Hierarchical Summary Statistics Encoding Across Primary Visual and Posterior Parietal Cortices

Despite growing evidence that the visual system pools sensory data into a summary statistical representation, the underlying neural mechanisms remain unclear. We characterized the neural coding of summary statistics at the single-cell and population levels using calcium signals imaged in primary visual cortex (V1) and posterior parietal cortex (PPC) while head-fixed mice passively viewed or classified eight mean motion directions of randomly moving dots into two categories. A small portion of neurons in both areas showed global mean motion direction selectivity beyond what would be expected from the simple summation of responses to individual dot motions. Although this selectivity was variable across stimulus variability and trials, population activity robustly encoded global mean motion direction, even though most neurons were not significantly tuned. The V1 population-level mean motion representation was dependent on stimulus variance and systematically biased toward the category center during the motion categorization task. These, along with the observed population-level neural coding of stimulus variance, suggest that multivariate V1 activity is well suited to processing summary statistics. The redundant summary statistical encodings in both V1 and PPC suggest that such information accumulates across the visual hierarchy, which may allow PPC to bind multiple levels of summary statistical representations into task-oriented category signals.