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The expression 'Neural Networks' refers traditionally to a class of
mathematical algorithms that obtain their proper performance while
they 'learn' from examples or from experience. As a consequence,
they are suitable for performing straightforward and relatively
simple tasks like classification, pattern recognition and
prediction, as well as more sophisticated tasks like the processing
of temporal sequences and the context dependent processing of
complex problems. Also, a wide variety of control tasks can be
executed by them, and the suggestion is relatively obvious that
neural networks perform adequately in such cases because they are
thought to mimic the biological nervous system which is also
devoted to such tasks. As we shall see, this suggestion is false
but does not do any harm as long as it is only the final
performance of the algorithm which counts. Neural networks are also
used in the modelling of the functioning of (sub systems in) the
biological nervous system. It will be clear that in such cases it
is certainly not irrelevant how similar their algorithm is to what
is precisely going on in the nervous system. Standard artificial
neural networks are constructed from 'units' (roughly similar to
neurons) that transmit their 'activity' (similar to membrane
potentials or to mean firing rates) to other units via 'weight
factors' (similar to synaptic coupling efficacies)."
This book has the unique intention of returning the mathematical
tools of neural networks to the biological realm of the nervous
system, where they originated a few decades ago. It aims to
introduce, in a didactic manner, two relatively recent developments
in neural network methodology, namely recurrence in the
architecture and the use of spiking or integrate-and-fire neurons.
In addition, the neuro-anatomical processes of synapse modification
during development, training, and memory formation are discussed as
realistic bases for weight-adjustment in neural networks. While
neural networks have many applications outside biology, where it is
irrelevant precisely which architecture and which algorithms are
used, it is essential that there is a close relationship between
the network's properties and whatever is the case in a
neuro-biological phenomenon that is being modelled or simulated in
terms of a neural network. A recurrent architecture, the use of
spiking neurons and appropriate weight update rules contribute to
the plausibility of a neural network in such a case. Therefore, in
the first half of this book the foundations are laid for the
application of neural networks as models for the various biological
phenomena that are treated in the second half of this book. These
include various neural network models of sensory and motor control
tasks that implement one or several of the requirements for
biological plausibility.
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