Closing the loop on multisensory interactions: A neural architecture for multisensory causal inference and recalibration

When the brain receives input from multiple sensory systems, it is faced with the question of whether it is appropriate to process the inputs in combination, as if they originated from the same event, or separately, as if they originated from distinct events. Furthermore, it must also have a mechanism through which it can keep sensory inputs calibrated to maintain the accuracy of its internal representations. We have developed a neural network architecture capable of i) approximating optimal multisensory spatial integration, based on Bayesian causal inference, and ii) recalibrating the spatial encoding of sensory systems. The architecture is based on features of the dorsal processing hierarchy, including the spatial tuning properties of unisensory neurons and the convergence of different sensory inputs onto multisensory neurons. Furthermore, we propose that these unisensory and multisensory neurons play dual roles in i) encoding spatial location as separate or integrated estimates and ii) accumulating evidence for the independence or relatedness of multisensory stimuli. We further propose that top-down feedback connections spanning the dorsal pathway play key a role in recalibrating spatial encoding at the level of early unisensory cortices. Our proposed architecture provides possible explanations for a number of human electrophysiological and neuroimaging results and generates testable predictions linking neurophysiology with behaviour.