  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.
