Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain
This work addresses a fundamental problem in neuroscience for understanding visual processing in the brain, specifically shape encoding in V4, but it is incremental as it builds on existing data and models.
The study tackled the unknown mechanisms for transforming early visual signals to curvature representations in V4 by proposing a hierarchical model that identifies essential V1/V2 encodings and learns V4 shape selectivity from Macaque responses, revealing that V4 cells integrate multiple shape parts with similar excitatory and inhibitory contributions across their receptive fields.
The mechanisms involved in transforming early visual signals to curvature representations in V4 are unknown. We propose a hierarchical model that reveals V1/V2 encodings that are essential components for this transformation to the reported curvature representations in V4. Then, by relaxing the often-imposed prior of a single Gaussian, V4 shape selectivity is learned in the last layer of the hierarchy from Macaque V4 responses. We found that V4 cells integrate multiple shape parts from the full spatial extent of their receptive fields with similar excitatory and inhibitory contributions. Our results uncover new details in existing data about shape selectivity in V4 neurons that with further experiments can enhance our understanding of processing in this area. Accordingly, we propose designs for a stimulus set that allow removing shape parts without disturbing the curvature signal to isolate part contributions to V4 responses.