Functional architecture for image statistics in early and mid-level visual cortical areas in macaque monkeys.

概要

Distribution patterns of functional specificities of neurons for visual image statistics determined the functional architecture of visual cortical areas. Elucidation of the architecture of a cortical area provides a critical clue to understanding how the neurons process the visual input and represent the information about the external world. Natural scenes are characterized by diverse set of image statistics, including various parameters of the luminance histogram, outputs of Gabor-like filters, and pairwise correlations between the filter output combinations of different positions, orientations, and scales (Portilla-Simoncelli statistics). Some of these statistics capture the response properties of visual neurons to the presentation of natural images and scenes. However, it remains unclear to what extent such statistics can explain neural responses to natural scenes and how neurons that are tuned to these statistics are distributed across the cortical plane. By the combination of an encoding-model approach and two-photon calcium imaging method, I addressed these issues in macaque visual areas V1 and V4. For each imaged neuron, I constructed an encoding model to mimic its responses to natural movies. By extracting Portilla-Simoncelli statistics through outputs of both filters and filter correlations, and by computing an optimally weighted sum of these outputs, the model successfully reproduced responses in a subpopulation of neurons. I evaluated the selectivities of these neurons by quantifying the contributions of each statistic to visual responses. Neurons whose responses were mainly determined by Gabor-like filter outputs (low-level statistics) were abundant at most imaging sites in V1. In V4, the relative contribution of higher-order statistics, such as cross-scale correlation, was increased. Preferred image statistics varied markedly across V4 sites, and the response similarity of two neurons at individual imaging sites gradually declined with increasing cortical distance. The results indicate that natural scene analysis progresses from V1 to V4, and neurons sharing preferred image statistics are locally clustered in V4. The combination of neural modeling methods, multi-scale and long-term imaging methods will extend the limitation of understanding the functional architecture in early and mid-level visual cortical areas in macaque monkeys.