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Implementing Dynamic Clamp with Synaptic and Artificial Conductances in Mouse Retinal Ganglion Cells
Published on: May 16, 2013
Gradient learning in spiking neural networks by dynamic perturbation of conductances
Ila R Fiete1, H Sebastian Seung
1Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California 93106, USA.
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.
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