Emergence of opposite neurons in a decentralized firing-rate model of multisensory integration

Abstract

Multisensory integration areas such as dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas in macaques combine visual and vestibular cues to produce better estimates of self-motion. Congruent and opposite neurons, two types of neurons found in these areas, prefer congruent inputs and opposite inputs from the two modalities, respectively. A recently proposed computational model of congruent and opposite neurons reproduces their tuning properties and shows that congruent neurons optimally integrate information while opposite neurons compute disparity information. However, the connections in the network are fixed rather than learned, and in fact the connections of opposite neurons, as we will show, cannot arise from Hebbian learning rules. We therefore propose a new model of multisensory integration in which congruent neurons and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, and show that these neurons exhibit experimentally observed tuning properties.

A spiking neural circuit model for learning multi-sensory integration

“…Previously, a firing-rate model has been proposed to achieve visual and vestibular sensory integration. [8] [9] But the congruent neurons in that model could send both excitatory and inhibitory signals to other neurons, violating Dales law. Also their nor-malization were done by activation function.…”