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We present a method of estimating the gradient of an objective function withrespect to the synaptic weights of a spiking neural network. The method worksby measuring the fluctuations in the objective function in response to dynamicperturbation of the membrane conductances of the neurons. It is compatible withrecurrent networks of conductance-based model neurons with dynamic synapses.The method can be interpreted as a biologically plausible synaptic learningrule, if the dynamic perturbations are generated by a special class of``empiric'' synapses driven by random spike trains from an external source.Comment: 5 pages; 1 figure; submitted to PR

Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances