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 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~\cite{Izh06NComp}. The model emphasizes the computational capabilities of this concept.


Presented at:
European Symposium on Artificial Neural Networks, ESANN
Year:
2007
Note:
IDIAP-RR 06-54
Laboratories:




 Record created 2010-02-11, last modified 2018-01-28

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