Learning and Intelligent Systems: Adaptive Cortical Computation in the Visual Domain: Integrated Approach UsingMulti-Unit Recording, Network Theory, & Experiments in O

学习和智能系统：视觉领域的自适应皮层计算：使用多单元记录、网络理论的综合方法，

• 批准号: 9720320
• 负责人: James Anderson
• 金额: $ 72.29万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9720320&HistoricalAwards=false
• 关键词: Learning; Intelligent; Systems; Adaptive; Cortical

IBN-9720320 PI: ANDERSON This project is being funded through the Learning & Intelligent Systems Initiative. This study investigates functional interactions among groups of neurons (nerve cells) in the brain. The cerebral cortex of the brain is a dynamic ensemble of groups of neurons with activities that coalesce and dissolve in the performance of particular tasks. A computational model called 'network of networks' describes the operation of computations based on neurons interacting in intermediate groupings, in the size range between single neurons (only one computing element) and entire brain regions (to hundreds of millions of computing elements). In the intermediate scale groupings, the model makes predictions about the behavior of both its component single neurons and the overall nature of the cortical computation, manifested as behavior and perception. Experimental tests utilize the mammalian visual system because so much is known about cortical processing of visual information at the level of single neurons, and also there is a large body of related experimental results for visual perception. There are three inter-related parts to this project. 1) Computer simulations and mathematical analysis further develop the 'network of networks' model itself. 2) Simultaneous activity of multiple neurons in visual areas are recorded physiologically to examine long-range transfer of information across visual cortex, of the type suggested by the model. 3) Analyses of previously obtained psychophysical behavioral data from human subjects are combined with computer simulations to try to understand the surprising effectiveness of silhouettes in object recognition, and to provide a test system for the network model. Results will have an impact because of the importance of linking cognition with neuroscience to understand mechanisms that underlie learning and perception, and because understanding how the brain handles complex computations will provide insights for the design of artificia l recognition and decision-making systems.

这个项目是由学习和智能系统倡议资助的。这项研究调查了大脑中神经元群（神经细胞）之间的功能相互作用。大脑的大脑皮层是神经元群的动态集合，它们的活动在特定任务的执行中结合和溶解。一种称为“网络的网络”的计算模型描述了基于神经元在中间分组中相互作用的计算操作，其大小范围从单个神经元（只有一个计算元素）到整个大脑区域（到数亿个计算元素）。在中等规模分组中，该模型对其组成的单个神经元的行为和皮层计算的整体性质（表现为行为和感知）进行预测。实验测试利用哺乳动物的视觉系统，因为我们对单个神经元水平的视觉信息皮质处理已经了解得很多，而且在视觉感知方面也有大量相关的实验结果。这个项目有三个相互关联的部分。1)计算机模拟和数学分析进一步发展了“网络的网络”模型本身。2)从生理学上记录视觉区域多个神经元的同时活动，以检查模型所建议的视觉皮层之间的远程信息传递。3)通过对先前获得的人类受试者心理物理行为数据的分析，结合计算机模拟，试图了解轮廓在物体识别中的惊人有效性，并为网络模型提供测试系统。结果将产生影响，因为将认知与神经科学联系起来对于理解学习和感知的机制非常重要，因为理解大脑如何处理复杂的计算将为人工识别和决策系统的设计提供见解。