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Activity-dependent clustering of co-active synapses during development

Winnubst, J.H.P.P.

2016

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citation for published version (APA)Winnubst, J. H. P. P. (2016). Activity-dependent clustering of co-active synapses during development.

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Chapter 1

General Introduction

Partly adapted from: Johan Winnubst, Christian Lohmann

Frontiers in Molecular Neuroscience, 2012, 5:70

Chapter 1. General Introduction

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Preface

Parents often mention how observant or ‘curious’ new born babies seem to be about

their surroundings. Indeed, even though a newborns eyesight is still limited, they are

already able to track moving objects, perceive depth, imitate finger movement and

even recognize faces (Campos et al., 1970; Field et al., 1984; Nagy et al., 2005). While

these abilities are usually sufficient to impress proud parents, they pale in comparison

to the capabilities of other mammalian offspring. For instance, calves of the blue

wildebeest, who have similar gestation periods as humans, are able to stand steady

on their feet within mere minutes after birth (Estes, 1991). Calves learn to walk and

sprint on the very same day, allowing them to avoid predators come nightfall. How

is it possible that these animals are able to perform such complex sensorimotor

tasks without any prior experience? To answer these questions we must look at the

processes that shape neuronal connectivity in our brains during early development.

These processes do not only involve properly connecting the right anatomical regions

with each other (for instance connecting the eyes with the visual cortex), but can

even involve fine-scale subcellular connectivity (connecting one cell onto a specific

part of another cell). Interestingly, this connection specificity arises not only through

molecular mechanisms but also through internally generated brain activity. Studying

these developmental mechanisms will not only aid our efforts to combat disorders

in which neurodevelopment goes awry (autism, schizophrenia etc.), but can also

increase our understanding of the functioning of the healthy adult brain.

Network connectivity and function

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In order for our brain to integrate and parse the information from our sensory organs, neurons must form precise synaptic connections with each other. I will first discuss this link between connectivity and function in the context of the two brain areas that I will focus on in this thesis: the hippocampus and visual cortex.

Hippocampus

The hippocampus is part of the limbic system and it is involved in long-term memory formation and spatial navigation (Cohen and Eichenbaum, 1995; Moser et al., 2008). All mammals have two hippocampi located on both sides of the brain underneath the cortex surface of the medial temporal lobe (2006). Each hippocampus consists of two parts: the entorhinal cortex (EC) and the Ammonis horn, so called because it resembles a rams horn (Cornu Ammonis, CA), which is subdivided into 4 regions (CA1-4). The entorhinal cortex is the main connectivity hub of the hippocampus and is heavily interconnected with the cortex, thalamus, hypothalamus and several nuclei in the brainstem. Connectivity inside the hippocampus is remarkably unidirectional and is usually considered to propagate along a ‘trisynaptic’ pathway (Figure 1). The main output path of the entorhinal cortex, the perforant path, heavily innervates the Ammonis horn and acts as its main input source. Perforant path connections from the EC mainly innervate the granule cell layer in the dentate gyrus whose axons, called mossy fibbers, connect to the dendrites of CA3 pyramidal neurons. Axons from the CA3 in turn send connections to the CA1 (Schaffer collaterals) and also make reciprocal connections with other CA3 neurons. CA1 neurons then close the loop by innervating the EC through the subiculum.

Due to these well-defined connections, the hippocampus is a favoured model system for studying activity-dependent changes in synaptic strength (Malenka and Bear, 2004). Since the discovery of the synapse it has been postulated that the strengthening of synaptic connections between simultaneously firing cells could act as physiological correlate of memory formation. This type of ‘fire together, wire together’ synaptic strengthening is often referred to as Hebbian plasticity after Donald Hebb who formalized the possible role of such a plasticity rule in learning and behavior (Hebb, 1949). The hippocampus was the first system in which such long-term changes in synaptic strength were observed in response to brief strong stimulation, a phenomenon known as long-term potentiation (LTP; Bliss and Lomo, 1973). Since the hippocampus receives information from many sensory modalities it is thought to facilitate memory formation by creating associative links between


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different patterns of inputs. Understanding the modification of synaptic inputs by cellular activity is therefore crucial in elucidating the physiological correlates of memory.

Another important function of the hippocampal formation is related to its role in spatial navigation. Electrical recordings made in freely moving rodents have shown that a large number of neurons in the hippocampus and entorhinal cortex fire action potentials on specific spatial locations (Moser et al., 2008). Broadly speaking these cells can be divided into four main categories: 1) place cells, which encode for a specific location in space; 2) grid cells, which are active on multiple locations in a grid-like pattern; 3) border cells, that fire on close proximity to a physical border and 4) head-direction cells that encode for head orientation in space. Together these cells can create a representation of the physical space of the animals surroundings in a ‘cognitive map’ (O’Keefe and Nadel, 1978). Interestingly, recent micro-electrode recordings in rat pups that explore their surroundings for the first time have found near adult-like grid and place cells firing in the hippocampus (Wills et al., 2010). Since these animals had no prior experience with spatial navigation the connection specificity required for this sensory processing must have already been established during early neuronal development.

Visual Cortex

In mammals visual information from the eyes travels through the lateral geniculate nucleus (LGN) in the thalamus to the most caudal part of the cortex (Maunsell

DG

CA3

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Figure 1. Anatomical structure of the hippocampus.A, Basic anatomy of Ammon’s Horn in the hippocampus showing the major pathways and principal neurons. Original by Ramon y Cajal. B, Schematic structure of the hippocampus and the ‘trisynaptic’ pathway. EC = entorhinal cortex, DG = Dentate gyrus, MF = Mossy fibers, SC = Schaffer collaterals.


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and Newsome, 1987). Here the visual information first arrives in the primary visual cortex (V1) where primary features of the visual scene are processed (lines, orientation etc.; Hübener, 2003). As the information is sent along to more anterior parts of the cortex (V2-V6) the features that are processed become increasingly complex and abstract (e.g. shapes, faces, motion). Anatomically V1 consists of 6 distinct layers, which are numbered from top to bottom (L1-6), and differ in their cell type content and input/output sources (Métin et al., 1988). The organization between these layers is best described in what is known as the ‘canonical circuit’ (Figure 2). First, retinal inputs from the LGN arrive in layer 4, where the information is passed along to excitatory neurons in layer 2 and 3 (L2/3). These L2/3 neurons further receive modulatory inputs from other cortical areas on their dendritic branches that extend into layer 1. Axons from L2/3 heavily innervate other cortical regions and neurons in layer 5 and 6 which have outputs back to the thalamus and other subcortical regions.

Historically V1 has been one of the most studied and best understood areas of the brain (Alonso, 2009; Hubel, 1959). This is in large part due to the clear functional organization in V1, as many cells only respond to visual stimuli within a specific region of the visual scene (Hubel, 1959). These ‘receptive fields’ are caused by selective innervation from connections arising from specific parts of the retina. Interestingly, spatially adjacent cells in V1 often share comparatively similar receptive fields that overlap in space (Hübener, 2003). Together these smaller regions of V1 cooperate to form a representation of the visual field in a so called retinotopic map. Other non-cortical structures, such as the LGN and brainstem show a similar retinotopic organization, indicating that outputs from the retina are highly specific and preserve their spatial organization in higher brain areas.

A B

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Figure 2. Organization of the striate visual cor-tex.A, Layered anatomical organization of the visual cortex. Left) groups of cells are shown in their respective layers. Right) axonal fiber pathways. Original by Ramon y Cajal. B, Canonical circuit diagram showing the information flow in the neocortex. Adapted with permission from (Constantinople and Bruno, 2013).


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Next to being retinotopically selective, many neurons preferentially fire in response to stimuli with specific orientations and spatial frequency. These neurons are called ‘simple cells’ and are mainly found in layer 4 of the visual cortex (Hubel and Wiesel, 1962). Cells in L2/3 often respond to a wider variety of orientations and phases (‘complex cells’) and emerge through selective innervation by simple cells in layer 4. Recently, recordings made in V1 of neonatal mice pups showed that even on the first day that the animals open their eyes (postnatal day (P) 14), L2/3 pyramidal neurons already possess receptive fields and orientation selectivity (Ko et al., 2013). The emergence of this functional specificity was found to be independent of sensory activity as it could also be observed in dark-reared animals (Ko et al., 2014). These findings suggest that highly specific synaptic connectivity can arise during early development in a way that prepares the network for future sensory processing.

Spontaneous activity during development

The previous descriptions of the hippocampus and visual cortex show that neurons need to form highly specific connections with each other in order to form a functional network. This connection specificity arises during brain development through successive organizational processes. First, the axon from a presynaptic cell is guided to its target location through the presence of both attracting and repellent molecular cues in its surrounding (Lowery and Van, 2009; Maness and Schachner, 2007). Once the axon has reached its destination it must then make connections with the right cells from a larger pool of possible synaptic partners. In part, this specificity is achieved using different variations of the so-called lock-and-key principle, whereby matching membrane-bound recognition proteins on both the pre- and postsynaptic side allows for the restricted formation of connections between appropriate cell types. Indeed, such ‘chemoaffinity’ mechanisms have been found in various developing systems (Shishido et al., 1998; Yamagata and Sanes, 2008). However, the fine-tuning of synaptic connectivity has been found not to be solely dependent on molecular factors but is later also shaped by synaptic activity itself (Cline, 2003; Goodman and Shatz, 1993; Yamagata and Sanes, 2008).

Interestingly, many developing systems, before they receive sensory information, internally generate spontaneous network activity (Ben Ari, 2001; Hua and Smith, 2004; Huberman et al., 2008; Katz and Shatz, 1996). Spontaneous activity can usually be observed as repetitive network events where large proportions of the network become activated at the same time (Ben-Ari et al., 1989; Galli and Maffei, 1988; Garaschuk et al., 1998; O’Donovan et al., 1994; Yuste et al., 1992). The initial burst of activity quickly travels through the network in a wave-like fashion as neighboring

Spontaneous activity during development

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cells become activated. The resulting synchronicity of neuronal firing was recognized early on as an important feature for activity-dependent refinement of synaptic connectivity.

The best studied form of activity-dependent refinement by spontaneous activity takes place in the visual system. Even before animals open their eyes, waves of spontaneous activity travel across the vertebrate retina and spread from cell to cell (Galli and Maffei, 1988). Spatial relationships are hereby maintained in the axonal firing patterns of neighboring retinal cells, which project to higher order brain structures and thereby aid retinotopic map formation (Stellwagen and Shatz, 2002; Triplett et al., 2009). The information that is present in spontaneous activity is therefore thought to have an instructive role in shaping synaptic connectivity in the visual system through Hebbian plasticity mechanisms. Indeed studies that affected not the presence of spontaneous activity but the patterns within the activity have shown such instructive influences (Stellwagen and Shatz, 2002; Weliky and Katz, 1997; Xu et al., 2011).

In the hippocampus the earliest forms of synaptic activity are also spontaneously

A B

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Figure 3. Spontaneous activity during brain development.A, Example of calcium imaging recording in the visual cortex in vivo. B, Example traces of changes in calcium fluorescence over time from cells labelled in A. Coloured markers show clear distinction between low and high participation events. Adapted from (Siegel et al., 2012). C, Example of spontaneous activity recorded in vitro from hippocampal slice cultures. D, Activity traces of neurons labelled in C.


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generated and are known as giant depolarizing potentials (GDPs, P0-10, Figure 3C-D). These events are mainly generated in CA3 and are heavily dependent on the influence of GABAergic signaling in the network. Due to the higher intercellular concentration of chloride in developing neurons GABA signaling actually leads to excitation of the cell instead of the inhibitory action seen in mature neurons (Ben-Ari, 2014). Interestingly, the functional significance of GDPs remain largely unclear especially since sensory information is not yet present in the network at the time they predominate.

While the tuning of neuronal connectivity by spontaneous activity is thought to be facilitated through Hebbian plasticity (Butts et al., 2007; Kasyanov et al., 2004; Kirkby et al., 2013), the precise plasticity mechanisms remain largely unknown. Furthermore, most studies have investigated the effects of spontaneous activity on the network level and rarely examined plasticity at the level of individual synapses.

Synaptic clustering

The connection specificity considered so far has dealt with the formation of connections between specific brain structures or between different cell types. However, previous studies have predicted that synaptic organization could extend far beyond simply connecting the right axon with the correct cell, but might entail a more precise subcellular organization. The prediction stems from both modeling and experimental studies in which individual segments of dendrite are thought to function as independent computational subunits (Branco and Hausser, 2010; Larkum and Nevian, 2008; Losonczy and Magee, 2006; Polsky et al., 2004). Interestingly, local plasticity mechanisms have recently been described that could lead to the activity-dependent establishment of such a clustered organization. The following section will give an overview of the research behind these new insights.

The dendritic compartmentalization model

The dendritic tree receives the bulk of synaptic inputs and plays an important role in the integration of incoming signals. It is therefore not surprising that the way the dendrite processes synaptic activity to influence somatic firing has been the topic of much research and debate. In early views the dendrite was seen as a linear integrator, summing the received inputs independently of their position on the dendritic tree as they are transmitted towards the soma (Cash and Yuste, 1999; Urban and Barrionuevo, 1998). When the linear sum of these inputs reaches a certain threshold value at the soma a non-linear processing step takes place as an action potential is generated

Synaptic clustering

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in an all-or-none fashion (Figure 4). In this model, the impact of a single synapse on somatic firing is low since it is merely one input amongst many available ones. Consequently, the information and memory of a single-cell is stored in the changing patterns of synaptic weights spanning the entire cell. It is clear that for this ‘synaptic democracy’ to work the dendrite must propagate and integrate the synaptic signals in a linear and neutral way (Yuste, 2011). Interestingly, while the described model is the standard model by which many neuroscientists consider synaptic integration, several properties of the dendritic tree have been discovered that seem to question its role as a linear integrator. Specifically, it has been known for some time that dendrites contain ionic conductances capable of generating active dendritic events in response to local synaptic activity (Hausser et al., 2000; Nevian et al., 2007; Schiller et al., 1997). These regenerative events are possible due to the presence of voltage gated channels, consisting of voltage-gated calcium channels; sodium channels and N-methyl-D-aspartate (NMDA) receptor channels, spread along the entire dendrite. In particular, dendritic NMDA receptor channels can be activated by the synchronous activation of spatially clustered synapses, due to the voltage-sensitive release of the Mg2+ block along a 10-20 µm stretch of dendrite (Losonczy and Magee, 2006; Nevian et al., 2007). The charge generated by such a regenerative ‘NMDA spike’ has been shown to be much larger than the linear sum of the synapses involved. Furthermore, the extended time course of NMDA activation causes the charge to be more effectively passed along towards the soma. Even though one NMDA spike might not be sufficient to trigger an action potential, the influence of these spatially clustered synapses on somatic firing is hereby significantly increased.

The described findings show that, much like the non-linear process of action potential generation at the soma, the synchronous activation of neighboring synapses on a sub-branch of the dendrite can lead to their non-linear summation. This allows for the implementation of a spatio-temporal coding scheme, resulting in a greater specificity in spiking responses and increased computational capabilities. A new model of synaptic integration has therefore been proposed in which the dendritic tree is thought to consist of local compartments, each functioning as an individual computational subunit (Figure 4; Govindarajan et al., 2006; Losonczy et al., 2008; Poirazi and Mel, 2001). In this view the extensive arborization of the dendritic tree becomes a feature, not an obstacle, of the synaptic integration process (Hausser and Mel, 2003). Modeling studies have shown that neurons utilizing such a compartmentalization model can perform mathematical transformations of synaptic inputs that would normally require multiple neurons connected in a network to be achieved (Poirazi and Mel, 2001; Wu and Mel, 2009).


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Plasticity mechanisms favoring synaptic clustering

While the dendritic compartmentalization model increases the computational capabilities of an individual neuron, it places additional demands on the synaptic organization that is established during development. Namely, it requires synapses with a synchronized activity pattern to be clustered close together on the dendrite. Interestingly, several local plasticity mechanisms have been described which could aid the formation of such a synaptic organization through activity dependent processes.

+

Point Neuron Model Compartmentalization Model

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Figure 4. Models of dendritic integration. A pyramidal neuron is depicted with synaptic inputs, represented as colored circles, making distributed contacts along its dendrite. The process of dendritic integration according to the “synaptic democracy” and the “compartmentalization” model are shown. Time points of synaptic input events are represented by the colored bars on top. The blue traces underneath show excitatory postsynaptic potentials as one would record in current clamp. When synaptic inputs reach a certain threshold value (dashed line) an action potential is generated (shown in red) in a non-linear fashion. In the case of the synaptic democracy model, integration is independent of the location of the synaptic inputs. However, when two neighboring synapses are active at the same time in the compartmentalization model (time points highlighted in grey) an additional non-linear step takes place as these inputs are super linearly summated giving them a larger influence on somatic firing.

Plasticity mechanisms favoring synaptic clustering

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Synaptic tagging and capture

Changes in synaptic weight have long been seen as the mechanism through which memory engrams are stored. However, not all these changes in synaptic weight are equally stable and can have different life-times. In the case of LTP, it is possible to distinguish both an immediate,- early phase of potentiation (E-LTP, lasting less than 3 hours) and a long-term,- late phase (L-LTP; Davis and Squire, 1984; Frey and Morris, 1997; Frey et al., 1988; Krug et al., 1984). These can be dissociated from each other since protein synthesis is required for the induction of L-LTP while the expression of E-LTP is protein-synthesis independent (Frey et al., 1988; Krug et al., 1984). Interestingly, a classic study by Frey and Morris found that cooperation can take place between synapses in these different states of potentiation. Specifically, field stimulation experiments in the Schaffer collateral – CA1 pathway showed that L-LTP inducing stimulation in one set of synapses before the application of anisomycin (a protein synthesis inhibitor) could rescue the expression of L-LTP in a later stimulated set of synapses. The authors proposed that the induction of LTP leads to the creation of a protein-synthesis independent synaptic ‘tag’ which can appropriate, or ‘capture’, the relevant plasticity related proteins (PrPs) needed for the induction of L-LTP (Frey and Morris, 1997).

Assuming PrPs can spread from the location of the synapse whose activity lead to their generation it could be hypothesized that this synaptic tagging and capture plasticity (STC) preferentially occurs at clusters of neighboring synapses on the dendrite. A study by Govindarajan et. al. aimed to investigate this possibility by looking at the spatial and temporal characteristics of STC at the level of individual spines. In order to induce L-LTP at a single spine the authors combined high-frequency glutamate uncaging with bath application of forskolin (GLU+FSK; Govindarajan et al., 2011). This protocol resulted in significant L-LTP expression at the stimulated spine. Conversely, glutamate uncaging in the absence of forskolin (GLU) resulted in shorter lived E-LTP expression. The authors found, in accordance with the STC model, that GLU+FSK stimulation of one spine followed by GLU stimulation of a second spine lead to L-LTP expression in both spines (Govindarajan et al., 2011). In addition, the efficiency of STC was found to be inversely proportional to the distance between the 2 stimulated spines and was almost completely undetectable at 70 µm.

These findings show that STC occurs more readily at neighboring synapses, making the dendritic branch the preferred site for cooperativity and association between synapses. Furthermore, this cooperativity acts on time scales that are much longer than the time window in which classical plasticity mechanisms take place (hours vs.


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ms).

Metaplasticity through local synaptic activity

The induction of long-term plasticity requires the activation of various signaling cascades in order to stimulate the necessary protein synthesis. Interestingly, synaptic stimuli leading to L-LTP have been shown to activate specific signaling kinases namely mitogen-activated protein kinase (MAPK) and mammalian target of rapamycin (mTOR; Kelleher et al., 2004a, 2004b). Since MAPK and mTOR remain active for several minutes it has been suggest that their activation by plasticity inducing stimulation could lower the threshold for plasticity in neighboring synaptic activity (Govindarajan et al., 2006; Wu et al., 2001). To investigate this possibility one study by Harvey et. al. looked at the spike-timing-dependent potentiated (STDP) of neighboring spines in hippocampal pyramidal neurons (Harvey and Svoboda, 2007). Here, STDP was induced in single spines using two-photon glutamate uncaging followed by action potential initiation. Similarly to classical experiments of STDP, the magnitude of plasticity was found to decrease as the time between glutamate uncaging and action potential initiation increased. Specifically, an increase in the excitatory postsynaptic current and spine volume could not be observed with a spike time window larger than 5 milliseconds. However, it was found that previous induction of LTP in one spine broadened the spike time window of STDP in neighboring spines (up to 35 ms.). Additionally, no change in STDP was observed when the time between stimulation of the two spines increased up to 10 minutes or when their relative distance exceeded 10 µm. The authors called this form of local plasticity cross talk, in order to distinguish it from STC plasticity mechanisms.

One of the early activators of the MAPK signaling pathway is the guanosine triphosphatase (GTPase) RAS. This GTPase becomes activated by NMDAR-induced calcium influx and is involved in the induction of LTP (Zhu et al., 2002). In order to uncover if activated RAS could spread between neighboring spines, and possibly facilitate crosstalk, a later study by Harvey et. al. used a fluorescence resonance energy transfer (FRET) based indicator of RAS activation (Harvey et al., 2008). Using this method a robust increase of RAS activity was observed after LTP induction in stimulated spines that peaked within 1 minute and returned to baseline after 15 minutes. Once activated, RAS spread over several micrometers in both directions and subsequently invaded neighboring spines. Both the spatial and temporal activation profile of RAS closely matched those observed in the crosstalk experiments, indicating that RAS activation could be involved in its expression. This was confirmed in a subsequent experiment where crosstalk was prevented by the local application of a

Thesis outline

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pharmacological blocker for MAPK and extracellular signal-regulated kinase (ERK; Harvey et al., 2008). These findings show that Ca2+-activated signaling machinery can regulate the threshold for plasticity induction in local stretches of dendrite. In this way the occurrence of strong LTP-inducing activity at one synapse can lower the threshold for potentiation at neighboring synapses, thereby aiding the storage of memory engrams in clusters of synapses on the dendrite.

Thesis outline

In this thesis I will investigate how spontaneous activity shapes synaptic connectivity during early development. The focus here will be specifically on the role of spontaneous activity in fine-scale synaptic clustering. Such an approach has only recently become possible due to the advances in calcium imaging techniques which allow us to follow the activity of a large population of synapses over time. Furthermore, I will attempt to overcome some of the shortcomings of previous studies which relied on blocking spontaneous activity during development in order to study its effect on synaptic connectivity. These manipulations have made it difficult to make causal statement about the instructive role of spontaneous activity since neuronal activity also drives genetic changes during neuronal development. An overarching strategy that I will therefore employ is to start out by studying the spontaneous activity patterns that occur naturally in the intact system. These observations then inform the design of further experiments to test the correlational relationships that we observe. Throughout the following chapters I will return to the important link between connectivity and neuronal processing specifically in regards to synaptic clustering. In this way I hope to show that studying activity-dependent neuronal development can aid our understanding of neuronal processing and plasticity in adulthood. Here, I aimed to answer the following research questions:

• Can spontaneous synaptic activity be imaged on the dendrites of individual pyramidal neurons (Chapter 2)?

• Is there an underlying structure to the spatiotemporal activity pattern of spontaneous synaptic inputs (Chapter 3)?

• What mechanism leads to the clustering of co-active synaptic inputs during spontaneous activity (Chapter 4)?

In chapter 2 I will give a detailed description of the imaging techniques that we used to perform the experiments in this thesis. By combining calcium imaging


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with whole-cell electrophysiology we could distinguish synaptic calcium signals (e.g. calcium influx through activated NMDA channels) from non-synaptic sources (calcium release from internal stores). We can therefore not only see when synapses are active (electrophysiology) but also where these synapses are located on the dendrite (calcium imaging). In this way we can map the functional synaptic sites on the dendritic tree (the “synaptome”) and follow their activity over time.

Chapter 3 uses the method described above to investigate the patterns of synaptic activity that occur during spontaneous network bursts in CA3 pyramidal neurons of the hippocampus. We found that while the identity of the synapses that are activated vary from burst to burst, the resulting patterns are not completely random. Instead, synapses that are located close together on the dendrite tend to be more often active within the same burst then synapses that are farther apart. Furthermore, this synaptic organization was dependent on spontaneous activity and NMDA receptor activation during development. We therefore find that spontaneous activity can connect neurons even with subcellular precision.

Chapter 4 investigates the plasticity mechanism responsible for the synaptic clustering organization that we observed in the previous chapter. We start by following the activity of a large number of synapses on layer 2/3 neurons in the visual cortex of live mice. Strikingly, we find that synapses that are not often co-active with neighboring synapses on the dendrite get suppressed in their activity on the subsequent part of the recording. We performed similar experiments in the hippocampus in vitro and found that this is a general local plasticity mechanism driven by spontaneous activity. By utilizing a closed-loop stimulation experiment, where we manipulate the co-activity rate of individual synapses, we show that this is due to a decrease in the synaptic transmission efficiency of ‘out-of-sync’ synapses. Spontaneous activity therefore drives an ‘out-of-sync, lose-your-link’ local plasticity mechanism in a way that facilitates synaptic clustering

Chapter 5 consists of a general discussion of the presented research. Here I will focus in particular on recent research concerning non-linear dendritic integration in functional processing in vivo. Synaptic clustering is likely a requirement for this form of dendritic processing and therefore sensory processing in the adult animal. Finally, I will discuss possibilities for future research that should help to clarify the link between these two important topics.

References

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