Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

ABSTRACT: Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instanta

SUBMITTER: Gardner B 

PROVIDER: S-EPMC4988787 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature