Academic Journal

Signatures of Bayesian inference emerge from energy-efficient synapses

التفاصيل البيبلوغرافية
العنوان: Signatures of Bayesian inference emerge from energy-efficient synapses
المؤلفون: James Malkin, Cian O'Donnell, Conor J Houghton, Laurence Aitchison
المصدر: eLife, Vol 12 (2024)
بيانات النشر: eLife Sciences Publications Ltd, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
LCC:Science
LCC:Biology (General)
مصطلحات موضوعية: synaptic plasticity, Bayesian inference, energy efficiency, computational neuroscience, Medicine, Science, Biology (General), QH301-705.5
الوصف: 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. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2050-084X
Relation: https://elifesciences.org/articles/92595; https://doaj.org/toc/2050-084X
DOI: 10.7554/eLife.92595
URL الوصول: https://doaj.org/article/08dbd6104404401eaf5e610ced0ffb47
رقم الانضمام: edsdoj.08dbd6104404401eaf5e610ced0ffb47
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:2050084X
DOI:10.7554/eLife.92595