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Proceedings Paper

Networks of spiking neurons that compute linear functions using action potential timing
Author(s): Berthold Ruf
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Paper Abstract

For fast neural computations within the brain it is very likely that the timing of single firing events is relevant. Recently Maass has shown that under certain weak assumptions a weighted sum can be computed in temporal coding by leaky integrate-and-fire neurons. This construction can be extended to approximate arbitrary functions. In comparison to integrate-and-fire neurons there are several sources in biologically more realistic neurons for additional nonlinear effects like e.g. the spatial and temporal interaction of postsynaptic potentials or voltage-gated ion channels at the soma. Here we demonstrate with the help of computer simulations using GENESIS that despite of these nonlinearities such neurons can compute linear functions in a natural and straightforward way based on the main principles of the construction given by Maass. One only has to assume that a neuron receives all its inputs in a time interval of approximately the length of the rising segment of its excitatory postsynaptic potentials. We also show that under certain assumptions there exists within this construction some type of activation function being computed by such neurons. Finally we demonstrate that on the basis of these results it is possible to realize in a simple way pattern analysis with spiking neurons. It allows the analysis of a mixture of several learned patterns within a few milliseconds.

Paper Details

Date Published: 22 March 1999
PDF: 11 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343071
Show Author Affiliations
Berthold Ruf, Technische Univ. Graz (Germany)

Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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