Telematik 2/02 - Institut für Grundlagen der Informationsverarbeitung ...

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rent readout maps were chosen such that each
one had a focus on another aspect of the input.
Results of computer simulations show that such
models of neural microcircuits can indeed
function as an optimal liquid since all considered
tasks can be performed with quit high accuracy.
Furthermore the issue how different connectivity
structures of neural microcircuits affect the
„usefulness of the liquid“ was explored. It turned
out that there exists some optimal regime of
connectivity parameters for the neural
microcircuit. Optimal in the sense that inputs
are integrated (mixed) into the liquid state such
that the considered computational tasks were
performed with maximal accuracy. In other
parameter ranges either the inputs are not
integrated (mixed) at all and hence no information
about past inputs is available in the liquid state,
or the circuits exhibit a chaotic behavior. If the
circuit exhibits chaotic behavior even the smallest
change in the input (which causes only small
changes in the target output) causes the liquid to
end up in a totally different state, and hence the
readout is usually not able to map such totally
different states to rather identical outputs.
These approach provides a new platform for
investigating through computer simulations the
structure and function of neural microcircuits,
for example to explore the computational
advantages of the extreme complexity and
diversity of neurons, synapses, and specific
connectivity patterns found in the microcircuity
of the brain.
Computations on spike trains in feedforward
networks
Whereas in [1] recurrent circuits of spiking
neurons were considered, in [8] a much simpler
type of liquid is investigated; see Fig. 6.
The liquid was just an array of biologically
realistic synapse models. It is known that
synapses are quite heterogeneous, i.e. produce
quit different outputs for the same input spike
train, even within a single neural circuit. In fact
these differences, which also vary from synapse
to synapse, can serve as short term memory for
a neural system, since the amplitude of the
postsynaptic response for the current spike
contains information about the preceding part of
the spike train. Hence the parameters for the
model synapses employed as liquid were drawn
from distributions in accordance with empirical
results (see e.g. [9]). The readout was
implemented as another pool of spiking neurons;
as in [1]. However, to demonstrate that the LSM
approach allows to construct circuits which
implement given mappings from input spike
trains to output spike trains the firing activity of
Fig.6: The network of spiking neurons used in
[8]. The liquid consists of an array of biologically
realistic model synapses. As in [1] the readout is
another population of neurons. Its firing activity
is explicitly transformed into a spike train by the
output neuron v. Networks of this type can be
trained to behave like any given small finite state
machine.
the readout pool was explicitly transformed into
a spike train using an extra single output neuron;
see Fig. 6.
The question explored in [8] was to what extent
such a simple neural circuit can carry out
arbitrarily complex computations on spike trains.
It was demonstrated by computer simulations
that such a simple neural circuit can be trained to
behave like any given small finite state machine.
Finite state machines (FSM) are frequently used
in computer science as general purpose models
for computations on time series. A FSM usually
gets a bit-string as input and produces as online
output again a bit-string (in our terminology it is
a filter which operates on bit strings). 3
Since the finite state machines which were used
as target for the training were drawn randomly,
this result demonstrates impressively the
generality of the LSM approach. Furthermore
these results show that the heterogeneity of
synapses enables a simple network of spiking
neurons to perform quite complex computational
tasks. This might be understood as a hint why
the diversity of neurons and synapses found in
real neural system may be computationally
advantageous.
Computations on analog time series
The main idea of the LSM was independently
discovered in [2]. The author of [2] considered
an artificial recurrent neural network (see e.g.
[3]) as liquid. 4
3 Note that bit-strings can easily converted to
spike trains and vice versa. Hence it is possible
to compare a FSM and the network depicted in
Fig. 6.
4 The network they used consists of N units. At
time step t unit i computes the output
xi
( t + 1)
= tanh ( ∑ w ( ) ) where w is a para-
j ij x j t
ij
meter describing the connection strength
between unit i and j.
As a readout function he used a single unit of the
same type as the liquid is composed of. Training
of this single output unit amounts to a linear
regression problem, which is one of the simplest
„learning“ algorithms one can think of.
The perspective taken is mainly that of
mathematics and engineering, where a recurrent
network is seen as a computational device for
realizing a dynamical system (the liquid). Hence
the target filters (learning tasks considered) are
from a different nature as presented so far. For
example it is demonstrated that the quasibenchmark
task of learning a chaotic attractor
can be accomplished by using the training
approach depicted in Fig. 4. Another challenging
task which could be solved in this way is the
design of a controller for a pendulum (private
communication; not reported in [2]).
The most appealing features of this learning
algorithm for recurrent neural networks (which
emerges from the LSM model) compared to
others (see e.g. [3]) are its simplicity and
robustness. These features stem from the fact
that only the parameters of a single unit are
trained and that this training can be done using
linear regression.
Summary
We have discussed a novel computational model
for analysing computations on time series in
particular on spike trains: the „liquid computer“
or more formally the liquid state machine. In
contrast to models that are inspired by the
architecture of the current generation of artificial
computing machinery it requires no storage of
information in stable states. We have formulated
two simple conditions, the separation property
and the approximation property, which together
endow liquid computers with virtually unlimited
computational power in the time series domain.
On a more practical level the LSM approach
provides a general method for training a computer
model of a neural microcircuit consisting of
biologically realistic models for neurons and
synapses, to carry out basically any given
computation on spike trains. Furthermore this
general method can also be applied in the domain
of artificial neural networks [2] and yields a simple
and robust learning algorithm. Applications of
this general method were discussed in Section 3.
To make the work on the LSM approach more
accessible to other people we are currently setting
up web resources (accessible via
www.lsm.tugraz.at) regarding data, computer
models, and analysis tools for neural
microcircuits, which will be functional by fall
2002. In this webpage we will collect data on the
anatomy and physiology of neural microcircuits
42 Telematik 1/2002