Introducing principles of synaptic integration in the optimization of deep neural networks.

ABSTRACT: Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mec

SUBMITTER: Dellaferrera G 

PROVIDER: S-EPMC8989917 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature