NCS-FO: A circuit theory of cortical function

NCS-FO：皮质功能的回路理论

• 批准号: 1532846
• 负责人: Charles Gilbert
• 金额: $ 97.01万
• 依托单位: Rockefeller University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1532846&HistoricalAwards=false
• 关键词: NCS; FO; circuit; theory; cortical

This project aims to develop and test a new conceptual framework for understanding brain function, and informing biologically based artificial intelligence systems. The underlying theory holds that the properties of any neuron and any cortical area are not fixed but undergo state changes with changing perceptual task, expectation and attention. Because of the multiple routes by which this top-down information can be conveyed, each neuron is essentially a microcosm of the brain as a whole. In this framework, a neuron is viewed as an adaptive processor rather than merely a link in a labeled line, taking on functions that are required for performing the current task. The theory accounts for cortical function at the circuit level. Through an interaction between feedback and intrinsic connections neurons select inputs that are relevant to a task and suppress inputs that are irrelevant. The experiments will combine visual psychophysics, fMRI, large scale high density electrode array recordings and optogenetic manipulation. These techniques will be used to measure changes in effective connectivity between cortical areas and the relationship between effective connectivity and the information represented by neurons at different recording sites as animals perform different visual recognition tasks. Computational models will be developed to account for how task-dependent gating of connections can be achieved and will reproduce the functional dynamics observed experimentally. Though the experiments will focus on the visual modality, the findings from the work will formulate a general theory of brain function that is broadly applicable to the brain as a whole.

该项目旨在开发和测试一个新的概念框架，用于理解大脑功能，并为基于生物学的人工智能系统提供信息。 基本理论认为，任何神经元和任何皮质区域的属性都不是固定的，而是随着感知任务、期望和注意力的变化而经历状态变化。 由于这种自上而下的信息可以通过多种途径传递，因此每个神经元本质上都是整个大脑的一个缩影。 在这个框架中，神经元被视为自适应处理器，而不仅仅是标记线中的链接，承担执行当前任务所需的功能。 该理论解释了电路层面的皮质功能。 通过反馈和内在连接之间的相互作用，神经元选择与任务相关的输入并抑制不相关的输入。 这些实验将结合视觉心理物理学、功能磁共振成像、大规模高密度电极阵列记录和光遗传学操作。 这些技术将用于测量当动物执行不同的视觉识别任务时，皮质区域之间的有效连接性的变化以及有效连接性与不同记录位点的神经元所代表的信息之间的关系。 将开发计算模型来解释如何实现任务相关的连接门控，并将重现实验观察到的功能动态。 尽管实验将重点关注视觉形态，但这项工作的发现将制定一个广泛适用于整个大脑的大脑功能的一般理论。