Gradient learning in spiking neural networks by dynamic perturbation of conductances

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


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
Issue number4
StatePublished - 2006

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


Dive into the research topics of 'Gradient learning in spiking neural networks by dynamic perturbation of conductances'. Together they form a unique fingerprint.

Cite this