A unified neural circuit of causal inference and multisensory integration

Abstract

Causal inference and multisensory integration are two fundamental processes of perception. It is generally believed that there should be one unified neural circuit in the brain to realize these two processes in an optimal way. However, there is no solution yet due to the complicated neural implementation for posterior probability computation. In this study, we propose a unified neural network by solving the complicated posterior probability computation. A unified theoretical framework is presented from the viewpoint of expectation. In addition, a biologically realistic neural circuit is proposed with the combination of importance sampling and probabilistic population coding. Theoretical analyses and simulation results manifest that our proposed neural circuit can implement both causal inference and multisensory integration. Taken together, our framework provides a new perspective of how different perceptual tasks can be performed by the same neural circuit.