Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios

Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios
Neuronal Assembly Dynamics in Supervised and Unsupervised
Learning Scenarios
Renan C. Moioli, Phil Husbands
Abstract— The dynamic formation of groups of neurons neuronal assemblies - is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis
suggests that synchronised oscillations underpin their formation and functioning, with a focus on the temporal structure
of neuronal signals. In this context, we investigate neuronal
assembly dynamics in two complementary scenarios: the first,
a supervised spike pattern classification task, in which noisy
variations of a collection of spikes have to be correctly labelled;
the second, an unsupervised, minimally cognitive evolutionary
robotics tasks, in which an evolved agent has to cope with
multiple, possibly conflicting objectives. In both cases, the more
traditional dynamical analysis of the system’s variables is paired
with information theoretic techniques in order to get a broader
picture of the ongoing interactions with and within the network.
The neural network model is inspired by the Kuramoto model
of coupled phase oscillators, and allows one to fine tune the
network synchronisation dynamics and assembly configuration.
The experiments explore the computational power, redundancy,
and the generalisation capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number
of assemblies and neurons in the network, and showing that the
framework can be exploited to generate minimally cognitive
behaviours, with dynamic assembly formation accounting for
varying degrees of stimuli modulation of the sensorimotor
Since Hebb’s seminal work on brain activity (Hebb, 1949),
the transient formation of neuronal groups or assemblies is
increasingly linked to cognitive processes and behaviour. In
fact, there is a growing consensus that ensembles of neurons,
not the single neurons, constitute the basic functional unit
of the central nervous system in mammalians (Averbeck &
Lee, 2004; Nicolelis & Lebedev, 2009). However, labelling
a certain group of neurons as constituting an assembly is
a challenging task that only recently has been alleviated
by more advanced recording techniques and analysis tools
(Buzsaki, 2010; Lopes dos Santos et al., 2011; Canolty et al.,
2012). Also, it is still unclear how neuronal groups form,
organize, cooperate and interact over time (Kopell et al.,
2010, 2011).
One hypothesis that has gained considerable supporting
experimental evidence states that groups of neurons have
their functional interactions mediated by synchronised oscillations, so-called “binding by synchrony” (Singer, 1999;
Varela et al., 2001; Uhlhaas et al., 2009). As structural
Renan C. Moioli and Phil Husbands are with the Centre for Computational Neuroscience and Robotics (CCNR), Department of Informatics,
University of Sussex, Falmer, Brighton, BN1 9QH, United Kingdom, (email:
{r.moioli, p.husbands}
to appear Neural Computation 25(11), 2013.
connectivity is relatively static at the time-scale of perception
and action, the central idea is that the synchronisation of
neuronal activity by phase locking of network oscillations
are exploited to define and encode relations between spatially
distributed groups of neurons, and information dynamics
and computations within the network relate to the timing of
individual spikes rather than their rates. Indeed, phase relationships contain a great deal of information on the temporal
structure of neural signals, modulate neuron interactions, are
associated with cognition and relate to memory formation
and retrieval (Izhikevich, 1999; Womelsdorf et al., 2007;
Masquelier et al., 2009; Kayser et al., 2009). Moreover,
recent works have shown that specific topological properties
of local and distant cortical areas support synchronisation
despite the inherent axonal conduction delays, thereby providing a substrate upon which neuronal codes relying on
precise interspike time can unfold (Vicente et al., 2008; Pérez
et al., 2011).
Based on the concepts above, in this paper we focus on
a pragmatic investigation of three aspects of computations
in neuronal assemblies. Given a computational task and
a neural network model, comprised of many neurons that
are organised in an arbitrary number of assemblies, (1)
does increasing the number of neural assemblies improve
performance? (2) Does the number of neurons per assembly
affect performance? (3) Can dynamic assembly reorganization alone, leaving aside other plasticity mechanisms, be
exploited to solve different tasks?
We approach these questions employing a neural network
model based on the Kuramoto model of coupled phase
oscillators (Kuramoto, 1984). It has been extensively studied
in the Statistical Physics literature, with recent applications
in a biological context due to its relatively simple and
abstract mathematical formulation yet complex activity that
can be exploited to clarify fundamental mechanisms of
neuro-oscillatory phenomena without making too many a
priori assumptions (Ermentrout & Kleinfeld, 2001; Cumin &
Unsworth, 2007; Kitzbichler et al., 2009; Breakspear et al.,
2010; Moioli et al., 2012). The model explicitly captures
the phase dynamics of units that have intrinsic spontaneous
oscillatory (spiking) activity and once connected can generate
emergent rhythmic patterns. The correspondence between
coupled phase oscillators and neuronal models is grounded in
the phase reduction approach (Ermentrout & Kopell, 1986),
according to which analysis of neuronal synchronisation phenomena based on complex models can be greatly simplified
by using phase models.
However, in addition to modelling constraints (described
in Section II), the original Kuramoto model has limited
spectral complexity compared to that of more biologically
plausible neuronal models (Bhowmik & Shanahan, 2012);
for this reason, recent extensions have been formulated
to enhance its suitability to study a variety of neurobiological phenomena, incorporating e.g. spatially embedded
couplings, transmission delays, and more complex phase
response curves (Breakspear et al., 2010; Wildie & Shanahan,
2012). Nevertheless, it is possible to represent neurons as
simple phase oscillators, model the spiking of individual
cells, and results can still be of relevance. Indeed, this is
exactly the objective, to avoid physiologically precise models
that could make the analyses laborious and instead use
a model that despite all the simplifications still presents
complex and relevant spatiotemporal activity. One particular
extension, presented in Orosz et al. (2009), allows one to
fine tune the synchronisation regime, the number of assemblies, and the number of neurons per assembly, thus suiting
our study, whilst also avoiding any problems in obtaining
phase information (an issue in other models which consider
frequency and amplitude dynamics (Pikovsky et al., 2001)).
Hence the extended Kuramoto model is highly relevant, at a
certain level of abstraction, to modelling neural mechanisms
underlying adaptive and cognitive behaviours and is used in
the studies presented here.
The experiments were set-up to encompass supervised and
unsupervised learning scenarios (Dayan & Abbot, 2001). In
supervised learning, there is an explicit target or supervisory
signal mapping each set of inputs to expected outputs. In
unsupervised learning, the system exploits the statistical
structure of the set of inputs and operates as a self-organised,
goal-oriented process. Although the latter is regarded as
being more common in the brain, evidence suggests that both
learning paradigms overlap and may be implemented by the
same set of mechanisms (Knudsen, 1994; Dayan, 1999).
The first experiment, a supervised learning scenario, follows a method described in Maass et al. (2005) and Legenstein & Maass (2007) to assess computational performance in
generic neuronal microcircuits. More specifically, we analyse
the computational power and generalization capability of
neuronal networks with diverse assembly configurations in
a generic spike pattern classification task. The method is
specially suited to our goals because it proposes a measure
to test the computational capability of neural microcircuits
that is not exclusive to the task investigated here but to
all computational tasks that only need to have in common
which properties of the circuit input are relevant to the target
outputs. In networks with the same number of neurons, we
show that the performance of architectures constituted by
many assemblies (and fewer neurons per assembly) is higher
than the ones with fewer assemblies (and more neurons per
assembly). We also show that in networks of varied size performance saturates as soon as a given number of assemblies
is formed, and the addition of neurons in each assembly does
not influence performance in the classification task. In both
scenarios, an analysis of redundancy and synergy, based on
concepts of information theory, supports and provides further
insights into the properties of the system.
The pattern classification task mentioned above may reflect
or mimic some of the computations that are actually carried
on in a real-world cognitive scenario, nevertheless it does not
capture the main task of cognition, which is the guidance of
action. As pointed out in Engel et al. (2001), “the criterion
for judging the success of cognitive operations is not the
correct representation of environmental features, but the
generation of actions that are optimally adapted to particular
situations”. Therefore, in the second experiment, an unsupervised learning scenario, we investigate evolved embodied
cognitive behaviours in a simulated robotic agent. Following
an Evolutionary Robotics approach, we show that the same
network architecture of Experiment 1 can be used as a control
system for a simulated robotic agent engaged in a minimally
cognitive task, and that assembly reconfiguration can account
for good performance in multiple, possibly conflicting tasks.
The analysis is centred both on the system’s variables dynamics, illustrating the interplay between dynamic assembly
formation and the action being displayed by the robot,
and the information dynamics between some components
of the system, which complements the former analysis by
quantifying and emphasizing the non-linear relationships that
are present in the brain-body-environment interactions.
As a consequence of approaching different learning
paradigms, the analyses for the two experiments use distinct,
but appropriate, tools. However, it is important to stress that
the above experiments are conceptually connected by the
emphasis on neuronal assembly dynamics and its impact
on task performance. The methods employed to explore
supervised learning tasks struggle to operate in unsupervised
scenarios because the former rely on coordinated, timespecific perturbations and measurements, with a focus on
precise classifications, whilst the latter are mainly concerned
with the behaviour of the evolved robots. Notwithstanding,
the first experiment provides insights into the system’s dynamics which contribute to the comprehension of the more
elaborated second experiment. In this sense, the supervised
and unsupervised learning tasks and the respective methods
of investigation do not contradict but rather reinforce the
flexibility of the framework in addressing diverse learning
This paper is organized as follows: Section II presents
the neural network model, including the rationale behind
neural network models using coupled phase oscillators and
the extension to the Kuramoto model which facilitates the
study of assembly dynamics, and a brief introduction to
Information Theory, which is the basis of some analysis
carried on in the experiments; Sections III and IV contain
task-specific analysis methods and the results of the first
and second experiment, respectively; the paper concludes in
Section V by highlighting the main contributions and giving
a general discussion of the results obtained.
A. Neural Network Model
Neural network models based on the dynamics of voltagedependent membrane currents (among which the HodgkinHuxley model is perhaps the most well known) can be
described by a single phase variable θ provided that the
neurons are assumed to spike periodically when isolated,
their firing rates are limited to a narrow range, and the
coupling between them is weak (Hansel et al., 1995). In
fact, many neural oscillatory phenomena can be captured and
analysed by studying the dynamics of coupled phase oscillators (Izhikevich, 2007), provided that the above conditions
hold. In this sense, the Kuramoto model (Kuramoto, 1984) of
coupled phase oscillators has been shown to be a useful tool
in studying oscillatory phenomena in a broad range of fields,
from semiconductor physics to fireflies’ blinking pattern. The
model is described by Equation 1:
θ̇n = ωn +
g(θn − θm ), n = 1, . . . , N.
N m=1
g(θn − θm ) + ǫn In (t), n = 1, . . . , N,
N m=1
where In (t) is an input scaled by a factor ǫn , and the PIF
g(γ) has the form of Equation 3:
θ̇n = ωn +
where θn is the phase of the nth oscillator, ωn is the natural
frequency of the nth oscillator, K is the coupling factor
between the nodes of the network, g(θn − θm ) = sin(θn −
θm ) represents the interaction between nodes, and N is the
total number of oscillators.
The phase interaction function (PIF) g assumes the mutual
influence among the symmetrically coupled oscillators to
be periodic, i.e. gnm (x + 2π) = gnm (x); it can, thus,
be expanded into a Fourier series. The Kuramoto model
considers only the first term of this series, but when g
incorporates more complex interactions between the nodes
rather than the first harmonic only, the model displays a more
complex spatio-temporal behaviour and the synchronisation
patterns observed are closer to the ones measured in real
brains (Hansel et al., 1995; Breakspear et al., 2010).
In particular, Ashwin et al. (2007) and Wordsworth &
Ashwin (2008) showed, when adopting a specific g, that the
model is able to display heteroclinic cycles, a fundamental
mechanism of cognition according to some authors (Ashwin
& Timme, 2005; Rabinovich et al., 2012). Additionally,
Orosz and collaborators (Orosz et al., 2009) demonstrated
how to design g so that the network organizes itself in
an arbitrary number of stable clusters with a given phase
relationship between clusters. These clusters, which emerge
as an attractor of the system, remain stable up to a certain
level of perturbations, applied in the form of inputs, above
which a reorganization occurs, maintaining the same number
of assemblies but with different membership configurations.
Therefore, considering the aims of our study, this latter
extension will be used in the subsequent experiments and
is used as an abstract representation of interactions between
spiking neurons. Equation 2 describes the model for N
oscillators (Orosz et al., 2009):
g(γ) = fM (γ) + fM (γ − ξ)
where fM (γ) = −2tanh(M sin(γ/2))sech2 (M sin(γ/2))cos(γ/2)
and ξ = 2π/M .
This PIF is obtained by a suitable choice of g and its
derivatives to ensure that a system with N oscillators will
present M stable assemblies separated equally in phase,
with oscillators grouped according to their initial phases
(which will dictate their position in the attraction basin
determined by the total number of assemblies and parameter
M ). Assembly membership, i.e. which oscillator belongs to
which assembly, can be changed if one applies an input to
a given oscillator with a minimum magnitude and length.
These will depend on the number of oscillators and assemblies (parameter M ) of the network. Nevertheless, small
perturbations still affect the overall behaviour of the system.
Observe Figure 1, which illustrates the main properties of
the model.
The network is composed of 9 fully connected neuronal
oscillators with unitary couple (without loss of generality, the
PIF is assumed to capture any effect due to larger or smaller
couplings). The initial phases are uniformly distributed in
the interval [0, 2π), and the oscillators organize in M = 3
different (but with equal number of members) assemblies
after a settling period (Figure 1(c)). As the focus is on
neuronal assembly in terms of phase relationships, we set
the natural frequency wn of all neurons to 1. In Figure 1(d),
the raster plot shows the neuronal spikes that occur every
time the phase of each oscillator reaches a given threshold
(0 in the example, but any other marker is acceptable). Notice
from both aforementioned figures the formation of three
assemblies of three neurons each.
After a settling period the system stabilizes in M assemblies and presents a periodic firing behaviour. However,
inputs to one or more neurons can change the network
dynamics in two ways: it can modulate the ongoing activity
in all assemblies without changing their organization or it
can cause the assemblies to rearrange. Figure 1(e) illustrates
the effect (see the caption for simulation parameters). At
the beginning of the simulation, the initial phase values of
each neuron will determine to which assembly each neuron
will be associated. The number of assemblies (parameter
M ) determine the size of the attraction basin and hence the
necessary input amplitude and length to cause a given node to
switch assemblies. In the example, the phase of an oscillator
has to be perturbed by an absolute value greater than π/3
to change to a different stable cluster. At time t = 30, an
input of sufficient duration and magnitude is applied to one
neuron causing it to “jump” and take part in a different
Fig. 1. Model simulation using the PIF described by Equation 3, with parameters N = 9, M = 3, K = 1 and wn = 1. Oscillators form 3 clusters,
and inputs to a given oscillator cause a transition to a different cluster, if the magnitude is high enough, or a modulation of the network behaviour, if the
input is small enough. (a) Network topology model. (b) PIF diagram (Equation 3). (c) Phase dynamics of each oscillator. The initial phases are uniformly
distributed in [0, 2π), and as the simulation progresses the oscillators form M = 3 assemblies (assembly membership is represented by different grey tones
in the plot). The small plot shows the moment (t = 30, see the black arrow) one oscillator moves from one assembly (solid light grey line) to another
(dashed light grey line). (d) Raster plot showing the neuronal spikes that occur every time the phase of each oscillator reaches 0. (e) Effects of inputs
on the system’s dynamics, portrayed as the phase difference γn,1 of each node n to node 1: inputs can cause an oscillator to change assemblies (black
arrows) or modulate its ongoing activity within the same assembly (grey arrow).
assembly. At iteration t = 50, an input of the same duration
but smaller amplitude than the one at t = 30 perturbs the
overall dynamics of the network but does not result in a
change in assembly membership. Lastly, at iteration 70 an
input of the same duration but opposite magnitude as the
first causes the related neuron to jump to another assembly.
Notice, in the insert plot of Figure 1(c) and in the raster plot
in Figure 1(d), the changes in phase dynamics and spiking
activity due to different forms of inputs.
H(X) = −
p(x) log p(x)
where X is a discrete random variable defined for an alphabet
A of symbols and probability mass function p(x).
In Experiment 1 and 2 we will present different measures
of information, based on the concept of entropy, to gain
further knowledge on the relationship between input spike
trains, neuronal responses, and motor behaviour.
B. Information Theory
Information Theory provides a framework for quantifying and emphasizing the non-linear relationships between
variables of the system, hence its suitability in Biology and
Robotics studies (Rieke et al., 1997; Lungarella & Sporns,
2006). According to the standard definition, information is
not an absolute value obtained from a measurement but rather
a relative estimation of how much one can still improve on
the current knowledge about a variable.
Commonly, transmitter-receiver modelling involves random variables, and the inherent uncertainty in trying to
describe them is termed entropy (Shannon, 1948; Cover &
Thomas, 1991). It is an intuitive notion of a measure of
information, described by Equation 4:
Maass et al. (2005) proposed a method to evaluate the
computational power and generalization capability of neuronal microcircuits which is independent of the network set
up. In this first experiment, the model described in the last
section is used to analyse the computational performance of
networks structured in various assembly sizes with diverse
numbers of neurons per assembly. In all of the following
analysis, different network configurations are obtained varying the value of M (Equation 2) and the initial phase of each
A. Methods
1) Classification Tasks, Computational Power, and Generalisation Capability: Maass et al. (2005) proposed the linear
separation property as a quantitative measure for evaluating
the computational power of a neuronal microcircuit. The
premises are that the microcircuit consists of a pool of highly
recurrently connected neurons and that the information encoded in their activity can be extracted by linear readout
neurons able to learn via synaptic plasticity, with no influence
from readout units to the microcircuit. Although these simplifying assumptions impact on the biological relevance of the
results, they are still valid in face of the many uncertainties
regarding electrochemical interactions in the brain and the
nature of neural coding. In fact, literature in brain-machine
interface (BM I) studies has been able to show that a
relatively simple linear readout unit from a reduced number
of neurons is able to extract the relevant neuronal activity that
relates to the action being performed (Lebedev et al., 2005).
Also, Buzsaki (2010) argues that cell assembly activity can
be better understood from a “reader” perspective, able to
produce outputs given the ongoing activity.
Consider the model described by Equation 2. Let us call
the n size vector θ(t0 ) the system state at time t0 . Now
consider a neuronal microcircuit C and m different inputs
u1 , . . . , um that are functions of time. One can build a n ×
m matrix M , in which each column consists of the states
θui (t0 ), i.e. each column consists of the phase value of each
node n at time t0 after the system has been perturbed by
an input stream ui . The rank r ≤ m of matrix M can then
be considered as a measure of the computational power of
circuit C. Based on linear algebra, the rationale is as follows:
if M has rank m, a linear readout unit of microcircuit C can
implement any of the 2m possible binary classifications of the
m inputs, i.e. any given target output yi at time t0 resulting
from the input ui can be mapped by a linear readout unit
(Maass et al., 2005).
Another important measure regarding a neuronal microcircuit is its ability to generalise a learnt computational function
to new inputs. Consider a finite set S of s inputs consisting
of many noisy variations of the same input signal. One can
build a n × s matrix M whose columns are the state vectors
θus (t0 ) for all inputs u in S. An estimate of the generalisation
capability of this circuit is then given by the rank r of matrix
M . See Vapnik (1998) and Maass et al. (2005) for a more
complete description of the method.
In the experiment, we evaluate oscillatory neuronal networks comprised of 80 ± 4 neurons organised in different assembly configurations. Ideally, for consistency in the
comparisons between the measurements, the system should
always have the same number of states across different trials;
however, as we are interested on the gradient of performance
when comparing different assemblies set up, we have used
architectures with a few more or a few less states to allow
for a broader set of configurations.
In this way, for a variety of possible architectures of
microcircuits C, the task consists of classifying noisy variations u of 20 fixed spike patterns which were arbitrarily
divided into two classes (0 or 1). For one randomly chosen
classification task (there are 220 possible classifications of
the spike patterns), the objective is to train a linear readout
unit to output at time t = 4s the class of the spike pattern
from which the noisy variation input had been generated.
Each spike pattern u consisted of a Poisson spike train with
a rate of 1Hz and a duration of 4s. Inputs are always applied
to node 2 of the network, according to Equation 2. An Euler
integration time-step of 0.02s is used.
At the beginning of a simulation, 20 fixed spike patterns
are generated. For each pattern, we produced 30 jittered
spike trains by jittering each spike in each spike train by an
amount drawn from a Gaussian distribution with zero mean
and standard deviation of 0.1s. If after jittering a spike was
outside the time interval of [0, 4] seconds, it was discarded.
20 of the jittered sequences are used for training and 10 are
used for testing the performance. Figure 2(a) shows some
examples of input spike trains and the respective jittered
versions. For each simulation, we randomly classified 10
spike patterns as belonging to class 1 and 10 to class 0 (recall
that there are 220 possible forms of classifying the patterns).
To calculate the computational power, we generated 76
different spike patterns in the same way as for the classification task described above. The state vectors of the neuronal
circuit at time t = 4s (θ(t0 = 4)) with one of the 76 spike
patterns as input were stored in the matrix M , and its rank r
was estimated by singular value decomposition. To calculate
the generalisation performance, the procedure was similar to
the one just described but instead of using 76 spike patterns
as inputs to the network we used 38 jittered versions of two
different spike patterns, following the recommendation that
the number of network states should be superior to the size
of S (Legenstein & Maass, 2007).
2) Redundancy and Synergy: Another insight into the activity of neuronal assemblies can be given by measurements
of redundancy and synergy (Reich et al., 2001; Schneidman
et al., 2003; Narayanan et al., 2005). In a given network composed of many interacting neurons arranged in assemblies, if
the information encoded by a given pair of neurons is greater
than the sum of the information encoded by the individual
neurons, we say that there is a synergistic interaction; if it is
less, we say that the interaction is redundant.
Consider a neuronal network, with an activity set An of
each individual neuron n composed of a states, and a finite
set S of s inputs. The mutual information (in bits) between
the stimuli and the responses, I(S; A), i.e. the reduction of
uncertainty about the stimuli given that the neuronal activity
A is known, is given by Equation 5:
I(S; A) = H(S)−H(S|A) =
p(s, a) log2
s∈S a∈A
The equation for a pair of neurons is thus:
p(s, a)
4 0
4 0
Fig. 2. Simulation results for Experiment 1. (a) Examples of spike trains used as inputs. In each of the four panels, five spike trains are presented: the
original spike pattern (top train of each panel) and 4 respective jittered versions (subsequent trains in each panel). (b) Classification performance (fraction
of correct classifications) obtained by architectures consisting of approximately 80 neurons arranged in diverse number of assemblies; computational power,
calculated as the value of rank(Mn,m ), where each column of M is the state θum (t4 ) of the network at time t = 4s when submitted to an input um the higher this value, the better a linear readout unit can discriminate between different input spike patterns (values are normalised between 0.6 and 1 to
improve visualisation); generalisation capability, similar to the computational power, but the inputs are now jittered versions of the same spike train - the
smaller this values, the more likely the variations in a spike train will be interpreted as noise instead of consisting of a different spike train; performance
prediction, calculated as the difference between the computational power and generalisation capability. (c) Impact on the classification performance of three
different architectures (6, 16, and 39 assemblies composed of 13, 5, and 2 neurons, respectively) caused by variations in three parameters of the input
spike train (each parameter is varied whilst keeping the other two constant): the standard deviation of the Gaussian jitter in the spike trains j (in s), the
spike firing rate f (in Hz), and the number of patterns to classify Np . (d) State separation and Synergy (rescaled to vary between 0 and 2 to improve
visualisation). Higher values of the first indicate that the network state θ(t) reflects more details of the input stream that occurred some time in the past,
higher values of the latter indicate a more synergistic (less redundant) system. All the previous results are mean values over 20 different simulations, and
shaded areas are the 95% confidence interval.
p(s, a1 , a2 )
p(s, a1 , a2 ) log2
I(S; A1 , A2 ) =
p(s)p(a1 , a2 )
s a1 ,a2
Given the two equations above, the synergy between a pair
of neurons is then defined as (Schneidman et al., 2003):
Syn(A1 , A2 ) =
I(S; A1 , A2 ) − I(S; A1 ) − I(S; A2 )
I(S; A1 , A2 )
Notice that if the mutual information between the two
neurons is 0, i.e. if they have unrelated activity, Equation
6 reduces to I(S; A1 , A2 ) = I(S; A1 ) + I(S; A2 ), and the
synergy value given by Equation 7 is 0. Synergy varies from
−1, if the interaction between the neuronal pair is completely
redundant, to 1, when the information conveyed by the pair
activity is greater than the information conveyed individually
by the neurons.
To estimate the synergy value, stimuli consisted of 8
noisy variations of 8 different spike patterns, lasting for 200
iterations and with the same characteristics as detailed before,
and the neuronal activity An is the phase value of neuron n
at the end of simulation. We performed 20 experiments for
each pair of neurons, and a total of 10 different randomly
Neurons per assembly
B. Results
Figure 2(b) shows the results for the classification performance, the computational power, and the generalisation capability of the system. Notice the increase in performance as
one moves from networks with fewer assemblies (and more
neurons per assembly) to architectures constituted by many
assemblies (and few neurons per assembly). The computational power and the generalisation capability have the same
values until a critical architecture is reached, after which
they start to behave differently. Recall that both measures are
based on calculations of matrices’ ranks, which indicate the
maximum number of linearly independent rows or columns
(whichever is smaller). With just a few assemblies composed
of several neurons, each assembly works as a single, large
oscillator, and inter-assembly modulations due to external
perturbations are minimum. The rank value, thus, is directly
connected with the number of assemblies in the system.
As the assemblies increase in number and decrease in size,
inter-assemblies modulations become more prominent, and
this is captured by the rank of the state matrix. The results
indicate, therefore, that networks with more assemblies have
the potential to classify a greater number of input patterns. In
contrast, the greater the value of the rank of the state matrix
M , the worse the generalisation of the circuit is likely to
be, which means that small perturbations in spike times for
a given spike pattern tend to be classified as belonging to a
different spike pattern.
Maass et al. (2005) and Legenstein & Maass (2007)
showed that, combined, the above two measures may provide
a good estimate of the computational capabilities of a given
neuronal microcircuit and may also be used to predict its
performance in a classification task. There’s no ultimate
method for combining them both, but simply using the difference between the computational power and the generalisation
performance can be a good indicator. Figure 2(b) shows
the result. As explained in the previous paragraph, due to
properties of the model the matrix ranks calculated for each
measure differ only for architectures with higher number
of assemblies; the prediction of computational performance,
therefore, is only applicable for a subset of all possible
chosen pairs were used. The results were then averaged. The
sets S and A were discretised into 8 equiprobable states,
which improves the robustness of the statistics (Marschinski
& Kantz, 2002), and finally the joint probabilities associated
to the information related measures were estimated using
histograms (Lungarella et al., 2007a). In this way, at the
end of each experiment, a table whose columns are all the
possible combinations of [a1 , a2 ] and whose lines are all the
possible stimuli s1 , . . . , s8 is formed, and each field of this
table contains the probability p(a1 , a2 |sn ), from which the
synergy calculations were performed (Equations 5 to 7). An
important point to stress is that ideally we should have tested
all possible neuron pairs and assembly combinations, but that
would have been computationally prohibitive. Nevertheless,
considering the standard deviations observed in the experiments that follow, we believe the results are informative.
Neurons per assembly
Fig. 3. Simulation results for Experiment 1. (a) Classification performance
and (b) Synergy values (not normalised) for different network configurations.
In contrast to Figure 2, in which all architectures had approximately 80 neurons, here the number of assemblies and neurons are varied independently.
Results are mean values over 20 different simulations, and the small plots
within each figure is the standard deviation.
configurations of our model. Nevertheless, the prediction
points at the correct region of possible architectures where
performance is maximum.
Consider Figure 2(c), which shows the response of three
different network configurations to variations in some simulation parameters: the number of different spike patterns
presented to the network for classification, the frequency of
the input spike trains, and the noise rate used to generate
the jittered spike trains. Notice that increasing the value of
the first or the latter result in a fall in performance, whilst
the performance peaks at an intermediate value of the input
frequency. This shows that classifying 60 different patterns
(260 possible classifications) is harder than classifying 20
using the same framework. Also, the relatively small variation in performance due to the input frequency indicates
that the model has a good spike pattern discrimination time
Not surprisingly, noisier spike trains result in more classification mistakes, for an otherwise noisy train is now
viewed as a different spike pattern, but notice that the drop
in performance is sharper for networks composed of more
assemblies (22.6% for a network with 40 assemblies in
contrast with a 10% fall for a network with 10 assemblies),
in agreement with what the generalisation analysis predicted.
One of the reasons this might occur is illustrated in Figure
2(d). It shows the state separation of the system, a measure
that captures how much the state θ(t) of one network reflects
details of the input stream that occurred some time back in
the past. Consider two input patterns u and v over 3000
iterations that differ only during the first 1000, with the
same properties as described before. The state separation
is given by kθu (t) − θv (t)k for t = 3000. Notice that the
architectures with fewer assemblies have a lower value of
state separation than the ones with more assemblies, which
means that perturbations caused by earlier input differences
persist more in the latter configurations. For noisy spike
trains, such amplified differences may impact in the overall
pattern classification performance. The results also highlight
that networks with more assemblies are affected more by
inputs, which can be explained considering that in the latter
case the network state θ(t) at a given time t is less influenced
by the activity of a single neuron and more a product of the
whole network interaction.
The synergy analysis confirms this last point (see Figure
2(d)). Notice that for architectures with fewer assemblies the
level of redundancy is high (Syn = −1, Equation 7), but it
reduces as the number of assemblies grow. The vast majority
of networks with higher numbers of assemblies present
information independence (Syn = 0), i.e., the information
conveyed by the pair of neurons is the sum of the information
they convey separately. Importantly, Schneidman et al. (2003)
make the point that information independence may relate to
neurons being responsive to different features of the stimulus,
but the synergy measurement reflects an average over the
whole set of stimuli S; for that reason, the neuronal pair may
be redundant, synergistic, or independent for different subsets
of S. Also, Reich et al. (2001) found neuronal pairs in nearby
cortical neurons presenting varied forms of interactions more specific, independent and redundant interactions. Thus,
the results portrayed in the figure may vary depending on
which pair is recorded and the measurements may be a result
of averaging, not from independence, across the whole trial.
In the results above, we investigated networks with
roughly the same number of states (Nassemblies ×
Nneurons/assembly ≈ constant). This constraint had to be
imposed in order for the calculations of computational power
and generalisation capability to hold. However, another interesting aspect of assembly computations is how performance
and synergy change as one varies the number of neurons
within each assembly for a given number of assemblies
in the network. Figure 3(a) shows the results. Performance
is predominantly higher in networks with more assemblies,
regardless of the number of neurons within each. In other
words, performance increases as the number of assemblies
increases, but given a certain network with fixed number
of assemblies, adding neurons to each assembly does not
cause a salient increase in performance (e.g. networks with
2 assemblies with 1 or 5 neurons within each assembly have
a classification performance of approximately 0.6 whereas
networks with 20 assemblies with 1 or 5 neurons in each
cluster have a classification performance of approximately
0.85). In contrast, the level of redundancy or independence
is related mainly to the number of neurons within each
assembly, regardless of the total number of assemblies (see
Figure 3(b)). For example, neurons in a network with 20
assemblies with 1 neuron in each assembly present a much
more independent activity than neurons in a network with 20
assemblies composed of 10 neurons each. This is in accordance with results obtained in motor cortex studies, which
show that the synergistic or redundant interactions depend on
the size of each neuronal assembly, and redundancy increases
with the size of assemblies (Narayanan et al., 2005).
Recall that assemblies are formed by their phase relationship, i.e. two neurons belong to the same assembly only if
they are synchronised with near zero phase lag. In this sense,
the synchronisation properties of the network (dictated by the
phase interaction function defined in Equation 3) make the
dynamics of neurons constituting the same assembly similar
and the dynamics of neurons constituting different assemblies
dissimilar. Thus, increasing the number of assemblies, not the
number of neurons, impact more on performance, and that is
possibly due to an increase in entropy. However, the rationale
is not simple because of nonlinear effects and the intrinsic
dynamics of the network responding to inputs. Notably, the
Kuramoto model presents second-order phase transitions and
a given node can influence in different ways other nodes in
the network, depending on the relationship between natural
frequencies and on whether nodes are directly connected
or not. Some of these effects may be in place, given the
saturation in performance and the nonlinear impact on classification to adding assemblies or neurons to the network. In
this sense, it is not trivial that adding neurons maximizes the
classification performance because this is determined by the
way these neurons are organised (assemblies) and limited by
non-linear effects (highlighted by the saturations depicted in
Figures 2(b) and 3(a)).
To conclude Experiment 1, we explore the experimental
evidence (Steinmetz et al., 2000; Lakatos et al., 2008) which
suggests that attentional mechanisms can promote phase
resetting and modulate the ongoing neuronal oscillations
to respond differently to stimuli to investigate whether the
system can cope with multiple tasks by just relying on the
phase dynamics, without any changes in the readout unit after
training. Therefore, as described in Section II, we manipulate
the phase relationship between nodes (emulating attention
mechanisms) and investigate the performance in opposite
versions of a classification task. The network architecture
has been arbitrarily chosen to have 10 clusters of 8 neurons
each, with similar results obtained for other configurations.
To begin with, we present to the network spike patterns
that have to be classified. At the end of each pattern
presentation, the network state θ(t) is stored, representing
71 73
time (s)
Fig. 4. Multiple classification task with dynamic network reconfiguration
in an architecture of 10 assemblies with 8 neurons each. (a) Phase dynamics
portrayed as the phase difference γn,1 of each node n to node 1. Dark grey
solid and dashed lines indicate nodes that change assembly membership (the
black arrow shows the moment of change). The solid black line depicts the
phase behaviour of node 2, that has its ongoing phase dynamics modulated
by the spike train input that has to be classified. Light solid grey lines
relate to the remaining nodes. Phase “a′′ shows the washout phase, when
the phase relationships stabilize in 10 clusters of 8 neurons each, phase “b′′
shows the dynamics during the classification task, phase “c′′ comprises the
reorganisation of the assemblies and phase “d′′ shows the classification task
(same input train, opposite classification labels). Assemblies are rearranged
by perturbing the phase of a given node with an input of magnitude ±1.7
for 20 iterations. Light grey arrows point at the perturbation caused by the
input in node 2 (detailed in the small plot). (b) Impact on the classification
performance caused by variations in three parameters of the input spike train
(notation similar to Figure 2(c)): the standard deviation of the Gaussian jitter
in the spike trains j, the spike firing rate f , and the number of patterns to
classify Np .
the system’s response for this given input. After all the
patterns are shown, the phase relationships in the network
are reorganised (see Figure 4(a)). Then, we present the same
spike patterns once more, the network state is stored, but the
corresponding classification label (1 or 0) for each pattern
is made exactly the opposite from the ones previously used.
Finally, an output readout unit is trained by linear regression
using the network state and the desired classification label
for each pattern. Essentially, the procedure replicates the
previous experiment but with the classification task changing
upon a reorganisation of the nodes’ phase relationship.
Figure 4(a) presents the resulting network dynamics. First,
the system goes through a washout phase (3000 iterations)
and has its phase activity stabilized in 10 clusters of 8
neurons each. Then a spike train input to node 2, lasting 200
iterations, modulates the phase dynamics; at the end, the final
network state is stored. In sequence, the phase relationships
are rearranged by inputs to certain nodes, and the same
classification procedure is executed, with the network state
stored at the end. This process is performed for every spike
pattern used for training, and finally the readout unit weights
are calculated. Figure 4(b) shows the network performance
obtained for different parameter configurations. Notice that
the performance is comparable to the one obtained in the
previous task (Figure 2(b)), which suggests that the phase
reorganisation dynamics can be exploited to solve different
tasks without the need for adaption or plasticity mechanisms
at the readout unit level.
The results for experiment 1 suggest that neuronal assemblies and phase reorganisation dynamics can play a significant part in supervised classification tasks and, perhaps most
relevant to cognition, can cope with multiple classification
tasks without the need for additional adaptive mechanisms.
However, the major part of (natural) neural and cognitive
dynamics is bound up in the generation of unsupervised
embodied behaviour. Hence, in order to explore the possible
roles of neuronal assembly dynamics further, the properties
of the model were investigated in a second experiment in
which it was used in an unsupervised embodied learning
scenario, as described in the next section.
Plasticity mechanisms are a common feature in the brain
and mediate many (if not all) cognitive processes during learning and development (Turrigiano & Nelson, 2004;
Masquelier et al., 2009). There is a rich literature exploring
models of artificial neuronal networks with some kind of
synaptic plasticity in the context of real or simulated agents
engaged in a behavioural task (Urzelai & Floreano, 2001;
Sporns & Alexander, 2002; Di Paolo, 2003; Edelman, 2007;
Shim & Husbands, 2012), but normally the techniques involve the modulation of the electric connections between
nodes of the network as a response to the agent’s actions and
the environment. Here, we explore the way in which neurons
and assemblies relate to each other, and how a modulation of
this relationship alone, without other plasticity mechanisms,
can be exploited to generate adaptive behaviour.
We conduct the analysis following an Evolutionary
Robotics approach (ER), where an evolved simulated robotic
agent controlled by a variation of the system investigated in
Experiment 1 has to solve multiple tasks. In the following
sections, we first present the concepts of Transfer Entropy,
an information theoretic measure used to analyse the results;
then, we explain the ER approach, the robotic model used,
the control system framework, the unsupervised learning
task, and conclude with the outcomes of the experiment.
attenuate these limitations, Marschinski & Kantz (2002) introduced an improved estimator, “Effective Transfer Entropy”
(ET E), which is calculated as the difference between the
usual Transfer Entropy (Equation 8) and the Transfer Entropy
calculated after shuffling the elements of the time series X,
resulting in the following equation:
ET E(X → Y ) ≡ T E(X → Y ) − T E(Xshuf f led → Y )
The ET E formulation is the one used in this paper. We
A. Methods
adopt the orders of the Markov processes as m = n = 1
1) Transfer Entropy: Agent-environment systems pose (Equation 8), and the conditional probabilities are calculated
extra challenges in devising and interpreting a sensible by rewriting them as joint probabilities which are then
measurement of information flow, for they normally have estimated using histograms.
noisy and limited data samples, asymmetrical relationships
2) Evolutionary Robotics: Evolutionary Robotics (ER) is
among elements of the system, and temporal variance (i.e. a relatively new field of interdisciplinary research grounded
sensory and motor patterns may vary over time). Transfer in concepts from Computer Science and Evolutionary BiolEntropy (TE) (Schreiber, 2000), in this context, is suggested ogy (Harvey et al., 2005; Floreano et al., 2008; Floreano &
as a suitable and robust information theoretic tool (Lungarella Keller, 2010). Originally devised as an engineering approach
et al., 2007a,b), and has also been applied to investigate to automatically generate efficient robot controllers in chalreal neuronal assemblies and other neuroscience problems lenging scenarios, where traditional control techniques have
(Borst & Theunissen, 1999; Gourévitch & Eggermont, 2007; limited performance, ER is now well regarded among bioloBuehlmann & Deco, 2010; Vicente et al., 2011); it will, thus, gists, cognitive scientists, and neuroscientists, as it provides
be used in our analysis.
means to simulate and investigate brain-body- environment
TE is based on classical information theory and allows interactions that underlie the generation of behaviour in
one to estimate the directional exchange of information a relatively unconstrained way, thus penetrating areas that
between two given systems. The choice of TE in this work disembodied studies cannot reach.
is based on a study conducted by Lungarella et al. (2007a),
Consider a real or simulated robot, with sensors and
who compared the performance of different IT tools in actuators, situated in an environment with a certain task to
bivariate time-series analysis, which will be the case here, accomplish. Each solution candidate (individual) is repreand concluded that TE is in general more stable and robust sented by a genotype, which contains the basic information
than the other tools explored. The next paragraphs describe of the agent’s body and/or its controller’s parameters (e.g.
the technique.
the number of wheels the robot has and/or the values of
Consider two time series, X = xt and Y = yt , and the weights of an artificial neuronal network acting as its
assume they can be represented as a stationary higher-order controller). According to some criteria, normally the previous
Markov process. Transfer Entropy calculates the deviation performance of that individual in solving the task (fitness),
parents are selected and undergo a process of mutation and
from the generalised Markov property p(yt+1 |ytn , xm
t ) =
recombination, generating new individuals which are then
p(yt+1 |ytn ) where xm
(yt , yt−1 , . . . , yt−n+1 )T and m and n are the orders of the evaluated in the task. This process is repeated through the
higher-order Markov process (note that the above property generations, eventually obtaining individuals with a higher
holds only if there is no causal link between the time series). performance in the given task.
In this sense, ER is a reasonable approach to studying
Schreiber (Schreiber, 2000) defines Transfer Entropy as:
embodied and situated behaviour generation, because it can
be used as a powerful model synthesis technique (Beer, 2003;
p(yt+1 |ytn , xm
t )Husbands, 2009). Relatively simple, tractable models can be
T E(X → Y ) =
p(yt+1 , xm
p(yt+1 |ytn ) produced and studied in the context of what have been called
yt+1 xt yt
(8) Minimally Cognitive Tasks (Beer, 2003), which are tasks
Therefore, from Equation 8 one can estimate the infor- that are simple enough to allow detailed analysis and yet are
mation about a future observation yt+1 given the available complex enough to motivate some kind of cognitive interest.
observations xm
3) Robotic model: The robot is based on the Khepera
t and yt that goes beyond the information
of the future state yt+1 provided by ytn alone. It is thus a II model (K-Team Corporation). It has two wheels with
directional, non-symmetrical estimate of the influence of one independent electric motors, 8 infrared sensors and a camtime series on another.
era (see Figure 5). The sensors measure the environmental
The original formulation of Transfer Entropy suffers from luminosity (ranging from 65 to 450 - 65 being the highest
finite sample effects when the available data is limited, luminosity that can be sensed) and the distance to nearby
and the results obtained may not be correctly estimated. To objects (ranging from 0 to 1023 - the latter value represents
Fig. 5. (a) Real Khepera II robot and (b) its schematic representation,
including the IR sensors and the camera.
frequency wn can be associated with the natural firing rate
of a neuron or a group of neurons, and the sensory inputs
mediated by ǫn alter its oscillatory behaviour according
to environmental interactions, thus improving the flexibility
of the model to study neuronal synchronisation (Cumin &
Unsworth, 2007) within a behavioural context.
At each iteration the phase differences γ from a node n to
nodes n − 1,n = 2 . . . 12, are calculated following Equation
2. Then, the phase differences plus a bias term are linearly
combined by a weight matrix W and fed into two nonlinear
output units that have as activation function the sin function,
which can be interpreted as two output neurons that capture
the ongoing network activity - according to Pouget et al.
(2008), nonlinear mappings (such as the one developed here)
can be used as a comprehensive method to characterize a
broad range of neuronal operations in sensorimotor contexts.
The calculation results in two signals that will command the
left and right motors of the agent (Equation 10):
M = sin(W γ)
Fig. 6. Framework for application in evolutionary robotics. The oscillatory
network is composed of 12 fully connected neuronal oscillators, with nodes
2, 6 and 10 connected to the robot’s infrared sensors and nodes 3, 7 and 11
connected to the visual sensors. Nodes 4, 5, 8 and 9 receive internal inputs
only. The phase differences θn − θn−1 , n = 2 . . . 12, plus a bias term,
are linearly combined by a weight matrix W and fed into two nonlinear
output units that have as activation function the sin function, which can be
interpreted as two output neurons that capture the ongoing network activity.
The activation of each output neuron is used to command the motors M1
and M2 .
the closest distance to an object). The camera provides a 36
degrees, 64 pixels gray-scale horizontal image from its field
of view. These 64 pixels are grouped into 3 mean inputs for
the system: the mean value of pixels 0 - 13 representing the
left reading, the mean value of pixels 24 - 39 representing
the central reading and the mean value of pixels 48 - 63
representing the right reading. The readings range from 50
to 175 - the first value representing the maximum perception
of a black stripe. In all experiments, a sensorimotor cycle
(time between a sensory reading and a motor command) lasts
400ms. The KiKS Khepera robot simulator was used (Storm,
2004); it simulates with great fidelity motor commands and
noisy sensory readings that are observed in the real robot.
4) Framework: The model studied in Experiment 1 was
adapted so that it could be applied to control a simulated
robotic agent. The framework, illustrated in Figure 6, is
composed of 12 fully connected oscillators, with some nodes
connected to the robot’s noisy sensors (1 sensor per node).
The rationale for a network with 12 nodes relates to richer
dynamical behaviour in the Kuramoto Model with this number of nodes (Popovych et al., 2005). The frequency of each
node is the sum of its natural frequency of oscillation, wn ,
and the value of the sensory input related to that node (0
if there is no input), scaled by a factor ǫn . The natural
where M = [M1 , M2 ]T is the motor state space, with M1
corresponding to the left motor command and M2 to the right
motor command.
In this way, the phase dynamics and the environmental
input to the robotic agent will determine its behaviour. It is
important to stress that nodes that receive no input participate
in the overall dynamics of the network, hence their natural
activity can modulate its global activity.
5) Task: The robot described in Section IV-A.3 has two
main objectives: it has to explore the environment whilst
avoiding collisions (O1 ) and it has to ensure that its battery
level remains above a threshold (O2 ), actively searching for
the recharging area otherwise. The environment is a square
arena with a recharging area represented by two light sources
located next to a black stripe tag (Figure 7(b)). Whenever the
robot’s light sensory readings are below 100, it is considered
to be inside the recharging area. The battery level BL
dynamics is given by Equation 11:
BL(t) − α(BL(t) − M in(BL)),
BL(t) + β(M ax(BL) − BL(t)),
where α and β control the battery consumption and recharge
rate, respectively, M in(BL) and M ax(BL) are the lower
and upper limit of BL (set here to 0 and 100, respectively),
and ROCA and RICA stand for Robot Outside Charging
Area and Robot Inside Charging Area.
The network consists of N = 12 neurons with initial
phases uniformly distributed in [0, 2π). Nodes 2, 6 and 10
are connected to the robot’s infrared distance sensors, and
nodes 3, 7 and 11 are connected to the camera sensors. We
set ǫn = 4 for n = 4, 8 and ǫn = −4 for n = 5, 9 (for
n = 2, 3, 6, 7, 10, 11, ǫn is evolved, 0 otherwise, see the next
section for details). We also adopted M = 3 (Equation 3),
which leads to the formation of 3 assemblies with 4 neurons
BL(t+1) =
Fig. 7. Scenarios used in Experiment 2. (a) Obstacle avoidance training scenario and the behaviour displayed by a successfully evolved individual. (b)
Task scenario. The arena has a recharging area represented by two light sources located next to a black stripe landmark (central top part of the figure).
Light grey and black trajectories show the robot’s behaviour when controlled by different assembly configurations.
in each, denoted (1, 2, 3, 4)-(5, 6, 7, 8)-(9, 10, 11, 12) (Figure
Whenever the battery level drops below 15 (t = tlow ),
an internal signal is generated which reorganizes the network in terms of neuronal synchronisation (Figure 8(b)).
This signal consists of an input lasting 400ms (the same
duration of a sensorimotor cycle) applied to nodes 4, 5, 8,
and 9, i.e., considering Equation 2, I4,5,8,9 (t) = 1 for
tlow ≤ t ≤ tlow + 0.4, 0 otherwise. Given the set up
of the network described in the previous paragraph, this
input shifts the phase of oscillators 4 and 8 and lags the
phase of oscillators 5 and 9 enough to move them from
their original basin of attraction to a neighbouring assembly.
The network final configuration is thus (1, 2, 3, 5)-(4, 6, 7, 9)(8, 10, 11, 12). Whenever the battery level increases above
95 (t = thigh ), an internal signal I4,5,8,9 (t) = −1 for
thigh ≤ t ≤ thigh + 0.4, 0 otherwise, brings the network
back to its original configuration (Figure 8(a)). Recall, from
the task description in the previous paragraph, that the goal
is to investigate if dynamic assembly formation can underpin
the coordination of different, possibly conflicting behaviours
in an autonomous agent.
Tasks are conflicting in the sense that the first (O1 ) requires
the agent to move and explore whilst minimizing sensory
readings (and hence avoiding collisions), whereas in the latter
(O2 ) it has to approach a certain area of the environment,
maximize the inputs from the light sensors (to recharge)
whilst suppressing its movement to increase the time spent
in the charging area (until the battery is recharged above the
6) Genetic Algorithm: We used a geographically distributed genetic algorithm with local selection and replacement (Husbands et al., 1998) to determine the parameters
of the system: the input weights ǫn ∈ [−0.5, 0.5], n =
2, 3, 6, 7, 10, 11, and the two output neurons’ weights
WN,o , o = 1, 2, with elements in the interval [−5, 5],
resulting in a genotype of length 30.
The network’s genotype consists of an array of integer
variables lying in the range [0, 999] (each variable occupies
a gene locus), which are mapped to values determined by the
range of their respective parameters. For all the experiments
in this paper, the population size was 49, arranged in a
7 × 7 toroidal grid. There are two mutation operators:
the first operator is applied to 20% of the genotype and
produces a change at each locus by an amount within the
[−10, +10] range according to a normal distribution. The
second mutation operator has a probability of 10% and is
applied to 40% of the genotype, replacing a randomly chosen
gene locus with a new value within the [0, 999] range in an
uniform distribution. There is no crossover.
In a breeding event, a mating pool is formed by choosing
a random point in the grid together with its 8 neighbours.
A single parent is then chosen through rank-based roulette
selection, and the mutation operators are applied, producing
a new individual, which is evaluated and placed back in
the mating pool in a position determined by inverse rankbased roulette selection. For further details about the genetic
algorithm, the reader should refer to Husbands et al. (1998).
During evolution, we adopted a shaping technique (Dorigo
& Colombetti, 1998; Bongard, 2011), in which the robot
is required to execute and succeed in one task environment
before proceeding to more complex scenarios. This technique
has been shown to improve the evolvability of controllers
in tasks that involve the accomplishment of many different
Therefore, considering the task previously described, the
first phase of evolution, Phase 1, consists of 800 iterations
of the algorithm where the fitness f is defined as the robot’s
ability to explore the environment whilst avoiding collisions
with the environment walls and obstacles (Equation 12, based
Fig. 8. Network assembly structure used in Experiment 2. Nodes with same colours are synchronised. (a) Configuration at the beginning of the experiment.
(b) Configuration after an internal signal (caused by the drop of the battery level below 15) changes the assemblies set-up.
on Floreano & Mondada (1994)). Note that there is no
influence of the battery level in this first stage of evolution.
Figure 7(a) depicts the training scenario.
f = V (1 − ∆v)(1 − i)
where V is the sum of the instantaneous rotation speed of the
wheels (stimulating high speeds), ∆v the absolute value of
the algebraic difference between the speeds of the wheels
(stimulating forward movement), and i is the normalized
value of the distance sensor of highest activation (stimulating
obstacle avoidance).
A generation is defined as 10 breeding events and the evolutionary algorithm runs for a maximum of 300 generations.
If, at the end of this first evolutionary process, the agent
attains a fitness above 0.4, it can proceed to the next phase.
During Phase 2 (scenario depicted in Figure 7(b)), robots
are evaluated according to their ability to avoid collisions
and the time they spend with the battery level below the
threshold, i.e. if the battery level is above 15, fitness is
scored following Equation 12, otherwise fitness is given by
the fraction of time it took the robot to recharge its battery
above 95. See Equation 13.
13 does not reward a specific sequence of actions, only the
final behaviour of the robot (reach the recharging area as fast
as possible and remain there until recharged). There is no
influence of the light or camera sensors in the calculations,
thus the robot has to associate the distance sensors and vision
information to find the area where recharge occurs.
B. Results
Robots successfully evolved to execute Phase 1 and Phase
2. Figure 7(a) portrays one of the evolved agents that navigates throughout the environment whilst avoiding collisions
(Phase 1). Notice that because the agent is surrounded by
walls and obstacles, sensory readings are nearly always
present, thus the maximum fitness obtained is less than the
maximum 1. Figure 7(b) shows the same agent after Phase 2
of evolution and Figure 9 shows the spiking activity (based
on the phase dynamics) for every node of the network, the
battery level, the distance and camera sensors, and the motor
commands. The next paragraphs will explore in details the
results of this latter phase.
At the beginning of the task, the agent wanders around
the environment in straight lines, adjusting its trajectory only
when faced by a wall. Notice from Figure 9 that both motor
outputs are close to the maximum value of 10 and only
V (1 − ∆v)(1 − i), if BL(t) ≥ 15
in response to the distance sensors’ stimuli - the
1 − tb /T,
if BL(t) < 15 and BL(t + τ ) <change
(13) motors remain unresponsive to changes in the camera input
where V , ∆v and i, and BL, are as described in Equation 12 (recall that the network receives input from all sensors at all
and 11, respectively; tb is the number of iterations the robot times, and there are no ontogenetic plasticity mechanisms).
spent with its battery level below 15 and T is the number Incidentally, the robot passes near the recharging area (grey
of iterations counting from the moment the battery dropped arrow near iteration 50), but because its battery level is
still above the threshold the predominant behaviour remains
below 15 until it reached a level above 95.
At each iteration of the trial, the corresponding fitness “explore and avoid collisions”.
value is calculated, and the final fitness is given by the mean
However, near iteration 300 the battery level drops below
fitness obtained across the whole trial. Notice that there is 15 (point A in Figures 7(b) and 9), which triggers an
a selective pressure towards agents that reach the recharging internal signal that reorganizes the network configuration.
area as fast as possible and remain in the area until the The agent now should stop exploring the environment and
battery is recharged. Also, the learned behaviour in Phase drive towards the recharging area as fast as it can to maximize
1 cannot be completely overwritten in Phase 2, as part of its fitness. Notice that at the moment this occurs, the robot
the evaluation function still accounts for the robot’s ability is far from the recharging area so it has to use its visual
to avoid collisions and explore the environment. Importantly, information to orient and move towards the correct direction.
the second part of the fitness function described in Equation The adopted strategy is to move in circles until the visual
Fig. 9. Experiment 2 variables dynamics. Downwards from the top: network raster plot (the dark and light grey shaded areas relate to different network
configurations, the small plot shows details of the phase dynamics, the black arrow shows how sensory stimulus modulate the ongoing dynamics, the grey
arrow points at a moment of assembly reorganisation); the battery level, with dashed lines indicating when the agent is within the recharging area (0 is
outside, 100 is inside); the distance sensors (0 when there is no obstacle, 1023 if very close to one); the camera sensors (175 if no black stripe is seen,
50 if all the camera pixels detect black); and the motor commands (positive values indicate forward movement, backward movement otherwise). Letters
A, B and C refer to the trajectories displayed in Figure 7(b).
Fig. 10. Phase dynamics portrayed as the phase difference γn,1 of each
node n to node 1. The small plot shows the moment of an assembly
reorganization (solid and dashed light grey and dark grey lines show the
change in assembly membership). Grey arrows point at examples of phase
modulations due to sensory stimuli, black arrows indicate moments of
assembly reorganisation. Notice how sensory stimuli modulate the ongoing
dynamics in the whole network but ultimately do not cause an assembly
stimulus (black landmark) is perceived, and then progress
in a straight line towards it. This can be seen in Figure 9,
with the consistent camera readings. As the robot approaches
the landmark, the distance sensory readings increase but
they don’t cause the same response as before the network
reorganization: the agent remains relatively still within the
recharging area until the battery is recharged and does not
display the characteristic turn around movement of obstacle
avoidance. After the battery is above 95, another internal
signal is triggered and assemblies are rearranged to their
previous state. The robot hence returns to explore and avoid
This same sequence of behaviour can be observed near
iteration 500 (point B in Figures 7(b) and 9). The turning
behaviour, brought about by differential wheel speeds, is
much more noticeable here, and although the robot is closer
to the recharging area, it does not have the visual stimulus
at the time the battery drops below 15. When the first visual
stimulus is perceived, the agent fixates on the landmark, and
the sensory readings increase as it slowly moves towards the
black stripe. A similar sequence of behaviour is displayed
as the task continues, but the following moments when the
battery drops below 15 occur when the agent has a visual
stimulus, therefore there is no need to move in circles before
heading towards the recharging area (points C).
Figure 10 depicts the activity of the assemblies during
the task, represented by the phase difference γn,1 of each
node n to node 1. The oscillators rapidly synchronise and
form three neuronal assemblies equally spaced according to
their phase differences (see Figure 8). Each assembly has
inputs from one distance sensor and one camera sensor. This
sensory stimuli modulate the ongoing network activity, causing small phase deviations from the respective assembly’s
mean phase (examples are indicated by light grey arrows
in Figure 10), yet all nodes remain within the basin of
attraction of their respective cluster - they do not change
their assembly membership. The small phase modulations
of each cluster are captured by both output neurons and
are responsible for adjusting the agent’s motor commands
and, consequently, its trajectory. Internal signals triggered
by the battery level dynamics change the assemblies original
arrangement (compare with Figure 9, top), and this new
phase relationship, together with the sensory modulation,
accounts for the change in the robot’s behaviour.
The relationship between the assemblies rearrangement
and the different behaviours displayed by the agent can be
seen by plotting the network’s phase differences together
with the corresponding motor outputs at every sensorimotor
time step. Because there are γi,i−1 = 11 phase differences,
we have to perform a dimension reduction to visualize the
system’s dynamics. This is done by projecting the original
phase differences into the first two principal components
calculated using a Principal Component Analysis (PCA)
(Jolliffe, 2002). A single time series is obtained from the
two motor commands by subtracting the left from the right
wheel commands. Figure 11(a) shows the results. Note that
there are two clearly discernible regions in the state space,
one comprising trial iterations 1 − 124, 356 − 503, 777 −
1039, and 1194 − 1465, and the other iterations 125 − 355,
504 − 776, 1040 − 1193, and 1466 − 1600. These regions
relate to different assembly configurations (see Figure 9, top,
and Figure 10), therefore rearranging the assemblies causes
movement in the state space of the network-motor system,
which has a direct correspondence with the behaviour of the
As pointed out, the assembly reconfiguration is the main
mechanism responsible for changing the way the robot
behaves, there are no other plasticity mechanisms and both
distance and visual sensors are always fed into the network.
The effects of the inputs (or their relevance to the behaviour
observed) vary depending on the assembly configuration.
Observe the black arrows near iterations 900 and 1300 in the
camera sensors panel in Figure 9. The battery level is above
15, the robot is exploring the environment and avoiding
collisions (notice the distance sensors dynamics), but it also
receives visual input. This input, however, does not affect the
ongoing behaviour (see the motors dynamics). To highlight
this effect, we conducted an information dynamics analysis,
exploring how information flows from sensors to motors and
from motors to sensors as the task progresses.
Figure 11(b) shows the transfer entropy between the
robot’s distance and camera sensors and its motors for the
duration of the trial, calculated according to Section IVA.1. To obtain the time series, we used a sliding window
containing data from the past 200 iterations, therefore note
that the results reflect a history of interactions and are not
an instantaneous measurement of information flow. More
specific, the sensors’ time series (3 infrared sensors and
3 camera sensors) are submitted to a principal component
analysis to perform a dimension reduction. The calculated
principal component and the original time series of each
sensor modality are used to create a single time series that
M −M
0 0
TE sm
TE ms
Transfer Entropy
1075 1250 1425 1600
TE ms
1075 1250 1425 1600
Fig. 11. (a) System dynamics depicted by the projection of the 11 phase
differences γi,i−1 , i = 2 . . . 12, of the 12 node network into their first two
principal components (P C1 and P C2 ), and the motor output represented by
the difference between the values of M1 and M2 (Equation 10). Solid and
dashed lines relate to different iteration intervals. Notice that, whenever there
is an assembly rearrangement (Figures 9 and 10), there is a corresponding
shift in the network-motor state space region, which relates to different
behaviours displayed by the robot. (b) Transfer Entropy between the distance
sensors time series and the motors time series (top panel), and between the
camera sensors time series and the motors time series (bottom panel). A
sliding window of length 200 iterations is used to obtain each time series at
every iteration of the Transfer Entropy analysis. Results are smoothed using
a Gaussian filter with time constant 0.08.
captures the most significant features of the multidimensional
input space. The motor commands are also combined to
generate a single time series by subtracting the value of
the left wheel command from the right wheel command.
This data were then discretised into 6 equiprobable states
and finally the Transfer Entropy is calculated. We performed
a series of analyses with different parameter choices, and
although there were differences in the values obtained, the
overall qualitatively aspect of the curves was maintained.
In the top panel, the information flow from distance
sensors to the motors (or, in other words, the causal influence from distance sensors to motor commands) oscillates throughout the task and has peaks between iterations
375 − 550, 900 − 1075 and 1300 − 1500. In the bottom
panel, the information flow from visual sensors to the
motors also oscillates throughout the task and has peaks
between iterations 375 − 550, 700 − 900 and 1100 − 1400.
Comparing these with Figure 9, one can see that there is
a relationship between the distance and camera sensors,
and the respective information flows. This relationship is
due to complex brain-body-environment interactions and not
only due to the presence of sensory stimulus, as e.g. one
can verify observing iterations 500, 900 and 1350 in the
camera sensors plot (Figure 9) and the respective information
flow plot: although there are variations in the visual input,
there is no corresponding increase in the transfer entropy
values. Therefore, sensory inputs may or may not affect the
motor commands, and the current assembly configuration
will modulate this interaction.
The information flow dynamics offers another perspective
in the robot’s behaviour analysis. Notice that the information
flow magnitude is nearly twice as much in the bottom panel
as in the top panel. We saw in the previous dynamics and
behaviour analyses that when the battery drops below 15,
the robot moves towards the recharging area, but it does not
display the otherwise natural obstacle avoidance behaviour
when it eventually finds a wall. The transfer entropy analysis
highlights that the visual information is the main source
of behaviour modulation even though the distance sensors
still have some influence. This is clear between iterations
1075 − 1250: the visual information flow (bottom panel)
increases as the robot approaches the recharging area (higher
visual input), and upon finding the wall there is an increase
in the flow from motors to sensors in the top panel. This
means that the robot’s trajectory, mainly influenced by the
visual inputs, determine the incoming sensory readings - the
robot actively “produces” its inputs - whilst the information
flow from distance sensors to motors decrease, meaning that
there is little influence of the distance sensors in the robot’s
Finally, to stress the relevance of assembly reorganization
in the evolutionary process, observe Figure 12. It shows the
values at the end of Phase 1 and Phase 2 of evolution of the
weights of the output unit neurons and of the input weights of
nodes that have sensory input (see Figure 6). Notice that there
are just minor adjustments in the value of a few parameters,
most of them remain unchanged as evolution progress from
one phase to another. This further supports the relevance
and flexibility of dynamic assembly reorganization in multiobjective tasks.
It is now established that synchronisation mechanisms and
dynamic assembly formation in neuronal networks have a
relationship with cognitive processes and behaviour; however, the underlying computational functions and interplay
with behaviour are still to be uncovered. In this work,
we conducted experiments both in supervised and unsupervised learning scenarios exploring concepts drawn from the
“binding-by-synchrony” hypothesis, which considers neuronal assembly computations from a spike time perspective.
In fact, there is a growing body of literature attesting that
Fig. 12. Values of the weights of the output unit neurons (panels (a) and (b)), and of the input weights ǫn of nodes that have sensory input (panel (c)),
at the end of Phase 1 (solid black) and Phase 2 (dashed black) of evolution. Values are normalised between 0 and 1 to improve visualisation. See Figure
6 for details.
neuronal codes based solely on spike rates underperform or
do not contribute in a variety of cognitive tasks (Borst &
Theunissen, 1999; Carmena et al., 2003; Jacobs et al., 2009;
Rabinovich et al., 2012).
The neuronal network model used is inspired by the
Kuramoto model of coupled phase oscillators, and allows
one to fine tune the network synchronisation dynamics and
assembly configuration. The model has an intrinsic, ongoing oscillatory activity that can only be modulated - not
determined - by external stimuli, in contrast with models
which consider a static system with responses elicited only
by stimulus onset. As reiterated throughout this work, cognitive processes unfold over time and therefore cannot rely
only on external events. Also, one can precisely determine
the number and constitution of assemblies in the model.
Although evidence points at assembly formation as a result of
emergent processes, and several models capture this property
(Izhikevich, 2006; Burwick, 2008; Ranhel, 2012), it is hard to
foresee or design how the network will self-organise; hence,
the model presented here contributes to studies which require
a consistent emergent configuration and studies focused on a
systematic exploration of different synchronisation regimes.
In Experiment 1, a supervised learning task, we studied
the influence of the number and size of neuronal assemblies
in a spike pattern classification task. The input spike patterns
and the network phase dynamics both had roughly the same
spike count across the task, similar to what has been observed
in real cortical neuronal ensembles (Carmena et al., 2003).
A linear readout unit generated the circuit outputs based on
the state of the network nodes at a particular time, which
resembles the approach adopted in brain-machine interface
studies (Lebedev et al., 2005; Hatsopoulos & Donoghue,
2009) but is also suggested as a more appropriate form to
understand assembly activity (Buzsaki, 2010). Although it
is still not clear how the temporal relations in the brain
are organized, and thus how reading the network state at
a predetermined time could be justified, there are some
possible solutions that may have evolved in natural brains: a
redundancy in the circuitry may exist so that at any time an
event occurs, or a classification task is required, an output
is produced (Ranhel, 2012); there can be an interaction of
the external rhythms with internally generated ones, forcing
synchronised firing events to occur in strict time windows
(Masquelier et al., 2009; Kopell et al., 2010); attention
mechanisms may also interfere and promote phase resetting
(Steinmetz et al., 2000; Lakatos et al., 2008).
Considering a network with a total number of neurons
equal to 80 ± 4, our results show that performance boosts
as we increase the number of assemblies, and that can be
predicted up to a certain extent by the computational power
and generalisation capability of the system following the
same procedure described in Legenstein & Maass (2007),
although in our case it is possible that simply using the
difference between these two measurements may not be the
most appropriate form of combining them; this is an open
problem which goes beyond the scope of this work. A further
analysis, which varied the number and size of assemblies,
revealed that the first impacts more on performance than the
latter, a fact that can be attributed to the increased variability
of possible network states due to a larger number of emergent
clusters rather than fewer but larger assemblies. Also, the
system presents a saturation in performance with respect to
the number of clusters. Therefore, the results indicate that
simply increasing the number of neurons or assemblies in
the system does not necessarily originate a correspondent
increase in performance. A similar phenomena is described
in neuronal assembly physiology as “the neuronal mass
principle” (Nicolelis & Lebedev, 2009), which states that
a minimal number of neurons is needed in a neuronal
population to stabilize its information capacity (captured by a
readout unit) at a satisfactory level. Reducing the number of
neurons causes an increasingly sharp drop in the information
capacity of this population, whereas increasing the number
of sampled neurons above a certain level does not increase
the accuracy of predictions (Carmena et al., 2003; Lebedev
et al., 2008).
Also, the results showed that, in our model, most of
the neuronal architectures were highly redundant, most of
the neurons in the higher performance configurations presented independent activity, and that increasing the number
of neurons in a network with fixed number of assemblies
increased the redundancy. All these findings resemble the
results obtained in real cortical experiments, therefore a few
remarks should be made: first, real neuronal ensembles are
highly redundant, and that can be associated with resistance
to error and natural mechanisms of probability distribution
estimation (Barlow, 2001; Szczepanski et al., 2011); second,
neuronal independence (as observed in our results) can be
linked to code efficiency because the information capacity
of individual neurons is not compromised by redundant
scenarios (Schneidman et al., 2003); and third, there is still
a lack of studies comparing the information flow dynamics
due to neuronal interactions and due to single neurons alone
- attesting the time-scales of the interactions as well as
spurious effects such as averaging is still work in progress
(Reich et al., 2001; Narayanan et al., 2005).
The computational power analysis emphasized that multiple readout units could be trained to perform different
classification tasks based on the same network state. In
contrast, to conclude Experiment 1, we investigated whether
the system could cope with multiple classification tasks
relying only on a manipulation of the phase dynamics by
means of an internally generated signal, employing the
same readout unit without any plasticity mechanisms. To
support the approach, there are clinical studies suggesting
that intracortical electrical stimulation can induce cortical
plasticity (Jackson et al., 2006), but functional plasticity can
also be obtained faster as a result of attentional processes
(Steinmetz et al., 2000; Lakatos et al., 2008; Schroeder &
Lakatos, 2009). Our results show that the system can be
trained in multiple classification tasks upon rearrangement
of assembly configuration.
Although the results of the first experiment show that such
a temporal code carries information and suggest that it can be
exploited in a variety of tasks, it is challenging to determine
to what extent the brain uses a temporal code. Moreover,
there is evidence that the neuronal activity evoked by the
body’s sensorimotor interactions with the environment differs
from the activity evoked by passive stimulus (Lungarella &
Sporns, 2006; Eliades & Wang, 2008). Hence, research on
temporal neuronal codes and assembly formation benefit if
linked with behavioural studies (Engel, 2010; Panzeri et al.,
2010); in this sense, evolutionary robotics (ER) emerges as
a suitable technique to combine both approaches (Floreano
et al., 2008; Floreano & Keller, 2010).
In Experiment 2, an ER unsupervised learning task, we
evolved a simulated robotic agent, controlled by a variation
of the system investigated in Experiment 1, to solve multiple
tasks depending on its battery state. The results showed that
the evolved framework together with the dynamic assembly formation can generate minimally cognitive behaviours.
When working with increasingly complex tasks, the changes
in the parameters of the system are relatively small, which indicate that the different assemblies formed dynamically also
facilitate the evolutionary process. Finally, we highlighted
the context-based neuronal dynamics showing that the phase
space formed by the motors readings and the nodes’ phases
have different orbits due to changes in assembly organisation,
and an analysis of the information flow in the network reveals
that such changes modulate the influence of the inputs in the
robot’s behaviour (determined by the motor commands).
Taken together, Experiments 1 and 2 employed information theory and decoding methods to provide further evidence
that the dynamic formation of assemblies and the relative
neuronal firing times can mediate processes involving the
classification of spike patterns, and can selectively modulate
the influence of external signals in the current network
activity. Ultimately, there is no guarantee that the brain
makes usage of a time-based decoding procedure, neither
that it is able to exploit the information content revealed
by the synergy analysis and the transfer entropy approach;
nevertheless, it may shed light on aspects of brain-bodyenvironment interactions and provide upper bounds on code
efficiency when testing hypothesis (Quiroga & Panzeri, 2009;
Jacobs et al., 2009).
There are several directions for future research. First, it is
common to construct the Kuramoto model (and its variations)
having additive noise at the input level equivalent to noise
applied at the network level (Acebrón et al., 2005). Based
on Equation 2, the following equation shows the usual form
of the Kuramoto model with inputs In (t) and noise χn (t):
g(θn − θm )+In (t)+χn (t), n = 1, . . . , N,
N m=1
In this sense, Experiments 1 and 2 had a subset of
noisy neurons (only neurons that had inputs). Given the
relevance of widespread noise to many neuronal and cognitive phenomena (Rolls & Deco, 2010), future investigations
should explore in depth its impact on the framework. As
a preliminary study, we have run two further simulations
of Experiment 1 adding Gaussian noise of zero mean and
standard deviation σ to all nodes of the network. The
results are presented in Figure 13. Notice that classification
performance falls with increasing noise magnitude (an effect
also present in Figures 2(c) and 4(b)), but the trend observed
in our original results is kept and higher performance levels
are obtained in architectures with more assemblies. Thus, at
least for this experiment, noise applied to all neurons alters
the classification performance in a quantitative rather than a
qualitatively way.
How would additive noise affect measures of redundancy
and synergy? The intuition that noisy scenarios are better tackled with redundant architectures is justified - there
are works showing that cortical circuits, which operate in
an intrinsically noisy environment, are highly redundant
(Narayanan et al., 2005; Szczepanski et al., 2011). However,
there is criticism regarding the interpretation of information
theoretical measurements such as redundancy (Schneidman
et al., 2003; Latham & Nirenberg, 2005), as well as findings
showing predominantly synergistic or independent activity
in neuronal circuits, instead of redundancy, depending on
factors such as which area and which neurons are recorded
or which kind of task is performed (Reich et al., 2001). Addi-
θ̇n = ωn +
Fig. 13. Effect of Gaussian noise of zero mean and standard deviation
σ applied to all nodes of the network in the performance of the system.
Results are mean values over 20 different simulations, and shaded areas are
the 95% confidence interval.
tionally, as shown by Szczepanski et al. (2011), neurons can
dynamically switch their interactions during the execution of
the task, thus synergetic, independent, or redundant activity
may be masked by averaging processes. Finally, redundancy
and synergy are found to be largely influenced by the network
architecture and the decoding unit used (Schneidman et al.,
2003). The conclusion is that redundancy is not necessary for
good performance in noisy scenarios, but mostly important
it depends largely on the experimental paradigm used.
One limitation encountered in the methods used in Experiment 1, chosen for their ability to assess computational
performance in generic neuronal microcircuits independently
of task paradigm, is that in some circumstances noisy neurons may make the computational power and generalization
capability analyses inconclusive due to state matrices having
complete rank most of the time (Legenstein & Maass, 2007).
This was not an issue in previous works that used these
methods (e.g. (Maass et al., 2005; Legenstein & Maass,
2007)) because emergent properties of the neural architecture
resulted in highly silent networks with dynamics that were
marginally affected by noise; conversely, the model in this
article is composed of self-sustained oscillators which are
always active. Considering that silence in the brain is still
a point of much controversy (Shoham et al., 2006), the
applicability of the methods used in Experiment 1 to a
variety of problems and neural architectures remains an open
Another possible future extension to the model would
be to substitute the continuously coupled oscillators used
in this work with pulse-coupled oscillators, which are not
only a more biologically plausible abstraction of neuronal
synaptic activity, but also present rich metastable dynamics
that can be exploited to compute arbitrary logic operations
(Neves & Timme, 2009, 2012; Wildie & Shanahan, 2012).
However, the network cluster states in the works just cited are
emergent processes found numerically (despite the switching
dynamics being controllable), whilst the model studied in this
work can be systematically tuned into predefined assembly
Finally, it would be interesting to extend the model to
include multiple assembly membership (i.e. entitle a given
neuron to participate simultaneously in two or more assemblies), as it has been shown to enhance the computational
power of a neuronal circuit (Izhikevich, 2006).
To conclude, implementing the methods or the experiments
described in this work in a biological network is impractical
at the moment for limitations in both recording and stimulation technologies: the best technologies are able to record
and stimulate a limited number of neurons. However, more
important than trying to implement the methods or experiments in a biological network are the insights and future
work opportunities we gain. There are many open questions
in neuroscience regarding neural assemblies, their properties,
and relationship with behaviour. This very simple model,
based on a model that is being increasingly applied to study
neuroscience problems (the Kuramoto model), has shown
promising results in supervised and unsupervised learning
tasks. The point to stress is not solely performance levels support vector machines, for instance, would surely excel in
Experiment 1, attaining far better results than our approach
- but the ability to solve relatively complex tasks mimicking
mechanisms that current research suggests is exploited by
the brain, namely neuronal assembly dynamics. Therefore,
a better comprehension of the framework, its limitations
and possible extensions, and ultimately understanding of the
computational properties of neuronal assembly dynamics,
whether at solving data mining tasks or as part of novel
behaviour generation mechanisms, should precede biological
RM was partly funded by a Sussex TA scholarship and
a de Bourcier scholarship. Special thanks to Bruno Santos,
James Thorniley and other members of the CCNR for useful
discussions relating to this work. We would like to thank two
anonymous reviewers for their valuable comments.
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