title: Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines.
abstract: Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated empirically how statistical regularities in natural 3D scenes are represented in the functional connectivity of disparity-tuned neurons in the primary visual cortex of primates. We applied a Boltzmann machine model to learn from 3D natural scenes, and found that the units in the model exhibited cooperative and competitive interactions, forming a "disparity association field", analogous to the contour association field. The cooperative and competitive interactions in the disparity association field are consistent with constraints of computational models for stereo matching. In addition, we simulated neurophysiological experiments on the model, and found the results to be consistent with neurophysiological data in terms of the functional connectivity measurements between disparity-tuned neurons in the macaque primary visual cortex. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. They also suggest that the Boltzmann machine can be a viable model for conceptualizing computations in the visual cortex and, as such, can be used to predict neural circuits in the visual cortex from natural scene statistics.
authors.affiliation: Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: yimengzh@cs.cmu.edu.
authors.forename: Yimeng
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: flit.lee@gmail.com.
authors.forename: Xiong
authors.initials: X
authors.lastname: Li
authors.affiliation: Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: samondjm@cnbc.cmu.edu.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Samonds
authors.affiliation: Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Electronic address: tai@cnbc.cmu.edu.
authors.forename: Tai Sing
authors.initials: TS
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D009474:Neurons / Q000502:physiology*; D015357:Vision Disparity / Q000502:physiology*; D015348:Vision, Binocular / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*
keywords: Association field; Binocular disparity; Functional connectivity; Scene statistics; Visual cortex