A supervised learning approach based on STDP and polychronization in spiking neuron networks
| Type of publication: | Conference paper |
| Citation: | paugam:esann:2007 |
| Booktitle: | European Symposium on Artificial Neural Networks, ESANN |
| Year: | 2007 |
| Note: | IDIAP-RR 06-54 |
| Crossref: | paugam:rr06-54: |
| 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~\cite{Izh06NComp}. The model emphasizes the computational capabilities of this concept. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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