Original paper
Local online learning in recurrent networks with random feedback
Abstract
Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsistent with biological features of the brain, such as causality and locality. We derive an approximation to gradient-based learning that comports with these constraints by requiring synaptic weight...
Paper Details
Title
Local online learning in recurrent networks with random feedback
Published Date
May 24, 2019
Journal
Volume
8