RI: Small: Statistical Perceptual Inference in Visual Cortical Neural Circuits

RI：小：视觉皮层神经回路中的统计感知推理

• 批准号: 1320651
• 负责人: Tai Sing Lee
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320651&HistoricalAwards=false
• 关键词: RI; Small; Statistical; Perceptual; Inference

This interdisciplinary research project seeks to elucidate the computational machinery and algorithms in our brain that enables us to perceive 3-dimensional surfaces of objects in visual scenes based on the 2D images projected on our retinae. The first conjecture of the project is that the neural circuitry underlying perceptual inference in our brain can be predicted by the statistical structures in our natural environment. To prove this conjecture, statistical studies on 3D natural scenes will be carried out and their predictions on neural connectivity will be compared with the functional connectivity and tuning properties of depth-sensitive neurons in the primate visual cortex obtained using large-scale multi-electrode recording techniques. This will provide deeper insights into how the brain represents and builds models of the structures of the external world to enable perceptual inference. To understand what such circuits can compute, the investigator conjectures that neural circuits realize a class of generative models in computer vision and computational neuroscience called Markov random fields and Boltzmann machine. This second conjecture will be evaluated by exploring the theoretical link between the neural circuits and these computational models, by comparing experimental neural observations with behaviors of these computational models, and by evaluating the computational performance of the inferred neural circuits in solving stereo computation and surface interpolation problems in real world data. The research combines techniques in machine and statistical learning, computer vision, neural networks and neurophysiology to dissect neural circuits from a functional perspective. By linking real circuits to a powerful class of computational models in statistical inference, the project will have broader impacts by providing novel evidence and fundamental insight to the neural mechanisms and computational algorithms underlying statistical perceptual inference in the brain. This interdisciplinary project will be a catalyst for the development of educational initiatives to bring bringing computer science and neuroscience together for undergraduates and graduate students, and to promote the awareness and involvement of students from multiple disciplines, including under-represented groups, in the field of computational neuroscience.

这个跨学科的研究项目旨在阐明我们大脑中的计算机制和算法，使我们能够根据投射在视网膜上的2D图像感知视觉场景中物体的三维表面。 该项目的第一个猜想是，我们大脑中感知推理的神经回路可以通过我们自然环境中的统计结构来预测。 为了证明这一猜想，将对3D自然场景进行统计研究，并将其对神经连接的预测与使用大规模多电极记录技术获得的灵长类视觉皮层中深度敏感神经元的功能连接和调谐特性进行比较。这将提供更深入的见解，了解大脑如何表示和构建外部世界结构的模型，以实现感知推理。为了理解这种电路可以计算什么，研究人员假设神经电路实现了计算机视觉和计算神经科学中的一类生成模型，称为马尔可夫随机场和玻尔兹曼机。第二个猜想将通过探索神经回路和这些计算模型之间的理论联系，通过比较实验神经观察与这些计算模型的行为，以及通过评估推断的神经回路在解决真实的世界数据中的立体计算和表面插值问题时的计算性能来评估。该研究结合了机器和统计学习，计算机视觉，神经网络和神经生理学的技术，从功能的角度剖析神经回路。通过将真实的电路与统计推断中的一类强大的计算模型联系起来，该项目将通过为大脑中统计感知推断的神经机制和计算算法提供新的证据和基本见解来产生更广泛的影响。 这个跨学科项目将成为教育举措发展的催化剂，为本科生和研究生带来计算机科学和神经科学的结合，并促进来自多个学科的学生（包括代表性不足的群体）对计算领域的认识和参与。神经科学。