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Chasing the Cortical Assembly

Damian J. Wallace, PhD, and Jason N. D. Kerr, PhD

Network Imaging Group Max Planck Institute for Biological Cybernetics

Tübingen, Germany

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Chasing the Cortical Assembly

IntroductionWhy is the cortex so difficult to understand? Although we know enormous amounts of detailed information about the neurons that make up the cortex, placing this information back into context of the behaving animal is a serious challenge. In this chapter, we aim to outline some recent technical advances that may light the way toward the chase for the functional ensemble. We summarize the progress that has been made using optical recording approaches with a view to what can be expected in the near future, given the recent technological advances. The modeling and theoretical arguments surrounding neuronal ensembles have been described in great detail previously (Palm, 1982; Braitenberg, 1978; Gerstein et al., 1989; Harris, 2005; Mountcastle, 1997, 2003; Wickens and Miller, 1997), so we will not review them here.

Testing Cortical HypothesesBoth anatomically (Douglas and Martin, 2004, 2007) and electrophysiologically (Spruston, 2008), the properties of individual cells that make up the cortex are very well described, albeit mainly from single-cell recordings or recordings made from cells in isolation. Numerous theories about cortical function date back to the early part of the 20th century (von Economo and Koskinas, 1925; Hebb, 1949; Lorente de No, 1949; Mountcastle, 1978). Of these theories, very few, if any, have been experimentally tested. Why is this? It is at least in part due to the vast scale of the problem. More importantly, it is not clear whether the proposed theories are able to generate hypotheses that are testable using the available methods, or alternatively whether these theories are too general to generate testable hypotheses.

The basic anatomical pathways and connectivity between cortical areas and cortical layers are reasonably well characterized (Braitenberg and Schüz, 1991; Braitenberg et al., 1998). However, it is the firing of action potentials (APs) that defines the functional cortical characteristics, moment by moment, through these pathways. APs propagate throughout an individual neuron’s entire axonal arbor (Cox et al., 2000; Koester and Sakmann, 2000), and probably influence all postsynaptic partners. Individual postsynaptic neurons receive and integrate a vast number of inputs from presynaptic neurons at any moment (Hasenstaub et al., 2005; Waters and Helmchen, 2006). Although individual neurons have considerable computational capacity (Larkum and Nevian, 2008; Losonczy et al., 2008; Jia et al., 2010), neuronal processing of sensory information

involves populations of neurons that are thought to form a percept of a stimulus.

The most influential theory regarding how activity in individual neurons may translate into percept formation, the cell assembly hypothesis, was originally conceived by D. O. Hebb in 1949 (Hebb, 1949). Hebb’s functional cell assembly hypothesis aimed to provide a mechanistic and anatomically relevant explanation of how groups of neurons, acting together, may form a percept. Through their multiple connections, Hebb proposed, neurons form cell assemblies that are collectively activated by sensory input and form a brief closed system after stimulation has ceased. Activity from each cell assembly can propagate and activate additional connected cell assemblies in sequence, which he termed a “phase sequence,” and this was proposed to be the core of neuronal representation of a stimulus-based percept (Hebb, 1949). As individual neurons can leave and join cell assemblies using activity-based synaptic plasticity rules, and therefore can be members of multiple assemblies, the cell assembly can be dispersed throughout a cortical population but linked through potentiated synaptic connections (Gerstein et al., 1989; Gerstner et al., 1993). This is where the challenge of testing the cell-assembly hypothesis lies: locating the neurons involved in forming a cell assembly. Testing this hypothesis has been exceedingly difficult owing to the vast numbers of neurons potentially involved, as neither the number nor locations of ensemble members are known.

Members of functional neuronal ensembles, in any of the proposed theories of cortical function, are dispersed within neuronal populations, and no systematic anatomical organization within these ensembles has yet been described. It is thus generally thought that increasing the numbers of neurons from which simultaneous recordings are made will increase the chances of capturing many members of an ensemble (Grewe and Helmchen, 2009). Given that it is not clear how many members make up a neuronal ensemble or whether the same neurons are involved from one trial to another, we suggest that this is only part of the picture. Making measurements from functional neuronal ensembles during cortical computation is likely to require multiple techniques capable of recording from, locating, and potentially manipulating the activity of individual neurons embedded within large populations. Although a multitude of new technical advances can be applied

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to locating the neuronal assembly (improved transynaptic tracing being an example), the question arises: Are we any closer to recording from, or understanding, the Hebbian cell assembly and its role in sensory coding?

Lighting the Cortical EnsembleMultiphoton imagingOne of the biggest recent advances in the ability to record activity simultaneously from many neurons in vivo (Stosiek et al., 2003), with single-cell and single-AP accuracy (Kerr et al., 2005), has come from multiphoton imaging (Denk et al., 1990; Svoboda et al., 1997; Kerr and Denk, 2008). Its three main advantages are as follows:

•The ability to infer electrical activity from allneurons within a local area on a trial-by-trial basis (Kerr et al., 2007; Sato et al., 2007; Rothschild et al., 2010);

•Knownspatiallocationofalltherecordedneurons(Ohki et al., 2005; Mrsic-Flogel et al., 2007);

•Thecapacitytorecordactivityfromneuronsthatfire at low rates (Kerr et al., 2005; Greenberg et al., 2008); and

•Theabilitytorecordfromthesameneuronsovermany days (Mank et al., 2008; Tian et al., 2009; Andermann et al., 2010).

More recently, several groups have extended multiphoton population imaging to the study of several animal models: the awake head-fixed (Greenberg et al., 2008), head-fixed and behaving (Andermann et al., 2010; Komiyama et al., 2010), head-fixed but mobile (Dombeck et al., 2007, 2009), and freely moving animal (Sawinski et al., 2009). If detection of AP firing is central to detecting neurons involved in a cell assembly, in which individual members could change with each trial, then imaging must be able to accurately resolve activity on a trial-by-trial basis to enable capture of activity related to neuronal assembly.

Inferring action potentialAll activity-based population recordings have used either bolus loading of synthetic fluorescent Ca2+-indicator dyes (Stosiek et al., 2003) (Fig. 1a–b) or infection of cells with genetically encoded Ca2+ indicators (Hasan et al., 2004; Mank et al., 2008; Wallace et al., 2008; Tian et al,. 2009). These indicators typically label populations of hundreds of neurons in areas covering ~500 × 500 × 500 μm (Kerr and Denk, 2008). Although the indicators report

Figure 1. (see opposite page) Imaging neuronal activity. a, Two-photon image of a population of cortical cells labeled with the fluorescent Ca2+ indicator Oregon green BAPTA-1. Astrocytes are counterstained with Sulforhodamine 101 (yellow/red), while neurons appear green. b, Ca2+ transients simultaneously recorded from a population of 13 neurons in vivo. c, Simultaneous Ca2+ imaging and cell-attached electrophysiological recording in vivo showing Ca2+ transients associated with single APs and doublets. Simultaneous electrophysiological recording is essential to accurately calibrate algorithms designed to convert the Ca2+ traces observed in in vivo recordings from populations of neurons into accurate AP raster plots. The simultaneously recorded Ca2+ trace and extracellular electrophysiology are shown to the right, with the model output from the spike-detection algo-rithm (described in Greenberg et al., 2008) corresponding to the Ca2+ trace (green). d–e, Infrared images and Ca2+ transients recorded in neurons in the forelimb representation of the primary motor cortex in a head-fixed mouse on a spherical treadmill. d, Infrared video images showing forepaw movements during typical grooming and running behavior. e, Baseline subtracted Ca2+ traces (black) with significant transients detected using the detection method employed (orange). In Dombeck et al. (2009), two independent analysis methods were used to provide compelling evidence for functional clustering of neurons preferentially active during running or grooming behaviors. f–h, High-speed in vivo two-photon imaging of neuronal activity using an AOD scanning system. f, Overview image of a field of labeled neurons, highlighting a group of 7 cells on an irregular scan path from which data were collected. g, Ca2+-imaging traces from the correspondingly numbered neurons in panel f. h, Graph showing the relationship between cellwise sampling rate and number of cells scanned when the number of acquisition points in each cell is varied from 3 to 9. Using this AOD scanning approach, ~100 cells can be scanned in vivo with a cellwise acquisition rate of ~100 Hz. i–l, data acquired using a miniaturized, head-mounted two-photon microscope. i, Image of a population of layer II/III neurons in the primary visual cortex. The labeling of neurons and astrocytes in this image is the same as that shown in panel a. In this study, the animal was allowed to move freely around a raised C-shaped track around which 3 different visual stimuli were arranged. The layout of the track and stimuli is shown in j. Ca2+ traces simultaneously recorded in 3 neurons and the associated raster plots derived from these data are shown in l and k, respectively. Colored blocks in the background of the raster demarcate times during which the animal’s center of gaze was within one of the stimulus monitors. Color coding cor-responds to the visual-stimulus monitor outline colors shown in panel j. The colors of the individual raster ticks correspond to transients that the spike-detection algorithm used in this study and allocated 1 (black), 2 (red), or 3 (green) APs. The neuron from which the trace labeled as “i” in k and l was robustly activated while the animal’s gaze was on the stimulus marked as 3 in j. a J. Kerr, unpublished observations. b–c, Greenberg et al., 2008, their Fig. 1, adapted with permission. d–e, Dombeck et al., 2009, their Fig. 1, adapted with permission. f–h Grewe et al., 2010, their Fig. 2, adapted with permission. i–l, Sawinski et al., 2009, their Fig. 3E and 4B,D, adapted with permission. Scale bars: a, 50 μm; b, 5 s and 40% ∆F/F0; c, 20% ∆F/F0 and 5 s; e, 30% ∆F/F0 and 15 s; f, 20 μm, 10% ∆F/F0, and 5 s; i, 20 μm; l, 30% ∆F/F0 and 10 s.

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NOTES AP-evoked changes in Ca2+, several research groups have been able to accurately infer single spikes from this signal (Kerr et al., 2005, 2007; Sato et al., 2007; Tian et al., 2009; Rothschild et al., 2010). They have also been able to infer spike numbers during complex bursting activity (Greenberg et al., 2008) using various spike-finding algorithm approaches (Fig. 1c).

Inferring APs from Ca2+ transients with the aim of calculating firing rates or response rates for individual neurons usually requires “ground truth” to be simultaneously established using cell-attached electrical recordings in order to establish both the false-positive and false-negative rates of the search algorithm (but see Dombeck et al., 2009 and Vogelstein et al., 2009 for statistical approaches). For inferring spikes in multiple summated transients (Greenberg et al., 2008; Dombeck et al., 2009), which are generally found in awake animals (Fig. 1d–e), algorithms need to be sufficiently robust to slow drifting baseline calcium levels and variable firing rates of the individual neurons (Greenberg et al., 2008). Accurate inference of APs from Ca2+ transients enables researchers to measure both spontaneous firing rates and stimulus response rates in neurons that have sparse activity or low firing rates—something that is almost impossible for extracellular recording approaches using spike-waveform separation (but see Girman et al., 1999 and Ecker et al., 2010).

Although almost all in vivo two-photon imaging studies have been restricted to the cortical supragranular layers, where neuronal responses and firing rates are lower than for neurons in the deeper granular layers (Girman et al., 1999; de Kock et al., 2007; de Kock and Sakmann, 2009), a recent study by Mittmann et al. (2011) has imaged activity from layer Vb neurons labeled with GCaMP3 (Tian et al., 2009). The question arises as to whether it will still be possible to accurately infer spiking activity from Ca2+ transients in neurons firing at higher rates. Several lines of evidence show that this is possible. High firing rates have been inferred from Ca2+ transients post hoc using a deconvolution method in mitral cells at firing rates of ~100 Hz (Yaksi and Friedrich, 2006). A promising approach that has, until recently, been employed mainly in vitro involves using acoustic-optical devices (AODs) to improved temporal resolution and to optimize scan paths (Iyer et al., 2006; Vucinic and Sejnowski, 2007; Duemani Reddy et al., 2008). A recent study (Grewe et al., 2010) has successfully applied the AOD two-dimensional scanning method in vivo to detect single APs and AP bursts. The group used similar detection

methods as those used with data from conventional galvanic scanning (Fig. 1f–h) but were able to resolve transient peaks evoked from individual spikes firing at ~30 Hz.

Spatial resolution of neuronsSeveral groups have been able to relate this activity back to the spatial location of the neurons. They have taken advantage of spatial resolution that allows them to precisely locate recorded neurons relative to both macrostructures (e.g., cortical layers and somatosensory columnar borders) and microstructures (e.g., surrounding neurons). In these experiments, a strong relationship has emerged on a trial-by-trial basis between neuronal firing–based correlations and distance, when studied on a fine scale, during sensory stimulation (Kerr et al., 2007), and in awake, head-fixed animals (Dombeck et al., 2009; Komiyama et al., 2010). A clear spatial relationship has also emerged for average orientation tuning preferences for cat visual cortex neurons, though not in the rat (Ohki et al., 2005).

Despite these promising applications, this approach still has fundamental limitations. Limitations include the small number of neurons that can be simultaneously scanned (typically, 20–50 neurons using conventional galvanic raster-scanning and requiring single-AP-detection fidelity) and the imaging-depth constraints (Theer and Denk, 2006). Further, because the functional cell assembly most likely will involve all the cortical layers, several attempts have been made at imaging neuronal populations in three dimensions. In vivo, this has been achieved by moving the objective lens with a piezoelectric actuator while using conventional galvanic scanners (Gobel et al., 2007). Although this approach allows researchers to scan many more neurons at a time, it is still limited by the duty-cycle speed and increases the number of potentially sampled neurons only by a factor of ~10 (but see Reddy and Saggau, 2005 and Botcherby et al., 2008 for promising approaches). All these improvements have their advantages and limitations (Grewe and Helmchen, 2009). Also, although current imaging approaches are limited to relatively small populations of neurons in the upper cortical layers, making recordings from substantially larger populations from most cortical layers will likely be achievable within the next few years in the awake behaving rodent.

Ultimately, the probability of finding a functional cell assembly will potentially increase. To achieve this goal, strategies will aim to reduce the number of neurons that have to be sampled from (e.g., to

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NOTESneurons known a priori to be synaptically coupled) and narrow the time window in which the neurons are active (e.g., by carefully designing behavioral experiments).

Recording Awake BehaviorGiven the complexity of the associating spikes with neuronal assemblies, recording in the primary sensory areas provides a possible timing link to the forming of a percept in the awake animal. Because during movement light penetration into tissue is less mechanically damaging than the placement of physical electrodes (but see Fee and Leonardo, 2001), imaging allows recording activity in the awake animal where movements within the image can be successfully corrected offline, with very little data loss (Dombeck et al., 2007; Greenberg et al., 2008; Greenberg and Kerr, 2009). The development of genetically encoded calcium indicators (GECIs) is a further advance that has made imaging neurons in trained animals a more realistic goal (Hasan et al., 2004; Mank et al., 2008; Wallace et al., 2008; Tian et al., 2009). GECIs now allow neuronal activity to be recorded from the same set of neurons for up to several months (Tian et al., 2009; Andermann et al., 2010) without having to open the recording chamber and reapply the Ca2+ indicator.

Several recent studies have begun to make use of these new opportunities to measure activity in ensembles of neurons and how behavioral outcomes are reflected by these ensembles’ activity (Dombeck et al., 2009; Komiyama et al., 2010). The recent development of a spherical treadmill (Styrofoam ball) on which an animal was free to run while head-fixed provides great opportunities for imaging in awake behaving animals (Dombeck et al., 2007). Most recently, this treadmill was used to provide compelling evidence for functional clustering of neurons into ensembles correlated with either running or grooming movements of the contralateral forelimb (Dombeck et al., 2009) (Fig. 1d–e). Another recent study identified two cortical areas involved in licking in mice (Komiyama et al., 2010). The group demonstrated different populations of neurons whose activity reflected different behavioral choices the animals made. They also showed that closely neighboring neurons (within ~150 μm)—particularly those with the same response type—often showed substantial synchronous activity and that these temporal correlations increased as the animals’ behavioral performance improved. Another recent advance has demonstrated population Ca2+ imaging in the visual cortex of awake head-fixed mice performing a visual orientation discrimination task (Andermann et al., 2010). This study found

that head-fixed mice perform the discrimination task under the two-photon microscope (albeit with slightly reduced behavioral performance) and will perform enough trials in a day to allow the construction of full psychometric functions. Using a GECI, the researchers were able to record responses from the same neurons for periods of up to several months. While adaptation of animals to head-fixation has enabled imaging of neuronal ensemble activity using conventional two-photon microscopes, another approach (not without its own complications) is to miniaturize the microscope sufficiently to allow it to be carried by the animal (Flusberg et al., 2008; Sawinski et al., 2009). Using a miniaturized microscope, functional Ca2+ imaging from populations of layer II/III neurons in the visual cortex of freely moving rats was recently demonstrated (Sawinski et al., 2009) (Fig. 1i–l). This study opens another door to investigating neuronal ensemble activity in freely behaving animals. Image stability was suitably high to allow continuous recordings of neuronal activity for prolonged periods (hours) while the animal moved around a raised C-shaped track, around which three differently orientated visual stimuli were arranged. Within the imaged neuronal populations, some neurons were found to be preferentially activated as the animal’s gaze swept across one of the three monitors, consistent with the known orientation preferences observed in rat visual cortex (Girman et al., 1999; Ohki et al., 2005).

Imaging in head-fixed animals retains several substantial advantages and is likely to remain the preeminent imaging method for studying awake behaving animals. However, the recent finding that peak visual responses in head-fixed mice on a treadmill were significantly greater if the stimuli were presented as the animal was running, compared with when it was motionless (Niell and Stryker, 2010), suggests that free movement may have a greater impact on recorded neuronal responses than may otherwise have been anticipated.

ConclusionThe Hebbian cell assembly hypothesis was published more than 60 years ago. Since then, a large amount of detailed knowledge about the neurons that make up the cortex has been gathered. The next challenge is placing this knowledge back into the context of the behaving animal. The recent emergence of techniques that enable optical imaging of activity from large neuronal populations, optical manipulation of activity, and large-scale reconstruction of neuronal circuits offers new opportunities to accurately correlate behavior and activity with circuit anatomy. Although the ability to

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NOTES simultaneously record activity in progressively larger neuronal populations is advantageous, because of the vast number of the potential member neurons in a functional cell assembly, testing the cell assembly hypothesis will most likely require the combination of all these approaches.

AcknowledgmentThis chapter is a vastly redacted version of an article that was originally published as “Chasing the cell assembly” in Current Opinion in Neurobiology 2010;20(3):296–305.

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