Neural dynamics and substrates of graphical knowledge

神经动力学和图形知识的基础

• 批准号: 10487519
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10487519
• 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.

项目概要/摘要 使用明确结构化的世界内部模型的能力对于生物智能和 认知障碍（例如痴呆症和精神分裂症）受损。最近的研究表明认知 陈述性记忆和导航等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“模式” 使人类和动物能够做出非凡和系统的推论、概括、总结 在单次试验中学习并想象：因此图式被理解为高级认知的基础。仍然， 尽管这一点至关重要，但人们对大脑中的神经元如何实现图式知之甚少。解决这个问题 首要问题是我的科学目标，也是本提案的目的。 在最近的工作中，我发现了一组循环神经网络（RNN）——神经系统 似乎在大脑中实现——可以执行三项需要图式的认知任务：及物 推理 (TI)、联想推理 (AI) 和同一性规则推理 (IRI)。重要的是，这些任务是 神经科学中传统示意性任务的潜在更容易处理的替代方案：确实，初步分析 表明 RNN 使用一组可解的动态机制来实现模式，因此 以前神经科学中不存在的机制假设。鉴于这些发现，我假设 这些候选机制用于海马体 (HPC) 和前额皮质 (PFC)，这两个大脑 模式所需的区域。为了检验这个指导性假设，我将解决并描述这些机制 在 RNN (K99) 中实现 AI 和 TI（目标 1）和 IRI（目标 2），然后通过实验探索机制 大鼠的 HPC 和 PFC 中的高密度记录以创新的基于嗅觉的方式执行这些任务 范例（目标 3）（R00）。实现这些目标有可能确定模式的实施方式 在大脑中，因此可以阐明高级认知的神经基础；这项工作还可以澄清 HPC 和 PFC 这两种对认知至关重要的大脑结构的计算和行为作用。 这项工作的 K99 阶段将在哥伦比亚大学祖克曼大脑与行为研究所完成 大学在拉里·阿博特（Larry Abbott）和约翰·坎宁安（John Cunningham）的指导下，这两位权威人士将 就神经建模、动力系统和高级机器学习提供建议；此外，合作者 （Stefano Fusi、Vincent Ferrera、Daphna Shohamy、Rui Costa 和 Richard Axel 博士）将贡献科学成果 以及认知任务（设计和神经生物学解释）和神经记录方面的技术专业知识。 培训环境还包括更广泛的创新和协作神经科学社区 哥伦比亚大学、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 作为领导研究小组的研究者，建立未来的科学和专业独立性。