Understanding the circuit for topological object tracking

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DESCRIPTION Abstract: Understanding the circuit for topological object tracking (Science Area: Neuroscience) The problem I want to solve is how an object first arises in the brain. Light hitting the retina is caried by over a million axons of the optic nerve into primary visual cortex. These are the pixels that drive visual experience. But when we look around us, we don't see pixels. We see invariant objects in space--invariant in that we perceive the objects as unchanged despite severe changes in appearance as we move around them. How does the brain stitch together pixels into invariant, discrete objects in space? The time is ripe for a fresh attack on this problem due to a critical theoretical advance, and a host of experimental advances. I believe the essential reason why no one has solved the problem of invariant object perception until now is that no one has realized the answer could be very simple. A new mathematical theory explains how the representation of objects in the 3D visual world as surfaces enables a complete and fundamentally simple solution to the problem of object segmentation and tracking, i.e., labeling all the pixels belonging to a single object over space and time, regardless of object shape. The theory strongly suggests that a powerful ""topological engine"" is churning away within very early stages of the visual cortex, to generate invariant labels for the different objects in the environment over space and time, and specifies the computations that must be performed in order to generate these invariant labels. Motivated by this new theory, and taking advantage of several key recent experimental advances in monkey and rodent vision research, I describe a set of experiments to: 1) identify the neural signature of the topological object label in early macaque visual cortical areas, 2) behaviorally test whether rodents also generate a surface representation, and if so, then 3) dissect the circuit by which this label is generated through two photon imaging an