Neurocomputational approaches to modelling multisensory integration in the brain: a review

Abstract

The Brain's ability to integrate information from different modalities (multisensory integration) is fundamental for accurate sensory experience and efficient interaction with the environment: it enhances detection of external stimuli, disambiguates conflict situations, speeds up responsiveness, facilitates processes of memory retrieval and object recognition. Multisensory integration operates at several brain levels: in subcortical structures (especially the Superior Colliculus), in higher-level associative cortices (e.g., posterior parietal regions), and even in early cortical areas (such as primary cortices) traditionally considered to be purely unisensory. Because of complex non-linear mechanisms of brain integrative phenomena, a key tool for their understanding is represented by neurocomputational models. This review examines different modelling principles and architectures, distinguishing the models on the basis of their aims: (i) Bayesian models based on probabilities and realizing optimal estimator of external cues; (ii) biologically inspired models of multisensory integration in the Superior Colliculus and in the Cortex, both at level of single neuron and network of neurons, with emphasis on physiological mechanisms and architectural schemes; among the latter, some models exhibit synaptic plasticity and reproduce development of integrative capabilities via Hebbian-learning rules or self-organizing maps; (iii) models of semantic memory that implement object meaning as a fusion between sensory-motor features (embodied cognition). This overview paves the way to future challenges, such as reconciling neurophysiological and Bayesian models into a unifying theory, and stimulates upcoming research in both theoretical and applicative domains.

Keywords: Auto-associative and hetero-associative networks; Bayesian and biologically inspired models; Feedforward and feedback architectures; Inverse effectiveness principle; Multisensory enhancement and suppression; Synaptic learning mechanisms.