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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
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.
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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 .
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
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.
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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.
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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 ICPs.
 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.
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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.
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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
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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))
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Signature 1 (3/4)
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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.
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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.
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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.
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.
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.
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Signature 3 (2/5)
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 spiketrain 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.
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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).
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.
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Signature 3 (5/5)
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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.
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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.
Timescale (2/2)
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.
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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.
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