Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines
Yimeng Zhang, Xiong Li, Jason M. Samonds, Ben Poole, Tai Sing Lee | Computational and Systems Neuroscience (Cosyne) 2015 | 2015
Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. Here, we investigated empirically how the second order statistical regularities in natural 3D scenes are represented in the functional connectivity of a population of disparity-tuned neurons in the primary visual cortex of primates. We applied the Boltzmann machine to learn from 3D natural scenes and found that the functional connectivity between nodes exhibited patterns of cooperative and competitive interactions that are consistent with the observed functional connectivity between disparity-tuned neurons in the macaque primary visual cortex. The positive interactions encode statistical priors about spatial correlations in depth and implement a smoothness constraint. The negative interactions within a hypercolumn and across hypercolumns emerge automatically to reflect the uniqueness constraint found in computational models for stereopsis. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. This relationship suggests that the functional connectivity between disparity-tuned neurons can be considered as a disparity association field. They also suggest that the Boltzmann machine, or a Markov random field in general, can be a viable model for conceptualizing computations in the visual cortex, and as such, can be used to leverage the natural scene statistics to understand neural circuits in the visual cortex.