1. Ch 3. Cell assemblies and serial computation in neural circuits Information Processing by Neuronal Populations, ed. C Holscher and M Munk. Also published as “Neural signature of cell assembly organization”, Nature Reviews Neuroscience(6), 399-407(2005) Summarized by Seok Ho-Sik

2. © 2008 SNU CSE Biointelligence Lab 2 Cell assembly Contribution:  Not at the neuron level but at the assembly level: introducing an alternative interpretation based on the older concept of cell assembly against the ‘temporal coding’.  Temporal coding: stimuli are represented by precise spike-timing patterns. Phenomenon: spike trains show a temporal structure that is stimulus-dependent and more variable than would be predicted by strict sensory control. Arguing: many observation that have been interpreted as evidence for temporal coding might instead reflect an underlying assembly structure. Limitations: short of explanation (1) on what the inputs for the assemblies would be, (2) on the sequential computation by cell assembly, and (3) on how the cell assembly is formed.

3. © 2008 SNU CSE Biointelligence Lab 3 Question and some facts Whether, and how, the brain might perform something like serial computation unlike ANN. Some background knowledge  Rate code: the only variable that neurons use to convey information is instantaneous firing rate, which is typically characterized by spike rate within a certain ‘encoding time window’.  Temporal code: the exact timing of spike sequence also plays a part in information transmission.  Comparison: the set of all spike sequences is much larger than the set of instantaneous rates, and a neuron that distinguish between sequences could transmit a larger number of possible signals.  It is less clear how a temporal code could be ‘read’ by downstream neurons.  1. Temporal integration code: a neuron’s firing rate at any particular moment reflects the summed firing rates of its presynaptic inputs.  2. Coincide detection mode: a neuron fires action potentials whenever a sufficient number of presynaptic neurons are active precisely synchronously.  Both (1) and (2) can be implemented by leaky integrator (the arrival of sufficient excitatory afferents within a time window leads to output spikes). Hebb’s theory: it conjectured how the dynamics of cortical circuits could subserve the evolution of internal state and consequent performance of behaviours beyond stimulus–response association. ICP (Internal cognitive process): psychological processes that are dependent on internal state.

4. © 2008 SNU CSE Biointelligence Lab 4 The unknowns or the uncertain Limitation in coding framework itself.  It is often not known what a given population is coding for.  The representation of external events is only part of the story, and that the firing pattern of neurons even in primary sensory cortices reflects not just the physical nature of a stimulus, but also internal factors.  Maybe, the primary function of a neural population is to convey information about the supposed stimulus, but there are complex dynamics even in the absence of sensory stimulus.  Ongoing activity that precedes sensory stimulation plays an important part in shaping neural activity during stimulus presentation, which indicates that it might be more accurate to regard sensory stimuli as modulating ongoing neural dynamics, rather than deterministically controlling firing patterns.

5. © 2008 SNU CSE Biointelligence Lab 5 The cell assembly hypothesis (1/2) The key to ICPs lies in the recurrent nature of neural circuits. Repeated co-activation of a group of neurons during behavior will lead to the formation of a cell assembly – an anatomically dispersed set of neurons amongst which excitatory connections have been potentiated.

6. © 2008 SNU CSE Biointelligence Lab 6 The cell assembly hypothesis (2/2) Consequently, the activity of assemblies can become decoupled from external events, and can be initiated by internal factors such as the activity of other assemblies. Phase sequence  A chain of assemblies, each one triggered by the last.  The phase sequence allows for complex computations, which are only partially controlled by external input, and is the proposed substrate of ICPs.  The same assembly might be triggered by either sensory or internal factors. Consequently, a single neuron might participate in both sensory representation and ICP s.  The progression of assemblies in the phase sequence represents successive steps in a serial computation. The fundamental currency of information processing  Cell assembly: the firing of a single assembly not the sequence.  Temporal coding: precise patterns of spike times.

7. © 2008 SNU CSE Biointelligence Lab 7 Signatures of cell assembly organizations Signature 1: spike trains show temporal structure that is not present in the stimulus. Signature 2: spiking is not strictly controlled by sensory input. Signature 3: apparently unpredictable spikes will be coordinated to reveal an assembly organization. Signature 4: patterns of assembly activity should correlate with ongoing ICPs.

8. © 2008 SNU CSE Biointelligence Lab 8 Signature 1 (1/4) If presentation of a stimulus initiates a phase sequence, the spike times of participating neurons will reflect the temporal structure of the phase sequence as well as that of the sensory stimulus. Observations:  Spike times can follow the temporal structure of a stimulus in a wide range of sensory systems. However, spike timing can correlate with stimulus visual qualities other than temporal structure (e.g.: presentation of different visual patterns can lead to responses with different temporal profiles, as well as different spike counts )  interpreted as evidence for temporal coding, whereby spike time patterns, in addition to firing rates, serve to communicate the stimulus to downstream neurons. Alternative interpretation: the presentation of a stimulus initiates a phase sequence, which evolves, in time, through the dynamics of the cortical network. So, spike trains show temporal structure that is not present in the stimulus itself. The sequence of assemblies reflects a chain of internal events that are triggered by the stimulus

9. Signature 1 (2/4) The antennal lobe of insect olfactory system  Different odor stimuli evokes a complex sequence of spike patterns, and pharmacological disruption of these pattern sequences alters firing in downstream structures and impairs fine behavioral discrimination of odors.  Neurons that are immediately postsynaptic to this structure do not appear to detect temporal sequences of afferent spikes but simply fire when sufficient coincident input occurs within a certain time window. In the hippocampus  It is the timing of spikes relative to the ongoing theta oscillation that is correlated with the animal’s location in the environment.  Question: does the phase of spikes with respect to the theta oscillation form a temporal code for the animal’s location? If this were the case, then when location is constant, phase should be either be constant or completely random.  Result: phase fluctuations showed the same relationship to rate fluctuations during periods of increased spatial and non-spatial behaviors (Figure 3-(a), (b)) © 2008 SNU CSE Biointelligence Lab 9

10. 10 Signature 1 (3/4)

11. © 2008 SNU CSE Biointelligence Lab 11 Signature 1 (4/4)  The similarity of phase dynamics in spatial and non-spatial behaviors indicates that theta phase is not an explicit code for space, but is just one manifestation of a more fundamental principle that underlies the processing of both spatial and non-spatial information.  this principle is related to the evolution of assembly sequences during the theta cycle, and can only be properly characterized at the population level. Precisely repeating temporal patterns of spikes can occur with millisecond precision  This would appear to be strong support for the occurrence of phase sequences.  However these have been controversial, (I) after a delay of several seconds, a single spike can be produced by a cell with millisecond accuracy, even though the same neuron may have fired multiple, untimed, spikes in the intervening time. What physicological mechanism could be capable of producing such accurate timing is unclear.  (II) the complex statistical methods necessary to detect such repeating sequences is not overly simplistic and already known to be incorrect.  Sequential activity does indeed occur in neural circuits and that sequences can be triggered by punctuate events that may or may not be correlated with behavior.

12. © 2008 SNU CSE Biointelligence Lab 12 Signature 2 (1/2) Stimulus and ICPs  The phase sequence following a stimulus will depend on internal factors prior to stimulus presentation, as well as the nature of stimulus  spike trains will appear variable, even across repeated presentations of an identical stimulus. Spike train variability: the variability of spike trains is often compared to that of a Poisson process.  In the primary sensory cortex and the thalamus: less variable than a Poisson process.  In the hippocampus: more variable than a Poisson process. Causes of irregularity, just noise?  In vitro, temporally structured current injections result in spike times reliably locked to stimulus transients, indicating that neurons are not fundamentally stochastic devices.  Irregularity of a neuron’s output spike train reflects irregularity of its inputs.  The apparent variability of spike trains reflects incomplete control of the sensory environment  trial-to-trial variability of neurons in the middle temporal area in response to moving-dot animations is reduced if a precisely repeated motion signal is presented.

13. Signature 2 (2/2) Question: could apparent variability results from trial-to-trial variability in the precise trajectories of individual dots, rather than a noisy representation of the mean motion vector?  Even if all sensory stimuli were controlled perfectly, one could not control the animal’s ICPs. If a neuron participates in ICPs, its spike train would therefore still appear unpredictable, however well the sensory environment is controlled. To produce irregular outputs, the input instead needed to show correlated fluctuations, which consisted of sporadic periods of synchronous input lasting for approximately 30ms.  this is precisely the type of input that would be expected if the presynaptic population was organized into assemblies that were synchronized at this timescale.

14. Signature 3 (1/5) If apparently unpredictable spike timing arises from participation of assemblies in ICPs, this will be reflected by synchronous firing of neurons, beyond what is expected from common modulation by external input. Peer prediction  (1) For each neuron, the spike train is first predicted from the external variable that it is presumed to represent.  (2) If the recorded population simply represented this external (1) would be the best prediction.  (3) Alternatively, if neurons are organized into assemblies whose firing is only partially determined by external factors, it should be possible to better predict when a neuron will fire, given the spike times of simultaneously recorded assembly members. © 2008 SNU CSE Biointelligence Lab 14 Experiment: a spatial exploration task  prediction of firing rate from the rat’s trajectory accurately captures the time-dependence of the mean firing rate, which rises and falls as the rat enters and leaves the place field.

15. Signature 3 (2/5) © 2008 SNU CSE Biointelligence Lab 15 Peer prediction analysis of assembly organization in the hippocampus. a | Activity of a ‘target cell’ (black, top), and a population of simultaneously recorded (‘peer’) pyramidal cells (below). Each peer cell is assigned a prediction weight, with activity of positively or negatively weighted cells predicting increased or decreased probability of synchronous target-cell spikes. b | The target cell’s place field and animal’s trajectory (white trace). Scale bar: 10 cm. c | Target-cell firing probability that is predicted from the animal’s position (green), or from position and peer activity (orange). d | Prediction quality is quantified by assessing the fit of the observed spike train against the prediction. e | One second of a simultaneously recorded spike- train data. Spike rasters were arranged vertically by stochastic search to highlight putative assembly memberships (circled). Modified, with permission, from REF. 62 © (2003) Macmillan Magazines Ltd. Stochastic spike times are indeed better predicted by this short-scale structures  the apparent variability might instead reflect an assembly organization that is visible only at the population level.

16. Signature 3 (3/5) Question: could correlated assembly firing arise from simultaneous phase coding for spatial location in multiple cells? Expectation: if this were the case, neuronal synchronization would arise from the dependence of the mean theta phase of each cell on position, but fluctuations in timing around this mean would be random and independent among cells. Validation: the prediction of spike trains from position was refined by incorporating a position-dependent theta modulation. Result: peer prediction further improved on the refined spatial prediction  indicates that neurons show coordinated activity beyond what is predicted by simultaneous phase precession, and that the phase-space correlation might be only one manifestation of a more fundamental mechanism determining exact spike times. Another observation: spike trains are not typically characterized by a single discrete spike cluster per theta cycle  instead, irregular patterns are observed  this irregularity is in fact coordinated across the population, which reflects an organization of neurons into synchronously firing groups (Figure (e) in the previous slide).

17. Signature 3 (4/5) Peer prediction and stimulus-reconstruction paradigm  Peer prediction: test for conditional independence by predicting individual spike trains from a stimulus and by determining whether this prediction can be further improved by predicting from peer activity.  Stimulus-reconstruction: population activity is used to predict the sensory stimulus that is presented to the animal.  Although this “decoding” paradigm can help clarify the relationship of neuronal activity to sensory input, it cannot determine the structure of assembly activity beyond what is caused by common stimulus modulation. © 2008 SNU CSE Biointelligence Lab 17

18. Signature 3 (5/5) © 2008 SNU CSE Biointelligence Lab 18

19. Structure 4 Patterns of assembly activity should correlate with the performance of ICPs  the nature of ICPs should be reflected in the observed pattern of population coordination.  ICPs are by definition unobservable.  It is possible to infer that certain cognitive processes are likely to occur at prescribed moments.  In tasks where animals are required to hold an item in working memory, some neurons show persistent spiking, suggesting their participation in assemblies that fire either continuously or repeatedly during the delay period.  Several studies support the hypothesis that patterns of assembly coordination correlate with internal cognitive state.  Patterns of correlated activity are preserved between waking activity and sleep. © 2008 SNU CSE Biointelligence Lab 19

20. Timescale (1/2) Question: the timescale at which assemblies are coordinated. Analysis: coincident spikes can occur at two characteristic timescale  Sharp correlations: a peak width in the order 1 millisecond.  Broader peaks: measured in tens of milliseconds. Hypothesis:  Sharp synchronization reflects monosynaptic drive between neurons or common monosynaptic input from a third cell.  Broader one is likely to involve larger networks. Result: the optimal window was ~25ms  it closely matches (1) the membrane time constant, (2) presumed excitatory postsynaptic potential (EPSP) width of pyramidal neurons in the hippocampal region (3) the period of the gamma oscillation in hippocampal circuits, and (4) the effective window for synaptic plasticity. Indication: the assembly activity at this timescale might be optimal for propagation and storage of information in local circuits.

21. Timescale (2/2) © 2008 SNU CSE Biointelligence Lab 21 Figure 6 | Timescales of synchronization. a | Cross-correlograms (CCGs) of simultaneously recorded neurons in neocortex. The left CCG shows a sharp peak (width ~2 ms), which reflects putative monosynaptic connections between cells. The right CCG shows a broader peak (width ~20 ms), which is probably the result from more complex network processes. b | Estimation of an assembly synchronization timescale from an optimal peer prediction window. Predictability is plotted against the peer prediction window for an example cell. For short timescales (~1 ms), prediction is poor. For long timescales (~1 s), prediction from peers is no better than that from trajectory alone. Predictability peaks at ~25 ms, which indicates that this is the timescale of synchronization for the assemblies in which this cell participates. c | Histogram of timescales at which peer activity best improved spike time prediction, for all cells expanded in inset). A large mode is seen between 10 and 30 ms, with a median optimal timescale of 23 ms (red line). Part a from P. Bartho and K.D.H., unpublished data.

22. Conclusion Observations that are often interpreted as evidence for temporal coding might instead reflect involvement of cell assemblies in ICPs. A rate-coding framework seems most appropriate  Down stream cells are not expected to have a memory of spikes that occurred further in the past that the EPSP width.  The information conveyed by a population at any moment is fully determined by the assembly that is currently active, without reference to temporal patterns of previous spikes.  The fundamental currency of information processing is the firing of a single assembly not the temporal sequence  each time the assembly fires this would constitute a discrete unit of information processing. © 2008 SNU CSE Biointelligence Lab 22