A modular neural circuit for computing the motion of objects

Abstract

Computing the direction of a moving object requires integrating motion signals from its component edges into coherent patterns. The representation of pattern motion in visual cortex has been extensively studied, yet its underlying neural circuitry remains unknown. Using high-density recordings in macaque area MT, we show that selectivity to pattern motion emerges from a functionally and anatomically distinct cortical circuit. We demonstrate that neurons specialized for encoding component and pattern motion comprise distinct cell types arranged in a hierarchical circuit in which pattern neurons integrate inputs from a range of component neurons. Furthermore, we show that component and pattern neurons are spatially segregated into modules arranged systematically across cortical columns encoding direction of motion. This architecture and circuit align with a classic solution to the problem of computing the motion of objects.