English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Biological synaptic transmission is unreliable, and this unreliability likelydegrades neural circuit performance. While there are biophysical mechanismsthat can increase reliability, for instance by increasing vesicle releaseprobability, these mechanisms cost energy. We examined four such mechanismsalong with the associated scaling of the energetic costs. We then embeddedthese energetic costs for reliability in artificial neural networks (ANN) withtrainable stochastic synapses, and trained these networks on standard imageclassification tasks. The resulting networks revealed a tradeoff betweencircuit performance and the energetic cost of synaptic reliability.Additionally, the optimised networks exhibited two testable predictionsconsistent with pre-existing experimental data. Specifically, synapses withlower variability tended to have 1) higher input firing rates and 2) lowerlearning rates. Surprisingly, these predictions also arise when synapsestatistics are inferred through Bayesian inference. Indeed, we were able tofind a formal, theoretical link between the performance-reliability costtradeoff and Bayesian inference. This connection suggests two incompatiblepossibilities: evolution may have chanced upon a scheme for implementingBayesian inference by optimising energy efficiency, or alternatively, energyefficient synapses may display signatures of Bayesian inference withoutactually using Bayes to reason about uncertainty.

Signatures of Bayesian inference emerge from energy efficient synapses