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 used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.

Cooperative coding of continuous variables in networks with sparsity constraint

“…Such recurrent excitatory connectivity may seem unintuitive from a normative stand-point, as it amplifies noise [20] and can increase response times [21, 22]. Previous studies suggested that it may support persistent activity and thus working memory [23, 24] and that it may implement complicated priors [25].…”