Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons.

ABSTRACT: Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be u

SUBMITTER: Zhang WH 

PROVIDER: S-EPMC10625605 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature