A spiking neural network architecture for nonlinear function approximation

Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural netw...

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Bibliographic Details
Published in:Neural networks 2001-07, Vol.14 (6), p.933-939
Main Authors: Iannella, Nicolangelo, Back, Andrew D.
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Animals
Central Nervous System - physiology
Delays
Humans
Integrate-and-fire units
Nerve Net - physiology
Neural architecture
Neural Networks (Computer)
Neurons - physiology
Nonlinear Dynamics
Nonlinear function approximation
Spiking neurons
Synaptic Transmission - physiology
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Summary:Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural networks are of interest both from a biological point of view and in terms of a method of robust signaling in particularly noisy or difficult environments. It is important to consider networks based on spike trains. A basic question that needs to be considered however, is what type of architecture can be used to provide universal function approximation capabilities in spiking networks? In this paper, we propose a spiking neural network architecture using both integrate-and-fire units as well as delays, that is capable of approximating a real valued function mapping to within a specified degree of accuracy.
ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(01)00080-6