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ESANN
2007

A supervised learning approach based on STDP and polychronization in spiking neuron networks

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A supervised learning approach based on STDP and polychronization in spiking neuron networks
We propose a network model of spiking neurons, without preimposed 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 effects the synaptic delays linking the network to the output neurons, with classification as a goal task. The network processing and the resulting performance are completely explainable by the concept of polychronization, proposed by Izhikevich [1]. The model emphasizes the computational capabilities of this concept.
Hélène Paugam-Moisy, Régis Ma
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Hélène Paugam-Moisy, Régis Martinez, Samy Bengio
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