Representation of natural contours by a neural population in monkey V4

Abstract

The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour-features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour-features in monkey (Macaca fuscata) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour-features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that the most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showed the responses depended on multiple contour-features. We next examined the population-level representation by using multi-dimensional scaling analysis. The neural preferences to the multiple contour-features and that to natural stimuli compared to silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour-features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour-features in natural images and represent contour and surface properties in population.

Significance Statement Contours of natural objects are often complex but the visual system extracts their features and efficiently represent their shape. Neurons in the intermediate-level visual cortex, V4, play crucial roles for the representation of natural contours. Analyzing the electrophysiological data, we found that V4 neurons simultaneously code multiple contour-features such as curvature, closure, and orientation, and represent the prominent contours such as corners and bumps. For instance, a number of neurons responded to acute curvatures that enclosed object extent. Mutual information and population analyses showed that the responses of many neurons depend on multiple contour-features and represent the contours and surfaces in population. A population of V4 neurons seems to encode complex but prominent contours for the representation of natural objects.