Neurons in visual and vestibular information integration areas of macaque brain such as medial superior temporal (MSTd) and ventral intraparietal (VIP) have been classified into congruent neurons and opposite neurons, which prefer congruent inputs and opposite inputs from the two sensory modalities, respectively. In this work, we propose a mechanistic spiking neural model that can account for the emergence of congruent and opposite neurons and their interactions in a neural circuit for multi-sensory integration. The spiking neural circuit model is adopted from an established model for the circuits of the primary visual cortex with little changes in parameters. The network can learn, based on the basic Hebbian learning principle, the correct topological organization and behaviors of the congruent and opposite neurons that have been proposed to play a role in multi-sensory integration. This work explore the constraints and the conditions that lead to the development of a proposed neural circuit for cue integration. It also demonstrates that such neural circuit might indeed be a canonical circuit shared by computations in many cortical areas.

Opposite neurons, found in macaque dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas, combine visual and vestibular cues of self-motion in opposite ways. A neural circuit recently proposed utilizes opposite neurons to perform causal inference and decide whether the visual and vestibular cues in MSTd and VIP should be integrated or segregated. However, it is unclear how these opposite connections can be formed with biologically realistic learning rules. We propose a network model capable of learning these opposite neurons, using Hebbian and Anti-Hebbian learning rules. The learned neurons are topographically organized and have von Mises-shaped feedforward connections, with tuning properties characteristic of opposite neurons. Our purpose is two-fold: on the one hand, we provide a circuit-level mechanism that explains the properties and formation of opposite neurons; on the other hand, we present a way to extend current theories of multisensory integration to account for appropriate segregation of sensory cues.