Investigating Symbolic Computation in the Brain: Neural Mechanisms of Compositionality

研究大脑中的符号计算：组合性的神经机制

• 批准号: 10644518
• 负责人: Lucas Y. Tian
• 金额: $ 13.43万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-16 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644518
• 关键词: Address; Algorithms; Animals; Area; Award; Behavior; Behavioral; Behavioral Model; Birds; Brain; Categories; Cognition; Cognition Disorders; Cognitive; Communication; Complex; Computer Models; Creativeness; Data; Decision Making; Disease; Elements; Evaluation; Exhibits; Goals; Grant; Human; Image; Influentials; Intelligence; Knowledge; Language; Leadership; Learning; Macaca; Modeling; Motor; Neural Network Simulation; Neurons; Neurosciences; Pattern; Primates; Property; Rattus; Research; Role; Shapes; Stroke; Task Performances; Testing; Theoretical Studies; Time; Training; Validation; Variant; Visual; Work; Writing; brain machine interface; career development; cognitive function; cognitive task; flexibility; frontal lobe; innovation; model building; multidisciplinary; neural; neural circuit; neural network; neuromechanism; neurophysiology; novel; novel strategies; operation; predictive modeling; programs; skill acquisition; success; syntax; tool; visual learning; visual motor

PROJECT SUMMARY/ABSTRACT Animals exhibit a remarkable array of creative, adaptive, and flexible behaviors. Birds and primates repurpose new materials to build nests and tools; rats efficiently construct navigational shortcuts, and humans generalize knowledge of one language to efficiently speak another. This ability to dynamically create novel behavior in one or a few trials often depends on compositional planning, or the ability to generate new combinations of a finite number of simple elements in a goal-directed manner. Despite its central importance for understanding cognition and its disorders, the neural mechanisms of compositionality remain unknown as there is a dearth of experimental frameworks for studying compositional planning. To address this critical need for new approaches, this proposal will elucidate neural mechanisms in a novel drawing task that I have developed in the Freiwald lab, in which macaques draw copies of never-before-seen visual figures. Subjects’ behavior exhibits a key signature of compositionality in the ability to construct novel combinations of previously learned elements to draw new images. I will investigate neural and computational mechanisms for compositional action planning by integrating this behavioral task two other innovations: (1) large-scale recordings in 12 frontal cortical areas, each implicated in cognition but never recorded simultaneously, which will allow me to discover how their distinct functions combine to support cognition (Aim 1), and (2) an integrative analysis framework building and comparing neural network (Aim 2) and symbolic (Aim 3) computational models of compositional planning with behavioral and neural data. I will test the main hypothesis that compositionality depends on neural dynamics implementing symbolic cognitive algorithms in hierarchically organized frontal cortical areas. These studies are expected to discover the first mechanisms, in neural substrates and dynamics, of compositional action planning. Further, because of these studies’ intersectional approach - testing neural network (Aim 2) and symbolic (Aim 3) modeling frameworks on the same data - they may unify these two influential approaches to cognition, which would be a foundational advance for the neuroscience of intelligence. Correspondingly, this study will contribute to understanding cognitive disorders, including frontal planning disorders, and to building brain-machine interfaces that decode cognitive plans from cortical activity. This award will also provide me with crucial training to prepare me for transitioning to independence. I will train in computational modeling - building, empirically testing, and interpreting these models - which will support my use of models to generate and test novel neural circuit and computational hypotheses. I will gain important career development skills in lab management and leadership, scientific communication, and grant writing, which will support my long term goal of establishing an independent research program on the neural substrates of intelligence and creative behavior.

项目概要/摘要 动物表现出一系列非凡的创造性、适应性和灵活的行为，鸟类和灵长类动物也能重新调整用途。 建造巢穴的新材料和工具；老鼠有效地构建了导航捷径，人类也进行了推广 掌握一种语言以有效地使用另一种语言的能力。 或者一些试验通常取决于成分规划，或者生成有限的新组合的能力 尽管它对于理解至关重要，但以目标为导向的方式包含了许多简单的元素。 认知及其障碍，组合性的神经机制仍然未知，因为缺乏 研究构图规划的实验框架来满足新的迫切需求。 方法，该提案将阐明我在一项新颖的绘图任务中开发的神经机制 弗赖瓦尔德实验室，猕猴在其中绘制了从未见过的视觉图形的副本。 表现出组合性的关键特征，即能够构建先前学到的新颖组合 我将研究构图动作的神经和计算机制。 通过整合此行为任务以及其他两项创新来进行规划：(1) 12 个额叶的大规模记录 皮质区域，每个区域都与认知有关，但从未同时记录，这将使我能够发现 它们不同的功能如何结合起来支持认知（目标 1），以及（2）综合分析框架 构建并比较组合的神经网络（目标 2）和符号（目标 3）计算模型 我将用行为和神经数据来测试组合性所依赖的主要假设。 在分层组织的额叶皮层区域中实施符号认知算法的神经动力学。 这些研究有望发现神经基质和动力学方面的第一个机制 此外，由于这些研究的交叉方法 ​​- 测试神经。 对相同数据的网络（目标 2）和符号（目标 3）建模框架 - 它们可以统一这两个 有影响力的认知方法，这将是神经科学的基础性进步 相应地，这项研究将有助于理解认知障碍，包括额叶。 计划障碍，以及构建脑机接口，从皮层活动中解码认知计划。 该奖项还将为我提供重要的培训，帮助我为过渡到独立做好准备。 在计算建模中——构建、实证测试和解释这些模型——这将支持我的 使用模型来生成和测试新颖的神经电路和计算假设我将获得重要的成果。 实验室管理和领导、科学沟通和资助写作方面的职业发展技能， 这将支持我建立神经基质独立研究计划的长期目标 智力和创造性行为。