Normalization Enables Robust Validation of Disparity Estimates from Neural Populations

Abstract

Binocular fusion takes place over a limited region smaller than one degree of visual angle (Panum's fusional area), which is on the order of the range of preferred disparities measured in populations of disparity-tuned neurons in the visual cortex. However, the actual range of binocular disparities encountered in natural scenes extends over tens of degrees. This discrepancy suggests that there must be a mechanism for detecting whether the stimulus disparity is inside or outside the range of the preferred disparities in the population. Here, we compare the efficacy of several features derived from the population responses of phase-tuned disparity energy neurons in differentiating between in-range and out-of-range disparities. Interestingly, some features that might be appealing at first glance, such as the average activation across the population and the difference between the peak and average responses, actually perform poorly. On the other hand, normalizing the difference between the peak and average responses results in a reliable indicator. Using a probabilistic model of the population responses, we improve classification accuracy by combining multiple features. A decision rule that combines the normalized peak to average difference and the peak location significantly improves performance over decision rules based on either measure in isolation. In addition, classifiers using normalized difference are also robust to mismatch between the image statistics assumed by the model and the actual image statistics.