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Evolving spiking neural network—a survey

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Abstract

This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We summarize the functioning of the method, discuss several of its extensions and present a number of applications in which the eSNN method was employed. We focus especially on some proposed extensions that allow the processing of spatio-temporal data and for feature and parameter optimisation of eSNN models to achieve better accuracy on classification/prediction problems and to facilitate new knowledge discovery. Finally, some open problems are discussed and future directions highlighted.

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Notes

  1. http://www.theneucom.com

  2. In Schliebs et al. (2009a) a slightly different definition of the firing threshold was introduced that deviates from the original description presented in Wysoski et al. (2006b).

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Authors and Affiliations

  1. KEDRI, Auckland University of Technology, Auckland, New Zealand

    Stefan Schliebs & Nikola Kasabov

  2. Institute for Neuroinformatics, ETH/UZH, Zurich, Switzerland

    Nikola Kasabov

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  1. Stefan Schliebs
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  2. Nikola Kasabov
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Correspondence to Stefan Schliebs.

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Schliebs, S., Kasabov, N. Evolving spiking neural network—a survey. Evolving Systems 4, 87–98 (2013). https://doi.org/10.1007/s12530-013-9074-9

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