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

Abstract : 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 has effect on 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 (2006). The model emphasizes the computational capabilities of this concept.
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Conference papers
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Submitted on : Thursday, September 7, 2017 - 3:09:11 PM
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  • HAL Id : hal-01583544, version 1

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Hélène Paugam-Moisy, Regis Martinez, Samy Bengio. A supervised learning approach based on STDP and polychronization in spiking neuron networks. 15th European Symposium on Artificial Neural Networks, ESANN'07, Apr 2007, Bruges, Belgium. pp.427-432. ⟨hal-01583544⟩

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