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When the brain receives input from multiple sensory systems, it is faced withthe 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 originatedfrom distinct events. Furthermore, it must also have a mechanism through whichit can keep sensory inputs calibrated to maintain the accuracy of its internalrepresentations. We have developed a neural network architecture capable of i)approximating optimal multisensory spatial integration, based on Bayesiancausal inference, and ii) recalibrating the spatial encoding of sensorysystems. The architecture is based on features of the dorsal processinghierarchy, including the spatial tuning properties of unisensory neurons andthe convergence of different sensory inputs onto multisensory neurons.Furthermore, we propose that these unisensory and multisensory neurons playdual roles in i) encoding spatial location as separate or integrated estimatesand ii) accumulating evidence for the independence or relatedness ofmultisensory stimuli. We further propose that top-down feedback connectionsspanning the dorsal pathway play key a role in recalibrating spatial encodingat the level of early unisensory cortices. Our proposed architecture providespossible explanations for a number of human electrophysiological andneuroimaging results and generates testable predictions linking neurophysiologywith behaviour.

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