Delay learning and polychronization for reservoir computing

We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised l...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 71; no. 7; pp. 1143 - 1158
Main Authors Paugam-Moisy, Hélène, Martinez, Régis, Bengio, Samy
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2008
Elsevier
Subjects
Classification
Computer Science
Margin criterion
Polychronization
Programmable delay
Reservoir computing
Spiking neuron network
STDP
Synaptic plasticity
Spiking neuron network
Programmable delay
Reservoir computing
Synaptic plasticity
Classification
STDP
Polychronization
Margin criterion
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Summary:We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245–282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2007.12.027