Computational principles and models of multisensory integration

•Models combining drift-diffusion and optimality can explain multisensory discrimination behavior.•A distributed network with multiple redundant pathways is involved in multisensory integration.•Dimensionality reduction can help understand heterogeneous multisensory neural populations.•Recurrent neural networks may be a new tool to understand multisensory circuits.

Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration. I also emphasize studies which show that task-relevant variables in multisensory contexts are distributed in heterogeneous neural populations. Finally, I describe dimensionality reduction methods and recurrent neural network models that may help decipher heterogeneous neural populations involved in multisensory integration.