Neural dynamics and substrates of graphical knowledge

项目摘要/摘要 使用明确结构的世界内部模型的能力既是生物智能的核心，也是 认知障碍(如痴呆症和精神分裂症)。最近的研究表明，认知能力 诸如声明性存储器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。这种图形化的知识结构，或称“模式”，现在被认为 是什么使人类和动物一样能够做出非凡的和系统的推论，概括， 在单次实验中学习，并想象：因此，图式被理解为高级认知的基础。不过， 尽管这一点至关重要，但人们对大脑中的神经元如何执行图式知之甚少。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，我发现了一组递归神经网络(RNN)-神经系统 似乎是在大脑中实现的--它可以执行三种需要图式的认知任务：传递性 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 神经科学中传统图式任务的潜在更易处理的替代方案：确实，初步分析 表明RNN使用一组可解动态机制来实现模式，因此 神经科学中从未出现过的机械论假说。考虑到这些发现，我假设 这些候选机制在海马区(HPC)和前额叶皮质(PFC)这两个大脑中使用 架构所需的区域。为了检验这一指导性假说，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 在HPC和PFC中高密度记录大鼠在创新的基于嗅觉的 范例(目标3)(R00)。实现这些目标有可能确定如何实现模式 从而可以阐明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阶段将在哥伦比亚大学的扎克曼大脑和行为研究所完成 大学在拉里·阿博特和约翰·坎宁安的指导下，这两位权威人士将 神经建模、动力系统和高级机器学习方面的建议；此外，合作者 (Stefano Fusi博士、文森特·费雷拉博士、Daphna Shohamy博士、Rui Costa博士和Richard Axel博士)将做出科学贡献 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，网址为 哥伦比亚大学理论神经科学中心及其相关的科学和职业 发展机遇。K99阶段的培训对于拟议的研究和 建立未来的科学和专业独立性，成为领导研究小组的调查员。

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.