New findings about how the brain interprets sensory information may have applications for treating brain disorders and designing artificial intelligence. Greg DeAngelis, Ph.D., the George Eastman Professor of Brain and Cognitive Sciences at the University of Rochester led the research published in eLife that describes a novel neural mechanism involved in causal inference – a key to learning, reasoning, and decision making – that helps the brain detect object motion during self-motion. This neural mechanism has a particular combination of response properties, which makes it well-suited to contribute to the task of distinguishing between self-motion and the motion of other objects.
“Although the brain probably uses multiple tricks to solve this problem, this new mechanism has the advantage that it can be performed in parallel at each local region of the visual field, and thus may be faster to implement than more global processes,” DeAngelis said. “This mechanism might also be applicable to autonomous vehicles, which also need to rapidly detect moving objects.”