Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain

TL;DR

This study introduces a hierarchical model that elucidates how V4 neurons in the brain encode shape by integrating multiple shape parts, revealing mechanisms of shape selectivity and proposing new experimental designs.

Contribution

The paper presents a novel hierarchical model that links V1/V2 encodings to V4 shape selectivity and demonstrates learned shape encoding mechanisms from macaque responses.

Findings

V4 cells integrate multiple shape parts across their receptive fields.

Shape selectivity in V4 can be learned from neural responses.

Proposed stimulus designs can isolate contributions of shape parts.

Abstract

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.