Bayesian inference is facilitated by modular neural networks with different time scales.

ABSTRACT: Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. Our findings reveal that networks with modular structures, composed of fast and slow modules, are adept at representing this prior distribution, enabling more accurate Bayesian inferences. Specifically, the modular network that consists of a main module connected with input and output layers and a sub-module with slower neural activity connected only with the main module outperformed networks with uniform time sc

SUBMITTER: Ichikawa K 

PROVIDER: S-EPMC10962854 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature