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

IBN-9720320 PI：安德森这个项目是通过学习和智能系统倡议提供资金的。这项研究调查了大脑中神经元组(神经细胞)之间的功能相互作用。大脑皮层是一组神经元的动态集合，这些神经元的活动在执行特定任务时合并和分解。一种称为“网络网络”的计算模型描述了在单个神经元(只有一个计算单元)和整个大脑区域(到数亿个计算单元)之间的大小范围内，中间分组中相互作用的神经元的计算操作。在中等尺度分组中，该模型对其组成的单个神经元的行为和大脑皮层计算的整体性质进行预测，表现为行为和感知。实验测试使用哺乳动物的视觉系统，因为在单个神经元水平上对视觉信息的皮质处理已知如此之多，而且也有大量与视觉感知相关的实验结果。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“网络网络”模型本身。2)生理上记录了视觉区域中多个神经元的同时活动，以检查信息在视觉皮质中的远程传递，这是模型所建议的类型。3)通过对先前获得的人体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令人惊讶的有效性，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，以了解学习和感知的基础机制的重要性，以及因为了解大脑如何处理复杂的计算将为人工L识别和决策系统的设计提供见解。

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.