Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).

ABSTRACT: A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge. Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing grad

SUBMITTER: Kaiser J 

PROVIDER: S-EPMC7235446 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature