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.

项目摘要/摘要 动物表现出一系列具有创造性、适应性和灵活性的行为。鸟类和灵长类动物重新调整用途 建造巢穴和工具的新材料；老鼠高效地构建导航捷径，人类进行泛化 掌握一种语言的知识才能有效地说另一种语言。这种动态创建新奇行为的能力 或者一些试验通常取决于成分计划，或产生有限的新组合的能力 以目标为导向的方式的简单元素的数量。尽管它对理解 认知及其障碍，合成性的神经机制仍不清楚，因为缺乏 研究成分规划的实验框架。为了满足这一对新技术的迫切需求 方法，这项提议将阐明在一个新的绘画任务中的神经机制 Freiwald实验室，猕猴在实验室里画出以前从未见过的视觉形象的复制品。受试者的行为 在构建先前学到的新的组合的能力方面表现出构成性的关键特征 元素来绘制新图像。我将研究合成作用的神经和计算机制。 通过整合这项行为任务来计划其他两项创新：(1)在12个正面进行大规模录音 大脑皮层区域，每个区域都与认知有关，但从未同时记录，这将使我发现 它们的不同功能如何结合起来支持认知(目标1)和(2)综合分析框架 建立和比较神经网络(目标2)和符号(目标3)计算模型的成分 利用行为和神经数据进行规划。我将测试作曲性取决于 在分层组织的额叶皮质区域中实施符号认知算法的神经动力学。 这些研究有望在神经底物和动力学方面发现 组合式行动计划。此外，由于这些研究的交叉方法-测试神经 基于相同数据的网络(目标2)和符号(目标3)建模框架-它们可能会将这两个统一起来 有影响力的认知方法，这将是神经科学的基础性进展 智慧。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，以及建立脑机接口，从大脑皮层活动中解码认知计划。 这个奖项还将为我提供重要的培训，为我过渡到独立做好准备。我会训练的 在计算建模中-构建、经验性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的神经电路和计算假设。我将获得重要的 在实验室管理和领导、科学交流和拨款撰写方面的职业发展技能， 这将支持我建立一个关于神经基质的独立研究计划的长期目标 智力和创造性行为。