takehara-Nishiuchi &
McNaughton, 2008). It has recently been suggested that neurons showing mixed selectivity
(what I called here Non-selective) as a population can be decoded for many of the individual
features of the memory (Rigotti et al., 2013). This type of encoding is proposed to have a
computational advantage over more highly selective responses and may represent higher
dimensional encoding important in cognition. Here these Non-selective cells or cells showing
mixed-selectivity were the largest proportion of cells I observed in the PrL. If Rigotti et al.,
(2013) are correct in their proposition of the importance of these cells, they may serve as the
hallmark of a more efficient index of the memory association.
Together this study provides a neurophysiological account of memory representation within the
prelimbic mPFC and reveals a potential neural signature of episodic memory consolidation in the
brain. I report actual changes in prelimbic encoding of memory features across time and
highlight several cortical interactions that appear to drive these changes. Overall these results
seem to fit the current evolving theories of medial prefrontal memory function and provide
insight into several areas that have remained uncertain.
5.2.2 Future directions
This thesis looked at the computations taking place within the prelimbic mPFC across learning
and consolidation and highlighted several brain regions that may help shape these
representations. However a drawback of this study is that while the results are informative, they
are correlative and are interpreted based on the numerous functional investigations already done
on the role of the mPFC in memory. My findings highlight several properties of prelimbic
121
encoding that require further study to uncover their functional significance. Future studies
combining pathway specific control and electrophysiological recordings would provide
substantial insight into the functional significance of the current findings. For example,
optogenetic methods can be used to target the LEC-mPFC projection or the mPFC-LEC
projection to demonstrate the functional significance of each of these for behaviour and also for
the formation and/or maintenance of mPFC representations and how they differ across learning,
consolidation and remote retrieval. Targeting pathways with methods that allow their direct
control would identify when and how these projections are shaping the state of memory in these
networks. For example during initial learning when hippocampal computations are essential for
memory formation, does the LEC drive mPFC activity and is this essential for the
representations of the memory created within the mPFC population? In contrast, after the
memory has been consolidated, does the directionality reverse, with the mPFC driving LEC?
Further the extent to which the mPFC stores cortical indices can be examined by adapting
methods previously used to reveal the engram like properties of hippocampal cells. Particularly,
mapping the effect of silencing memory activated neurons in the mPFC not only on behaviour,
but also on the memory relevant activity in other cortical structures, and how this differs before
and after consolidation of the memory. Systematically assessing these effects and their
differences across some of the important sub-regions of the mPFC would also be very revealing.
The combination of electrophysiological monitoring and functional interrogation can be very
revealing. The interaction between the mPFC and the other major nodes of the remote memory
network layout several possibilities of their functional significance, questions tailor-made for
such an approach. We are at an exciting time in the science of memory, a time when our
technical toolbox to manipulate and observe neural activity is starting to catch up to our
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understanding of how these circuits and networks mediate our cognitive and mental capacities.
New frontiers are being opened to investigate the neural workings of the brain and they can help
build upon the ingenious groundwork already laid. The field of memory research has taken great
leaps in the last couple decades and there is great optimism for what lies ahead.
1
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