Neural dynamics and substrates of graphical knowledge

神经动力学和图形知识的基础

• 批准号: 10371663
• 负责人: Kenneth Norman Kay
• 金额: $ 12.54万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371663
• 关键词: Algebra; Animals; Behavior; Behavioral; Biological; Birds; Brain; Brain region; Cognition; Cognition Disorders; Cognitive Science; Collection; Communities; Complex; Creativeness; Data; Dementia; Disease; Environment; Event; Foundations; Future; Goals; Graph; Hippocampus (Brain); Human; Impaired cognition; Impairment; Insecta; Institutes; Intelligence; Knowledge; Language; Learning; Location; Machine Learning; Mammals; Memory; Mentorship; Modeling; Neural Network Simulation; Neurobiology; Neurons; Neurosciences; Pathologic; Pattern; Phase; Population; Prefrontal Cortex; Primates; Problem Solving; Rattus; Research; Research Personnel; Rodent; Role; Route; Schizophrenia; Scientific Advances and Accomplishments; Semantic memory; Semantics; Stimulus; Structure; System; Technical Expertise; Testing; Training; Universities; Work; authority; autism spectrum disorder; base; brain behavior; career development; cognitive ability; cognitive neuroscience; cognitive task; density; design; dynamic system; experience; experimental analysis; innovation; insight; mathematical theory; neural model; neural network; neurobiological mechanism; novel; recurrent neural network; relating to nervous system; way finding

Project Summary/Abstract The ability to use explicitly structured internal models of the world is both central to biological intelligence and impaired in disorders of cognition (e.g. dementia and schizophrenia). Recent work indicates that cognitive abilities such as declarative memory and navigation rely on internal models having the structure of graphs, i.e. composed of rules and variables. Such graphical knowledge structures, or ‘schemas,’ are now thought to be what enable humans and animals alike to make extraordinary and systematic inferences, to generalize, to learn in single trials, and to imagine: thus schemas are understood to be basis of advanced cognition. Still, despite this vital importance, little is known about how neurons in the brain implement schemas. Solving this overarching problem is my scientific goal, and the aim of the present proposal. In recent work, I have discovered a collection of recurrent neural networks (RNN) – neural systems that are plausibly implemented in the brain – that can perform three cognitive tasks requiring schemas: transitive inference (TI), associative inference (AI), and identity rule inference (IRI). Importantly, these tasks are potentially more tractable alternatives to traditional schematic tasks in neuroscience: indeed, initial analyses indicate that RNNs use a solvable set of dynamical mechanisms to implement schemas, and are thus mechanistic hypotheses that have not previously existed in neuroscience. Given these findings, I hypothesize that these candidate mechanisms are used in hippocampus (HPC) and prefrontal cortex (PFC), two brain regions required for schemas. To test this guiding hypothesis, I will solve and characterize the mechanisms accomplishing AI and TI (Aim 1) and IRI (Aim 2) in RNNs (K99), then probe mechanisms experimentally via high-density recordings in the HPC and PFC of rats performing these tasks in an innovative olfactory-based paradigm (Aim 3) (R00). Achieving these Aims has the potential to establish how schemas are implemented in the brain, and therefore can clarify the neural basis of advanced cognition; this work can also clarify computational and behavioral roles of HPC and PFC, two brain structures essential to cognition. The K99 phase of this work will be done in the Zuckerman Institute for Brain and Behavior at Columbia University under the mentorship of Larry Abbott and John Cunningham, two leading authorities who will advise on neural modelling, dynamical systems, and advanced machine learning; in addition, collaborators (Drs. Stefano Fusi, Vincent Ferrera, Daphna Shohamy, Rui Costa, and Richard Axel) will contribute scientific and technical expertise on cognitive tasks (design and neurobiological interpretation) and neural recordings. The training environment also includes the wider innovative and collaborative neuroscience community at Columbia, the Columbia Center for Theoretical Neuroscience, and their associated scientific and career development opportunities. Training in the K99 phase will be crucial for both the proposed research and for establishing future scientific and professional independence as an investigator leading a research group.

项目摘要/摘要 使用明确结构化的世界模型的能力既是生物智能的核心，又是 认知障碍受损（例如痴呆和精神分裂症）。最近的工作表明认知 诸如声明性内存和导航之类的能力依赖于具有图形结构的内部模型， 即由规则和变量组成。现在认为，这种图形知识结构或“架构” 成为使人类和动物都能做出非凡和系统的推论，推广，概括， 在单个试验中学习，并想象：因此，模式被理解为高级认知的基础。仍然， 尽管至关重要，但对大脑实施模式中的神经元如何了解。解决这个问题 总体问题是我的科学目标，也是本提议的目的。 在最近的工作中，我发现了一系列经常性神经网络（RNN） - 神经系统 在大脑中合理地实现 - 可以执行需要模式的三个认知任务：及时 推理（TI），关联推理（AI）和身份规则推理（IRI）。重要的是，这些任务是 在神经科学中，可能是传统示意图的可能更易于处理的替代方案：的确，初始分析 表明RNN使用一组可解决的动态机制来实现模式，因此是 神经科学中以前不存在的机械假设。有了这些发现，我假设 这些候选机制用于海马（HPC）和额叶皮层（PFC），两个大脑 图式所需的区域。为了检验该指导假设，我将解决并表征机制 在RNN（K99）中完成AI和Ti（AIM 1）和IRI（AIM 2），然后通过实验探测机制 在基于创新的嗅觉中执行这些任务的大鼠HPC和PFC中的高密度记录 范式（AIM 3）（R00）。实现这些目标有可能确定如何实施模式 在大脑中，可以阐明晚期认知的神经元基础；这项工作也可以澄清 HPC和PFC的计算和行为作用，两种对认知必不可少的大脑结构。 这项工作的K99阶段将在哥伦比亚的扎克尔曼大脑和行为研究所完成 在拉里·雅培（Larry Abbott）和约翰·坎宁安（John Cunningham）的指导下，大学 神经建模，动态系统和高级机器学习咨询；此外，合作者 （Stefano Fusi博士，Vincent Ferrera，Daphna Shohamy，Rui Costa和Richard Axel）将有助于科学 以及有关认知任务（设计和神经生物学解释）和神经记录的技术专长。 培训环境还包括更广泛的创新和协作神经科学社区 哥伦比亚哥伦比亚理论神经科学中心及其相关的科学和职业 发展机会。 K99阶段的培训对于拟议的研究和对 建立未来的科学和专业独立性，成为领导研究小组的研究人员。