Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT

TL;DR

This paper reviews computational models of visual motion processing inspired by neurophysiological data, focusing on theories of local and pattern motion integration to enhance understanding of neural computation in the visual cortex.

Contribution

It provides a comprehensive overview of experimentally driven computational models of MT, proposing new hypotheses and experimental tests for motion processing mechanisms.

Findings

Models support local motion integration theories

Pattern motion processing mechanisms are elucidated

Proposed experiments to test neurophysiological hypotheses

Abstract

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.