Gradient learning in spiking neural networks by dynamic perturbation of conductances. Export

@article{citeulike:4731761, 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.}, author = {Fiete, Ila R. and Seung, H. Sebastian}, citeulike-article-id = {4731761}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/16907616}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=16907616}, day = {28}, issn = {0031-9007}, journal = {Physical review letters}, keywords = {neural-networks, reinforcement-learning, spiking-neurons}, month = {July}, number = {4}, posted-at = {2009-10-26 07:34:47}, priority = {2}, title = {Gradient learning in spiking neural networks by dynamic perturbation of conductances.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16907616}, volume = {97}, year = {2006} }

TY - JOUR ID - citeulike:4731761 L3 - citeulike-article-id:4731761 N2 - 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. IS - 4 JF - Physical review letters SN - 0031-9007 TI - Gradient learning in spiking neural networks by dynamic perturbation of conductances. VL - 97 KW - neural-networks KW - reinforcement-learning KW - spiking-neurons AU - Fiete, Ila AU - Seung, Sebastian PY - 2006/07/28/ UR - http://view.ncbi.nlm.nih.gov/pubmed/16907616 ER -