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.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Hélène Paugam-Moisy, Régis Martinez, Samy Bengio
• Keywords: ESANN 2007, Network Model, Neural Networks, Supervised Learning Algorithm, Unsupervised Learning Mode