Gradient learning in spiking neural networks by dynamic perturbation of conductances

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Abstract

We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of "empiric" synapses driven by random spike trains from an external source.

Original languageEnglish (US)
Article number048104
JournalPhysical review letters
Volume97
Issue number4
DOIs
StatePublished - Aug 4 2006

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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