Zhang Yimeng, Li Xiong, Samonds Jason M, Lee Tai Sing
Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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.
Vision Res. 2016 Mar;120:121-31. doi: 10.1016/j.visres.2015.12.002. Epub 2015 Dec 28.
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.
贝叶斯理论为大脑中的知觉推理提供了一个令人信服的概念框架。贝叶斯推理的核心是统计先验的概念。为了理解贝叶斯推理的神经机制，我们需要了解自然环境中统计规律的神经表征。在本文中，我们通过实证研究了灵长类动物初级视觉皮层中视差调谐神经元的功能连接如何表征自然三维场景中的统计规律。我们应用玻尔兹曼机模型从三维自然场景中学习，发现模型中的单元表现出合作和竞争相互作用，形成了一个“视差关联场”，类似于轮廓关联场。视差关联场中的合作和竞争相互作用与立体匹配计算模型的约束相一致。此外，我们在模型上模拟了神经生理学实验，发现结果在猕猴初级视觉皮层中视差调谐神经元之间的功能连接测量方面与神经生理学数据一致。这些发现表明，视觉皮层中观察到的功能连接与自然场景的统计之间存在关系。它们还表明，玻尔兹曼机可以作为一个可行的模型来概念化视觉皮层中的计算，因此可以用于根据自然场景统计预测视觉皮层中的神经回路。
