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Delay learning and polychronization for reservoir computing

Hélène Paugam-Moisy 1 Regis Martinez 1 Samy Bengio 2
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random 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 affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich (2006, Neural Computation, 18:2), on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network.
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Submitted on : Monday, April 3, 2017 - 9:56:23 AM
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Hélène Paugam-Moisy, Regis Martinez, Samy Bengio. Delay learning and polychronization for reservoir computing. Neurocomputing, Elsevier, 2008, 7-9, 71, pp.1143-1158. ⟨10.1016/j.neucom.2007.12.027⟩. ⟨hal-01500331⟩



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