Metastable Dynamics and Learning in Balanced Cortical Networks

Metastable Dynamics and Learning in Balanced
Cortical Networks
Praveen Venkatesh, Majid Mahzoon
1 Balanced Cortical Networks
Consider a balanced cortical network: a model of a bunch of neurons, e.g., 4000 excitatory neurons and 1000 inhibitory neurons, which are wired up without any structure, i.e.,
like an Erdos-Renyi network (with connection probability of 0.2). These neurons are in
a balanced state, i.e., there is a lot of recurrence in the network and a lot of soft-coupling
between neurons; there is lots of excitation and lots of inhabitation, but both of these
things cancel each other out because they are of opposite polarities. As a result, even
though there is lots of excitation and lots of inhabitation, the system has a zero-mean
input and it is really driven by fluctuations. Thus, the spike trains happen very radically
in time and this is all self-generated in this network. There is no true randomness in
this network. From a dynamical systems point of view, this situation is effectively chaos
and this is a high-dimensional chaotic system. Chaos at these system-size levels is not
distinguishable from noise.
“We studied a simple network of NE excitatory and NI , inhibitory neurons. An
additional NO neurons from outside the network provide external excitatory inputs to
the network neurons. On average, K excitatory, K inhibitory, and K external neurons
project to each neuron in the network. An important aspect of the architecture is the
random, sparse connectivity. Although the average number of projections, K, is large,
it is still much smaller than the total number of neurons in the subpopulations NE and
NI . Also important is the
√ assumption that the individual connections are relatively
strong, namely, that only K excitatory inputs√are needed to cross the firing threshold.
Because the threshold is only of the order of K synaptic inputs, the total synaptic
input to a cell will be overwhelmingly depolarizing or hyperpolarizing unless the activity
of the excitatory and inhibitory populations dynamically adjust themselves so that the
large total inhibitory input nearly cancels the large excitatory one. This balance between
excitation and inhibition generates a net synaptic input whose mean and fluctuations are
both on the order of the threshold. The precise timing of the crossing of the threshold
is determined by the fluctuations of the synaptic input, yielding a strongly irregular
pattern of activity. This disorder is a signature of deterministic chaos. Its presence does
not require external sources of noise. The resulting state is characterized by strongly
chaotic dynamics, even when the external inputs to the network are constant in time.
Such a network exhibits a linear response, despite the highly nonlinear dynamics of single
neurons, and reacts to changing external stimuli on time scales much smaller than the
integration time constant of a single neuron.” [1]
1.1 Spike Train Statistics
Why do people study these balanced networks? Balanced networks have been popular
to study because they produce variability that is consistent with cortex. As said above,
this network has chaotic internal dynamics. We can form the raster plot of the network:
assuming that the x-axis represents time and the y-axis represents neuron index, if
we look at the spike train from any one of the neurons, any time the neuron crosses
a threshold, i.e., fires, we put a little dot in the appropriate coordinates and that is a
spike. We can immediately see kind of salt-and-pepper behavior. The individual neurons
are quite variable and they are relatively asynchronous from one another. If we look at
the distribution of the firing rates in the network, we see that it has a heavy-tailed
distribution: most neurons do not fire very much and some neurons fire a little bit more.
If we look at their Fano factor which is the ratio of spike count variance to the spike
count mean, it is quite high. A Poisson process would have a Fano factor of one and in
our model, it is a little less than one.
“The balanced state is characterized also by a broad distribution of (time-averaged)
firing rates across the network. This distribution is the outcome of the different synaptic
projections on each neuron as well as their different threshold levels. A prominent
feature is the skewness of the distribution at low mean rates. Because in our model the
temporal fluctuations are generated by the network activity itself, they are relatively
small when the mean network activity is low. Hence, only those neurons that have
relatively low thresholds fire vigorously; the rest show activity levels much lower than
the mean rate. This feature agrees well with experimentally obtained histograms of
firing rates of neurons in the monkey prefrontal cortex.” [1] See Figure 1.
The internally-generated variability produces these kinds of behaviors.
Figure 1: (Left) The distribution of the rates of neurons in the excitatory population
for different population-averaged rates. The rate distribution is shown in terms of the
local rates divided by the mean rate. (Right) The distribution of firing rates of neurons
in the right prefrontal cortex of a monkey attending to a variety of stimuli (light source
and sound) and executing simple reaching movements. The rates were averaged over
the duration of events (stimuli or movements) that showed a significant response. The
average rate was 15.8 Hz. Most cells fire at a lower rate, whereas a small fraction of the
cells fire at much higher rates. (Figure 3 in [1])
Firing Rate Distribution
If we look at cortex, we see that the lognormal distribution of firing rates matches what
people see in an unanesthetized auditory cortex. This is work from Anthony Zador’s lab
where they have measured spontaneous activity and have seen that “the distribution of
spontaneous firing rates across the population was remarkably well fit with a lognormal
distribution - that is, the logarithm of the firing rates was well fit with a Gaussian
distribution. Because the lognormal distribution has a heavy tail, most spikes were
generated by just a few neurons.” [2] See Figure 2.
Balanced networks produce this.
Figure 2: The distribution of firing rates follows a lognormal distribution: Firing rates
of most neurons were low and followed a lognormal distribution. (Left) Frequency histogram of non-zero spontaneous firing rates. The filled arrow shows the position of the
median spontaneous firing rate, and the open arrow shows the position of the mean
spontaneous firing rate. (Right) The distribution of spontaneous firing rates (dots) was
fit with a lognormal distribution (gray line), the mean and variance of which were given
by the mean and variance of the original firing rate distribution. The lognormal distribution appears as a normal distribution on a (semi-) logarithmic scale. The error bars
show 95% confidence intervals determined by bootstrapping. (Figure 3B,C in [2])
Spike Train Variability
We can also ask how variable our network is. “We have measured the responses of
neurons in area MT of the alert monkey while we varied the strength and direction of
the motion signal in such displays [i.e., dynamic random-dot stimuli]” [3]. This is work
done in Anthony Movshon’s lab where they have looked at the relationship between the
mean spike count and the variance of the spike count and have seen that they are kind of
huddled around the unit line: as the mean increases, the variance increases proportionally
or more precisely, equal to it which will yield a Fano factor of one. “We also explored
the relationship between response magnitude and response variance for these cells and
found, in general agreement with other investigators, that this relationship conforms to
a power law [V ariance = k(mean)b ] with an exponent slightly greater than 1. We used
a maximum-likelihood technique that takes account of the variability of the estimates of
both mean and variance to fit this relationship to our data.” [3] See Figure 3.
Balanced networks produce this as well.
Figure 3: Relationship between mean response level and response variance. The dashed
line represents the average slope of the relationship estimated using maximum-likelihood
fitting to individual cells’ data. Therefore, balanced networks without too many assumptions, just that excitation and inhibition are large but balanced so that they cancel
(meaning that fluctuations are the only things left), give us responses that are like cortex.
(Figure 4 in [3])
Asynchronous Dynamics
Recently, Alfonso Renart and Jaime de la Rocha showed that in these balanced networks
we get asynchronous behavior. If we look at the pairwise correlation between any pair of
neurons in our network, it looks roughly asynchronous in the raster plot and it indeed is
asynchronous. Previous to this work, theorists used to think that the asynchrony came
from the sparseness condition that the likelihood of any two neurons wire together was
; so, the likelihood of joint connections or common input to a pair of neurons is 2
which is negligible. Alfonso and Jaime pushed this further and said that we can get this
asynchronous activity even for heavily-coupled networks: their connection probability
was 0.2 which is not negligible and they got asynchronous behavior.
“To investigate whether such decorrelation can arise spontaneously from the dynamics of a recurrent network, we analytically characterized the behavior of correlations in a
simple recurrent circuit of binary neurons. The network consists of two populations (of
size N ) of E and I neurons connected randomly, both receiving excitatory projections
from an external (X) population of N cells. The network has two key properties: first,
the connectivity is dense so that the connection probability p is fixed independently of
the network size (e.g., p = 0.2). Second, the synaptic couplings are strong such that
only a small fraction of a cell’s excitatory inputs is enough to evoke firing; in the model,
although the average number of inputs is proportional
√ to N , the number of excitatory inputs needed to induce firing is only proportional to N . Our analysis showed that, even
in the presence of shared input, the network settles into a stationary state in which the
population-averaged firing correlation is very weak, if inhibition is sufficiently strong and
fast. In fact, in networks of different sizes, correlation decreases inversely proportional
to N , a signature of asynchronous networks.” [4]
In another work from Andreas Tolias’s group, they were looking at the very careful
recordings of the visual system of primates. They actually made a big deal about the
spike sorting algorithms to really make sure that they were looking at the multiunit
activity. Noise correlations in the visual systems of primates seem to be asynchronous.
“Correlated response fluctuations among simultaneously recorded neurons have been
observed in a number of cortical areas (referred to as noise correlations). We developed
chronically implanted multi-tetrode arrays offering unprecedented recording quality to
re-examine this question in the primary visual cortex of awake macaques. We found
that even nearby neurons with similar orientation tuning show virtually no correlated
variability.”[5] See Figure 4.
The balanced networks capture this asynchronous activity in cortex as well, even though
there is a lot of wiring in the system.
Figure 4: Distribution of noise correlation. (Figure 3B in [5])
Therefore, balanced networks capture many key features of cortical dynamics,
but there is still stuff they do not capture.
1.2 Super-Poisson Variability on Long Time Scales
Anthony Movshon’s group in the 90’s showed that neurons were Poisson. They looked
at the same data recently and they said they are not as Poisson as we thought: they
are super-Poisson. What they did is that they looked at lateral geniculate nucleus
(LNG), primary visual cortex (V1), secondary visual cortex (V2) and the area of the
brain involved in sensing motions (MT) and they saw that when the number of spikes
is small, things look relatively Poisson, but if we let the windows over which we observe
the system be really large so that the average number of events in that window will grow
large, we start to deviate from Poisson. As we look at tens or hundreds of spikes in a
window, the variability is sometimes five, six or seven times that for a Poisson process.
See Figure 5.
Therefore, things are super-Poisson which is not captured by balanced networks.
Figure 5: Comparison of neural response variability for cells in different visual areas.
Variance-to-mean relation for 63 LGN cells (orange), 396 V1 cells (dark green), 189 V2
cells (blue) and 137 MT cells (violet). Each data point illustrates the mean and variance
of the spike count in a 1,000-ms window of one cell for one stimulus condition. (Figure
2a in [6])
Spontaneous and Evoked Dynamics: Doubly Stochastic Process
This super-Poisson activity has also been prevalent in motor cortex where Mark Churchland did some nice work. He looked at variability before and during a task. Suppose
that an animal’s task is to reach to a certain area (target) and it is going to do this
over and over again. He looked at a single neuron spike train over many trials of this
identical task where the target appears midway through the experiment.
“We measured the across-trial variability of neural responses in dorsal premotor
cortex of three monkeys performing a delayed-reach task. Such variability was initially
high, but declined after target onset, and was maintained at a rough plateau during the
delay. An additional decline was observed after the go cue. Between target onset and
movement onset, variability declined by an average of 34%. This decline in variability
was observed even when mean firing rate changed little. We hypothesize that this effect
is related to the progress of motor preparation. In this interpretation, firing rates are
initially variable across trials but are brought, over time, to their appropriate values,
becoming consistent in the process.” [7]
Before the target appears, the average firing rate is quite low. When the target
appears, since this neuron is responsible for encoding that muscle movement, the average
rate goes up. But if we look at the estimation of the firing rate within a specific trial
(gray lines) not the trial average, we see that during the spontaneous activity, there is
lots of variability in terms of the rate. On some spontaneous trials, the firing rate is a
little higher compared to the average and on the other trials, it is lower. But during the
task, all these gray lines sort of collapse onto a single line. If we think of this from a
stochastic process point of view, they are all Poisson processes, but in the spontaneous
state, the firing rate of that Poisson process is itself random. That is why they call it a
“doubly stochastic” process. Then, during the trial, that firing rate variability collapses
and we will just have the classic Poisson. Thus, this super-Poisson behavior seems to
be characterized in the spontaneous state quite heavily. Fano factor is quite high in the
spontaneous state, i.e., the variance is higher than the mean, but then it drops during
the task. See Figure 6.
Mark Churchland, Krishna Shenoy, and Byron Yu published a paper a few years
back where they said that the stimulus- or motion-induced quenching a variability is a
generic feature of cortex.
“We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys
and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability
decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays.
The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests
a rather general property of cortex, that its state is stabilized by an input.” [8]
In all these cases, the arrow denotes the beginning of a trial or a stimulus. The Fano
factor tends to drop pretty reliably when the stimulus comes in or when the task starts.
Therefore, this stimulus-induced quenching a variability due to this doubly stochastic
process seems to be something that we think cortex has. See Figure 7.
Balanced networks will not produce these kinds of behaviors.
If we put attractors into our system, we can capture these behaviors.
2 Clustered excitatory networks
Balanced cortical networks by themselves show many important properties of biological
neural networks, but as mentioned in the previous section, they do not capture all of
their properties. Specifically, they do not capture super-Poisson firing rate behaviour and
stimulus-dependent decrease in trial-to-trial variability. We wish to explore, therefore,
Figure 6: Simulations illustrating how an increasing consistency in across-trial firing rate
could be detected using the normalized variance (NV) metric. Simulations were based on
the mean firing rate of one recorded neuron (solid black trace at top). Baseline activity
was artificially extended (to the left) to allow longer simulations. For each of 10,000
simulated trials, spike trains were generated using Poisson statistics. Two versions of
the simulation were run. For the first version, the underlying firing rate was identical
(black trace at top) on all simulated trials. The resulting NV is shown by the black
trace at the bottom. For the second version, each trial had a different underlying firing
rate, generated by adding noise, filtered with a 30 ms SD Gaussian, to the mean. The
magnitude of this noise decayed with an exponential time constant of 200 ms after target
onset. Ten examples of the resulting underlying firing rates are shown in gray at top,
and the resulting spike trains (computed with Poisson statistics, with the time-varying
mean taken from the gray traces) are shown in the rasters. The NV computed from
10,000 such spike trains is shown by the gray trace at the bottom. (Figure 2 in [7])
the possibility that introducing clustering into balanced cortical networks can reveal
some of these signatures of attractor dynamics in spiking activity.
2.1 Introducing clustering
Clustering can be introduced into an Erdos-Renyi model by artificially breaking the
network up into groups. Edges are once again assigned randomly and independently of
each other, however, the probability of creating an edge within each neuron group (pEE
in )
is different from the probability of creating an edge between two neurons in different
groups (pEE
out ). Setting pin > pout results in dense clusters with few edges going between
It should be noted that this clustering is only present for the excitatory neurons.
There has been no evidence of inhibitory clustering. Fino and Yuste used a two-photon
photostimulation technique in order to explore the structure of cortical microcircuits in
mouse frontal cortex and found that “most, and sometimes all, inhibitory neurons are
locally connected to every sampled pyramidal cell”. Furthermore, “inhibitory innervation of neighboring pyramidal cells is similar, regardless of whether they are connected
among themselves or not”, from which they conclude that inhibitory connectivity “can
approach the theoretical limit of a completely connected synaptic matrix” [10].
2.2 Firing rate transitions
The introduction of clusters creates a new kind of spiking dynamic in the network,
as depicted by the neural spike trains [9]. We observe that clusters of neurons tend to
increase and decrease their overall firing together. That is, the clusters have spontaneous
and synchronous changes in their firing rates, against a background average clusterindependent firing rate for all neurons. Due to the presence of denser clusters, neurons
within a cluster provide positive feedback to each other as a result of which they have
an increased propensity to fire with a higher rate synchronously. This leads to the kind
of raster plot depicted in figure 8.
Spike train statistics
The clustered network still gives a lognormal distribution of firing rates. The average
Fano factor of the clustered network is found to be closer to 1.5, indicating superPoisson behaviour. The autocovariance of single-neuron variability shows long time-scale
dynamics that lasts 100s of milliseconds, despite the fact that the intrinsic time scales in
the network (synapses, membrane potentials, etc.) are of the order of 10ms, indicating
that the long time-scale dynamics is a feature of the architecture. We also see long
time-scale correlations between members of the same cluster.
An energy-landscape explanation of synchronous in-cluster firing rate fluctuations
A simple explanation of why clustering causes the kind of dynamics we just saw is
provided by an energy-landscape model, shown in figure 9. When the network consists
of just a single cluster, it has a unique stable state, in which all neurons have the same
average firing rate. But when we introduce clustering into the network, positive feedback
between neurons within a cluster creates new stable states or “attractors” within this
energy landscape. Chaotic fluctuations can now cause transitions from one stable state
to another, resulting in two different average firing rates for a given cluster.
Clusters are not entirely independent of each other due to the absence of inhibitory
clustering. So the excess in activation of one cluster will cause inhibition and hence
reduced activity of other clusters.
2.3 Stimulus-dependent reduction in trial-to-trial variability
The natural question to ask next is whether or not this variability in firing rate is reduced
as a result of stimulation. This problem was explored by Doiron and Litwin-Kumar [11],
who found that stimulation causes a change in the energy landscape by breaking the
symmetry between the low-activation and high-activation states of each cluster. In the
absence of stimulation, clusters have an equal likelihood of being in either state (since
their “potentials” are equal). High activation is not restricted to any particular cluster,
and clusters “compete” for increased firing rate.
But on stimulation of one cluster, the potential of its high-activation state reduces
and thus it is more likely to fire at a higher rate. Correspondingly, other clusters are
likely to fire at a lower rate (due to the inhibitory effect of the stimulated cluster on the
others), suggesting an increase in the “potential” of their high-activation states. This is
shown in figure 10.
Stimulation, therefore, breaks the symmetry between clusters and makes one of the
clusters “more attractive” in the energy landscape than the others. This is essentially a
decrease in the variability of the firing rates of clusters, and hence of neurons.
Fano factor reduction
This is reduction in variability is shown in simulation by a reduction in the Fano factor.
The bias towards firing of the stimulated clusters is evident from the spike train raster
plots. The reduction in Fano factor matches what is expected from investigations of
actual neurons in motor cortex by Churchland and Yu [8].
We have therefore shown that the single neuron signature of cortical activity (decrease
in variability) supports an architectural hypothesis of clustered networks.
3 Can stable attractors be learned?
Tsodyks et. al. [12] showed that spontaneous dynamics often in cortical networks often
resemble or mimic dynamics produced on stimulation. Specifically, they examined the
dynamics of a large area of cortical network of a cat, evoking certain patterns by repeatedly showing the cat an image. Later, they found that in the absence of input (eyes
closed), the spontaneous patterns produced were similar to the evoked patterns. This is
depicted in [figure]. Luczak et. al. [13] have more recently shown that in rat auditory
and somatosensory cortex, “vectors [i.e., patterns] evoked by individual stimuli [occupied] subspaces of a larger but still constrained space outlined by the set of spontaneous
events”. Han et. al. [14] have also shown, using Calcium waves as identifiers of cortical
dynamical patterns, that “visually evoked cortical activity reverberates in subsequent
spontaneous waves” in rat visual cortex.
These results indicate that stimulation creates an imprint of patterns in biological
neural networks, glimpses of which are seen during subsequent spotaneous activity. This
therefore begs the question: is it possible to “learn” such patterns in a balanced cortical
3.1 Introducing weight update for learning: Spike-timing-dependent plasticity
In order to examine this, we need to create a system of learning, wherein the synaptic
weights between different neurons can change. So far, we have assumed that the network
structure and synaptic weights themselves are static. Dynamical activity in the network
does not cause any change in network connections. But if we want evoked patterns to
get imprinted on the network, we need to create a mechanism for learning, by allowing
synaptic weights to change.
To this end, we use a simple learning rule called spike-timing-dependent plasticity
(STDP). If there is a connection from neuron A to neuron B, we increase the strength of
the connection every time A fires just before B fires, and we decrease the strength of the
connection every time A fires just after B fires. In other words, this scheme is causalityrewarding, and strengthens connections whenever it finds it likely that A played a role
in making B fire. This is depicted in figure 13.
3.2 Unstable network activity and STDP of inhibition
Using STDP only for excitation does not work, however, as it leads to large positive
feedbacks: rewarding causality strengthens connections, which makes it more likely that
those connections will produce causal spike trains, which in turn will continue to increase
their strength. The end result of this is that the network starts seizing - the whole
network fires synchronously at a constant high rate. This is very pathological behaviour,
and is not something observed in normal, healthy biological neural networks. This had
been shown earlier by Morrison et. al. [16].
One way to overcome this problem is to introduce plasticity in the inhibitory neurons
as well, as suggested by Vogels et. al. [17]. This ensures that excitation and inhibition
keep each other in check, and prevent explosive positive feedback. The inhibitory weightupdate is depicted in figure 14.
3.3 Learning metastable activity
With the introduction of STDP of inhibition, we can train the network to show spontaneous metastable activity. This is done by simultaneously and repeatedly stimulating
groups of neurons that we want to make into clusters. The stimulation causes those neurons to fire more, which strengths inter-neuron connections through STDP. Over multiple
stimulation cycles, the neurons form clusters and start exhibiting the same spontaneous
metastable dynamics that we saw earlier in clustered balanced cortical networks.
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Figure 7: Changes in firing-rate variability for ten datasets (one per panel). Insets
indicate stimulus type. Data are aligned on stimulus onset (arrow). For the two bottom
panels (MT area/direction and MT speed), the dot pattern appeared at time zero (first
arrow) and began moving at the second arrow. The mean rate (gray) and the Fano
factor (black) were computed using a 50-ms sliding window. For OFC, where response
amplitudes were small, a 100-ms window was used to gain statistical power. The resulting
stabilized means are shown in black. The mean number of trials per condition was 100
(V1), 24 (V4), 15 (MT plaids), 88 (MT dots), 35 (LIP), 10 (PRR), 31 (PMd), 106
(OFC), 125 (MT direction and area) and 14 (MT speed). (Figure 1 in [8])
Figure 8: (a) Schematic of recurrent network, showing excitatory (triangles) and inhibitory (circles) neurons. Connections within and between populations were nonspecific for the uniform network. Shaded regions in the clustered network schematic indicate
subpopulations with increased connection probability and strength for neurons belonging to the same cluster. The full network contained 4,000 excitatory neurons in 50
clusters of 80 neurons each and 1,000 inhibitory neurons. (b) Visualization of connectivity for a subpopulation of 240 excitatory neurons in three clusters. Nodes correspond
to excitatory neurons; edges, synaptic connections. Nodes are positioned according to
the Fruchterman-Reingold force algorithm (see Online Methods). Edge widths reflect
synaptic strength. (c) Example voltage trace for an excitatory neuron. (d) Spike raster
showing the spike times of a subpopulation of 1,600 excitatory neurons. Figure taken
from Figure 1 in [9].
Figure 9: (a) Potential governing dynamics of average cluster activity hsE
in i, for different
values of clustering REE . As clustering increases beyond a critical value, bistability
emerges (black curve) and switches (arrow) can occur between the two stable states. (b)
Potential (top) and corresponding histogram for average cluster activity (bottom). (c)
Top: example activity raster for the parameters in b. Transitions are evident between the
high activity (red) and low activity (blue) states. Bottom: histogram of input currents
for neurons in the cluster conditioned on the cluster being in the low or high activity state
(defined as average activity less than or greater than 0.5, respectively) and normalized
in peak height. (d) Average timescale of transitions from high activity to low activity
state as a function of well depth ∆U . Each point corresponds to an average over four
networks simulated for 200,000 time steps. The line corresponds to a linear regression
of well depth against the logarithm of the transition time (coefficient of determination
R2 = 0.89). Figure taken from Figure 5 in [9].
Figure 10: (a) Potentials for REE = 2 and different values of stimulus (stim) applied to
the cluster. A stimulus can bias activity toward the low or high activity state depending
on its sign. (b) Consequences of stimulation and recurrent inhibition. Left: when all
clusters are symmetric, any cluster can transition to the high activity state. Right:
when cluster 2 receives a stimulus to bias it to the high activity state, other clusters are
suppressed by recurrent inhibition. Hence the network remains in one particular activity
configuration. Figure taken from Figure 6 in [9].
Figure 11: (a) Spike raster showing the activity of 1,600 excitatory neurons on one trial.
Neurons in the shaded region received the stimulus when applied. (b) Time course of
stimulus, a depolarizing step of current that lasted 400 ms. The stimulus caused the
clusters that received it (a, shaded region) to transition into a high activity state. (c)
Fano factor computed over 100-ms windows as a function of time for excitatory neurons.
For comparison, the average Fano factor for uniform networks receiving an identical
stimulus is shown. Only clustered networks exhibited a noticeable decrease in variability
when stimulated. Shaded regions denote 95% confidence intervals. (d) Left: same as
c, but restricted to neurons in stimulated clusters (corresponding to a, shaded region).
Right: same as left, but restricted to neurons not in stimulated clusters. Figure taken
from Figure 7 in [9].
Figure 12: Effect of repeated visual stimulation at a given position on spontaneous
waves. (A) Spontaneous waves immediately before and after training (10.24 s/session).
Each image shows the initial frame of a wave. Initiation site and propagation path are
indicated by a white circle and a line. Left, Evoked wave in response to the training
stimulus. Spontaneous waves well matched to the template are indicated; ∆, CC >
0.6; ∆∆, CC > 0.7. (B) Superposition of the initiation sites and propagation paths
for all spontaneous waves before and after training (gray) and for the training-evoked
wave (red). Dotted circle indicates a 0.6 mm radius from the initiation site of the
training-evoked wave. (C) Distribution of the distance between the initiation sites of the
spontaneous waves and the evoked template before (dashed) and after (solid) training
(30 training experiments, 9 rats). (D) Difference in the percentage of matched waves
before and after training plotted as a function of the CC threshold (*, p < 0.05; **,
p < 0.02; ***, p < 0.01, same data as in C). Error bar, ±SEM. Figure taken from Figure
2 in [14].
Figure 13: This graph shows the change in excitatory post-synaptic current amplitude,
after repeated correlated spiking, plotted against spike timing. The same kind of function
is used as a weight-update rule, where the y-axis now represents the percentage change
in synaptic strength that is made, for a given spike-timing difference. Figure taken from
figure 7 in [15].
Figure 14: Spike-timing–dependent learning rule: Near-coincident pre- and post-synaptic
spikes potentiate inhibitory synapses, whereas every presynaptic spike causes synaptic
depression. Figure taken from figure 1 in [17].
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