Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition

Authors:
Stefan Schliebs;Haza Nuzly Abdull Hamed;Nikola Kasabov
Affiliations:
KEDRI, Auckland University of Technology, New Zealand;KEDRI, Auckland University of Technology, New Zealand;KEDRI, Auckland University of Technology, New Zealand
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

Evolving spiking neural networks (eSNN) are computational models that are trained in an one-pass mode from streams of data. They evolve their structure and functionality from incoming data. The paper presents an extension of eSNN called reservoir-based eSNN (reSNN) that allows efficient processing of spatio-temporal data. By classifying the response of a recurrent spiking neural network that is stimulated by a spatio-temporal input signal, the eSNN acts as a readout function for a Liquid State Machine. The classification characteristics of the extended eSNN are illustrated and investigated using the LIBRAS sign language dataset. The paper provides some practical guidelines for configuring the proposed model and shows a competitive classification performance in the obtained experimental results.