Representation of Natural Contours by a Neural Population in Monkey V4.

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 most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showing the responses depended on multiple contour features. We next examined the population-level representation by using multidimensional scaling analysis. The neural preferences to the multiple contour features and that to natural stimuli compared with 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.

Correlation between neural responses and human perception in figure-ground segregation.

Segmentation of a natural scene into objects (figures) and background (ground) is one of crucial functions for object recognition and scene understanding. Recent studies have investigated neural mechanisms underlying figure-ground (FG) segregation and reported neural modulation to FG in the intermediate-level visual area, V4, of macaque monkeys (FG neurons). However, whether FG neurons contribute to the perception of FG segregation has not been clarified. To examine the contribution of FG neurons, we examined the correlations between perceptual consistency (PC), which quantified perceptual ambiguity in FG determination, and the reliability of neural signals in response to FG. First, we evaluated PCs for the images that were used in the previous neural recording in V4; specifically, we measured how consistently FG can be determined across trials and participants for each stimulus. The PCs were widely distributed, so that we identified the ambiguity in FG segregation for each stimulus. Next, we analyzed the correlation between the PCs and the reliability of neural modulation to FG. We found that the stimuli with higher PCs evoked more consistent and greater modulation in the responses of single neurons than those with lower PCs. Since perception is expected to show a greater correlation with responses of neural population compared to those of single neurons, we examined the correlation between the PCs and the consistency of the population responses in FG determination. Stimuli with higher PCs evoked higher population consistency than those with lower PCs. Finally, we analyzed the correlation between the PCs and neural latencies in FG modulation. We found that the stimuli with higher PCs showed shorter reaction times in FG perception and evoked shorter modulation latencies in FG neurons. These results indicate that the responses of FG neurons recorded from macaque monkeys show significant correlations with human FG perception, suggesting that V4 neurons with FG-dependent responses contribute to the perception of FG segregation.

Population coding of figure and ground in natural image patches by V4 neurons.

Segmentation of a natural scene into objects and background is a fundamental but challenging task for recognizing objects. Investigating intermediate-level visual cortical areas with a focus on local information is a crucial step towards understanding the formation of the cortical representations of figure and ground. We examined the activity of a population of macaque V4 neurons during the presentation of natural image patches and their respective variations. The natural image patches were optimized to exclude the influence of global context but included various characteristics of local stimulus. Around one fourth of the patch-responsive V4 neurons exhibited significant modulation of firing activity that was dependent on the positional relation between the figural region of the stimulus and the classical receptive field of the neuron. However, the individual neurons showed low consistency in figure-ground modulation across a variety of image patches (55-62%), indicating that individual neurons were capable of correctly signaling figure and ground only for a limited number of stimuli. We examined whether integration of the activity of multiple neurons enabled higher consistency across a variety of natural patches by training a support vector machine to classify figure and ground of the stimuli from the population firing activity. The integration of the activity of a few tens of neurons yielded discrimination accuracy much greater than that of single neurons (up to 85%), suggesting a crucial role of population coding for figure-ground discrimination in natural images.