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Supervised Learning in Multilayer Spiking Neural Networks

Sporea, Ioana and Gruning, Andre (2012) Supervised Learning in Multilayer Spiking Neural Networks arXiv.

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Abstract

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

Item Type: Article
Authors :
NameEmailORCID
Sporea, Ioanai.sporea@surrey.ac.ukUNSPECIFIED
Gruning, AndreA.Gruning@surrey.ac.ukUNSPECIFIED
Date : 10 February 2012
Identification Number : 10.1162/NECO_a_00396
Copyright Disclaimer : This is an arXiv publication
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 16 Aug 2017 11:24
Last Modified : 16 Aug 2017 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/713370

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