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.

项目总结/摘要 使用明确结构化的世界内部模型的能力是生物智能的核心， 认知障碍（例如痴呆和精神分裂症）受损。最近的研究表明， 诸如声明性记忆和导航的能力依赖于具有图结构的内部模型， 即由规则和变量组成。这种图形化的知识结构，或“模式”，现在被认为是 是什么使人类和动物一样，作出非凡的和系统的推论，概括， 在一次尝试中学习，想象：因此图式被理解为高级认知的基础。不过， 尽管如此重要，我们对大脑中的神经元是如何执行图式的却知之甚少。解决这个 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，我发现了一系列递归神经网络（RNN）-神经系统， 可以在大脑中实现-可以执行三个需要图式的认知任务：传递性 推理（TI）、关联推理（AI）和恒等规则推理（IRI）。重要的是，这些任务是 神经科学中传统图式任务的潜在更易处理的替代方案：事实上，初步分析 表明RNN使用一组可解动态机制来实现模式，因此 神经科学中以前不存在的机械假说。根据这些发现，我假设 这些候选机制用于海马体（HPC）和前额叶皮质（PFC）这两个大脑 架构所需的区域。为了验证这个指导性假设，我将解决和描述机制 在RNN（K99）中完成AI和TI（目标1）和IRI（目标2），然后通过实验探索机制， 高密度记录在HPC和PFC的大鼠执行这些任务在一个创新的嗅觉为基础的 范例（目标3）（R 00）。实现这些目标有可能建立模式是如何实现的 在大脑中，因此可以澄清高级认知的神经基础;这项工作也可以澄清 HPC和PFC的计算和行为作用，这两个大脑结构对认知至关重要。 这项工作的K99阶段将在哥伦比亚的Zuckerman大脑和行为研究所完成 在拉里·阿博特和约翰·坎宁安的指导下，两名权威人士将 就神经建模、动力系统和高级机器学习提供建议;此外， (Drs. Stefano Fusi、Vincent Ferrera、Daphna Shohamy、Rui Costa和Richard阿克塞尔）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，以及他们相关的科学和职业生涯 发展机遇。K99阶段的培训对于拟议的研究和 作为领导一个研究小组的研究人员，建立未来的科学和专业独立性。