The brain is capable of integrating information in multiple sensory channels in a Bayesian optimal way. Based on a decentralized network model inspired by electrophysiological recordings, we consider the structural pre-requisites for optimal multisensory integration. In this architecture, same-channel feedforward and recurrent links encode the unisensory likelihoods, whereas reciprocal couplings connecting the different modules are shaped by the correlation in the joint prior probabilities. Moreover, the statistical relationship between the difference in the optimal network structures and the difference in the priors and the likelihoods clearly shows that the network can encode multisensory information in a distributed manner. Our results generate testable predictions for future experiments and are likely to be applicable to other artificial systems.
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