Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

ABSTRACT: Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced

SUBMITTER: Albers C 

PROVIDER: S-EPMC4763343 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature