Paper available: A Spiking Neuron Model: Applications and Learning

[Chris Christodoulou]

Dear all,

I would like to announce the availablility of the following paper
that will appear in one of the forthcoming issues of Neural Networks.
A preprint of the paper can be downloaded from:

http://www.dcs.bbk.ac.uk/~chris/papers/nn_pprnt.pdf
or
http://www.tech.plym.ac.uk/soc/staff/GuidBugm/pub/nn2002.pdf

..............................................................
Title:  A Spiking Neuron Model: Applications and Learning
Authors: Chris Christodoulou, Guido Bugmann and Trevor G. Clarkson
(in press, Neural Networks)

Abstract
This paper  presents a biologically-inspired, hardware-realisable spiking
neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI).
The dynamic applications of the model as well as its applications in
Computational Neuroscience are demonstrated and a learning algorithm
based on postsynaptic delays is proposed. The TNLI incorporates temporal
dynamics at the neuron level by modelling both the temporal summation of
dendritic postsynaptic currents which have controlled delay and duration
and the decay of the somatic potential due to its membrane leak. Moreover,
the TNLI models the stochastic neurotransmitter release by real neuron
synapses (with probabilistic RAMs at each input) and the firing times
including the refractory period and action potential repolarisation.  The
temporal features of the TNLI make it suitable for use in dynamic
time-dependent tasks like its application as a motion and velocity
detector system presented in this paper.  This is done by modelling the
experimental velocity selectivity curve of the motion sensitive H1 neuron
of the visual system of the fly.  This application of the TNLI indicates
its potential applications in artificial vision systems for robots.  It
is also demonstrated that Hebbian-based learning can be applied in the
TNLI for postsynaptic delay training based on coincidence detection, in
such a way that an arbitrary temporal pattern can be detected and
recognised.  The paper also demonstrates that the TNLI can be used to
control the firing variability through inhibition; with 80% inhibition to
concurrent excitation, firing at high rates is nearly consistent with a
Poisson-type firing variability observed in cortical neurons.  It is also
shown with the TNLI, that the gain of the neuron (slope of its transfer
function) can be controlled by the balance between inhibition and
excitation, the gain being a decreasing function of the proportion of
inhibitory inputs.  Finally, in the case of perfect balance between
inhibition and excitation, i.e., where the average input current is zero,
the neuron can still fire as a result of membrane potential fluctuations.
The firing rate is then determined by the average input firing rate.
Overall this work illustrates how a hardware-realisable neuron model can
capitalise on the unique computational capabilities of biological neurons.

Keywords:  Spiking Neuron Model; Temporal Noisy-Leaky Integrator;
Motion detection; Directional selectivity; postsynaptic delay learning;
temporal pattern detection; high firing variability; inhibition.


* * *
Dr Chris Christodoulou		chris at dcs.bbk.ac.uk
				Chris.Christodoulou at kcl.ac.uk
School of Computer Science and Information Systems
Birkbeck College, University of London
Malet Street, London WC1E 7HX, UK
Tel. (+44) 20-7631 6718, Fax (+44) 20-7631 6727