Results in the recent literature suggest that
multisensory integration in the brain follows the rules of Bayesian inference.
However, how neural circuits can realize such inference and how it can be
learned from experience is still the subject of active research. The aim of
this work is to use a recent neurocomputational model to investigate how the
likelihood and prior can be encoded in synapses, and how they affect
audio-visual perception, in a variety of conditions characterized by different
experience, different cue reliabilities and temporal asynchrony. The model
considers two unisensory networks (auditory and visual) with plastic receptive
fields and plastic crossmodal synapses, trained during a learning period.
During training visual and auditory stimuli are more frequent and more tuned
close to the fovea. Model simulations after training have been performed in
crossmodal conditions to assess the auditory and visual perception bias: visual
stimuli were positioned at different azimuth (±10° from the fovea) coupled with
an auditory stimulus at various audio-visual distances (±20°). The cue
reliability has been altered by using visual stimuli with two different
contrast levels. Model predictions are compared with behavioral data. Results
show that model predictions agree with behavioral data, in a variety of
conditions characterized by a different role of prior and likelihood. Finally,
the effect of a different unimodal or crossmodal prior, re-learning, temporal
correlation among input stimuli, and visual damage (hemianopia) are tested, to
reveal the possible use of the model in the clarification of important
multisensory problems.