Signatures of Bayesian inference emerge from energy-efficient synapses.

ABSTRACT: Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Spec

SUBMITTER: Malkin J 

PROVIDER: S-EPMC11302983 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature