Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control

TL;DR

This paper explores how the canonical six-layer cortical circuits in the brain may implement dual computations for Bayesian inference and optimal control, linking sensory processing and motor control through shared computational principles.

Contribution

It proposes that the brain's canonical cortical architecture underpins dual computations for perception and action, integrating Bayesian inference with optimal control theories.

Findings

The duality between Bayesian inference and optimal control is reflected in cortical circuits.

Canonical cortical circuits may implement variable representations for inference and control.

The architecture supports a unified view of sensory and motor processing in the brain.

Abstract

The duality of sensory inference and motor control has been known since the 1960s and has recently been recognized as the commonality in computations required for the posterior distributions in Bayesian inference and the value functions in optimal control. Meanwhile, an intriguing question about the brain is why the entire neocortex shares a canonical six-layer architecture while its posterior and anterior halves are engaged in sensory processing and motor control, respectively. Here we consider the hypothesis that the sensory and motor cortical circuits implement the dual computations for Bayesian inference and optimal control, or perceptual and value-based decision making, respectively. We first review the classic duality of inference and control in linear quadratic systems and then review the correspondence between dynamic Bayesian inference and optimal control. Based on the architecture of the canonical cortical circuit, we explore how different cortical neurons may represent variables and implement computations.