Geometric changes in monkey V4 and IT neural responses during visual category learning

Behavior changes over the course of learning a task. This behavioral change is due to shifts in neural responses that support improved performance. Here, we investigated how the underlying representational geometries in visual areas V4 and IT of the Macaque visual system change during a categorization learning task. Visual stimuli varied in two independent attributes, and monkeys learned to categorize them based on a category boundary in the stimulus space that was defined by a combination of the attributes. Chronic neural population recordings were obtained from V4 and IT over multiple days of training while a monkey learned the task through receiving correct/incorrect feedback. Additionally, we recorded from the same neural populations while a monkey performed a fixation task viewing the same sets of stimuli. In all eight analyzed tasks, the monkey’s performance on the categorization task improved with training. To link this behavioral improvement to the underlying population responses, we investigated how the geometry of neural population activity changed over the course of learning. We treated population responses to all stimuli in each of the two categories as manifold-like representations, and analyzed the geometric properties of these representations using mean-field theoretic manifold capacity analysis. As the monkey learned the task, we observed that the representations in both V4 and IT for the two classes became more separable as measured by an increase in manifold capacity. This increase in capacity was associated with a characteristic geometric change in the neural population response geometry. Our results suggest that both V4 and IT responses actively change during category learning in ways that directly lead to increased separability and improved readouts for downstream neural areas, and point towards future work linking these population-level geometric changes to local changes at the single-neuron level.