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 动物暴露了一系列创意，自适应和灵活的行为。鸟类和私人改革 建造巢和工具的新材料；大鼠有效构建导航快捷方式，人类概括 了解一种语言可以有效说出另一种语言。这种动态创造新型行为的能力 或一些试验通常取决于复合计划，或者产生结局的新组合的能力 以目标定向方式的简单元素数量。尽管对理解的重要性 认知及其疾病，构成性的神经机制仍然未知，因为死亡的死亡 用于研究复合计划的实验框架。满足新的新需求 方法是，该提案将在我在一项新的绘画任务中阐明神经机制 弗莱瓦尔德实验室（Freiwald Lab），猕猴绘制了从未见过的视觉人物的副本。受试者的行为 在构建先前学到的新型组合的能力中表现出组成的关键签名 绘制新图像的元素。我将研究组成作用的神经和计算机制 通过整合此行为任务的计划，其他两项创新：（1）12额叶中的大规模录音 皮质区域，每个区域都与认知有关，但从未简单记录，这将使我发现 它们的独特功能如何结合以支持认知（AIM 1）和（2）综合分析框架 建立和比较神经网络（AIM 2）和符号（目标3）组成的计算模型 使用行为和神经数据进行计划。我将检验一个主要假设，即组成性取决于 在层次组织的额叶区域中实现符号认知算法的神经动力学。 预计这些研究将发现神经底物和动力学中的第一批机制 复合行动计划。此外，由于这些研究的交叉方法 ​​- 测试神经元 网络（AIM 2）和符号（AIM 3）建模框架在相同的数据上 - 它们可以统一这两个 认知的有影响力的方法，这将是神经科学的基本进步 智力。相应地，这项研究将有助于理解认知障碍，包括额叶 计划疾病，并构建从皮质活动中解释认知计划的脑机界面。 该奖项还将为我提供重要的培训，以使我为过渡到独立做好准备。我会训练 在计算建模中 - 建造，经验测试和解释这些模型 - 将支持我 使用模型生成和测试新型神经回路和计算假设。我会很重要 实验室管理和领导，科学沟通和授予写作方面的职业发展技能， 这将支持我的长期目标，即建立有关神经底物的独立研究计划 智力和创造性行为。