Gradient learning in spiking neural networks by dynamic perturbation of conductances

Details Gradient learning in spiking neural networks by dynamic perturbation of conductances Gradient learning in spiking neural networks by dynamic perturbation of conductances

2006-01-19

arxiv.org/abs/q-bio/0601028v1

Phys. Rev. Lett. 97, 048104 (2006)

Biology

Quantitative Biology

Neurons and Cognition

5 pages; 1 figure; submitted to PRL

Scientific paper

10.1103/PhysRevLett.97.048104

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.

Affiliated with

Also associated with

No associations

Rating

Gradient learning in spiking neural networks by dynamic perturbation of conductances does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Gradient learning in spiking neural networks by dynamic perturbation of conductances, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Gradient learning in spiking neural networks by dynamic perturbation of conductances will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-9877