Supervised Learning in Multilayer Spiking Neural Networks.

Sporea, Ioana and Gruning, Andre (2013) Supervised Learning in Multilayer Spiking Neural Networks. Neural Computation, 25 (2), 2. pp. 473-509.

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Abstract

We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Computing Science
Authors :	Name Email ORCID Sporea, Ioana i.sporea@surrey.ac.uk Gruning, Andre A.Gruning@surrey.ac.uk
Date :	2013
DOI :	10.1162/NECO_a_00396
Copyright Disclaimer :	Copyright 2013 Massachusetts Institute of Technology
Related URLs :	Publisher
Additional Information :	Available Neural Computation, 25, 473-509, http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00396#.VGs1GvmsV8E © 2013 The MIT Press
Depositing User :	Symplectic Elements
Date Deposited :	18 Nov 2014 12:02
Last Modified :	19 Jun 2018 06:05
URI:	http://epubs.surrey.ac.uk/id/eprint/806443

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