Acquiring cognitive maps: how brains learn hidden structure

获取认知图：大脑如何学习隐藏结构

• 批准号: 10739622
• 负责人: David Hocker
• 金额: $ 14.52万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739622
• 关键词: Address; Affect; Animal Behavior; Animals; Behavior; Behavioral; Behavioral Model; Biological; Biology; Bipolar Disorder; Brain; Chalk; Cognitive; Collaborations; Communication; Complement; Complex; Computational Technique; Computer Models; Cues; Decision Making; Dependence; Development Plans; Educational workshop; Electrophysiology (science); Environment; Exhibits; Experimental Designs; Exposure to; Faculty; Future; Goals; Health; Implant; Institution; Knowledge; Learning; Link; Machine Learning; Maps; Modeling; Neural Network Simulation; Neurons; Neurosciences; New York; Performance; Population; Population Dynamics; Positioning Attribute; Preparation; Procedures; Process; Protocols documentation; Psychological reinforcement; Psychology; Rattus; Research; Research Personnel; Rewards; Rodent; Rodent Model; Role; Schizophrenia; Science; Statistical Models; Structure; Subgroup; System; Testing; Therapeutic; Training; Universities; Water; Weight; Work; career; career development; deep neural network; deep reinforcement learning; experience; experimental study; improved; insight; learning strategy; neural; neural circuit; neural model; neuromechanism; neuropsychiatric disorder; novel; present value; recurrent neural network; response; symposium

Project Summary: Animals perform goal-directed behaviors in complex environments, without the need for extensive experience, by harnessing an internal model of the world. These internal models, or cognitive maps, are central to model-based decision making. Importantly, understanding the neural circuitry of model-based decisions holds promise for improving treatments of neuropsychiatric disorders in which decision-making goes awry. An outstanding question in reinforcement learning (RL) is how cognitive maps are learned from experience, and what neural substrates support them. The orbitofrontal cortex (OFC) has been implicated in representing cognitive maps, and this project proposes to characterize the emergence of a cognitive map in rodent OFC during a value-based decision making task. This project asks three core questions: 1) How does a model-based RL agent learn a cognitive map using purely model-free RL methods? Here recent advances in meta-RL in state-of- the-art recurrent neural network (RNN) models will be trained with model-free RL, in which the emergence of a cognitive map can be fully characterized. 2) How does the OFC represent cognitive maps? Here partially trained rats will be implanted with Neuropixels probes, and neural recordings will capture emerging representations of cognitive maps in population-level OFC activity as rats learn the task structure. 3) Can poor learning of cognitive maps be bolstered with structured behavioral training? An attractive therapeutic approach for improving decision making strategies is through behavioral training alone, and here an RNN model of rodents will be used to characterize modes of poor learning and develop prescriptive training, and will then be employed in rats to uniquely addresses specific learning deficits. This proposed project employs both experimental and computational techniques as part a comprehensive career development plan toward becoming an independent investigator. Specific experimental training for electrophysi- ological recordings from behaving animals complements computational training on modeling neural activity with deep neural networks. The career development plan also includes structured opportunities for collaborating with experimentalists. It incorporates a breadth of science communication experiences through research conferences and public talks within the local training institution. Importantly, the career development plan incorporates targeted preparation for independent investigator applications, including chalk-talk opportunities, workshops to develop unique research questions, and exposure to the faculty search process. The Constantinople, Savin, and Glim- cher labs at New York University’s Center for Neural Science are a training environment uniquely positioned to deliver this interdisciplinary training: their cutting-edge research into decision-making, animal behavior, reinforce- ment learning, and systems neuroscience make it an ideal institution and set of labs to develop a health-related career studying decision-making strategies in neuroscience.

项目摘要：动物在复杂环境中执行目标指导行为，而无需 通过利用世界内部模型来获得丰富的经验。这些内部模型或认知图 对于基于模型的决策至关重要。重要的是，了解基于模型的神经电路 决策有望改善决策进行的神经精神疾病的治疗 出了问题。增强学习（RL）的一个杰出问题是如何从经验中学到认知图， 以及神经底物的支持。在代表时隐含了眶额皮质（OFC） 认知图，以及该项目的建议，以表征啮齿动物OFC认知图的出现 基于价值的决策任务。该项目问三个核心问题：1）基于模型的RL如何 代理使用纯粹的无模型RL方法学习认知图？在这里，元rl的最新进展 ART复发性神经网络（RNN）模型将通过无模型RL训练，其中出现了 认知图可以充分表征。 2）OFC如何表示认知图？在这里受过部分训练 大鼠将植入神经质子问题，而神经记录将捕获新兴的表示形式 当大鼠学习任务结构时，人口级活动中的认知图。 3）学习不良 认知图被结构化行为训练鲜血吗？一种有吸引力的治疗方法 决策策略仅通过行为训练，在这里将使用RNN模型 为了表征学习不良和开发规定培训的模式，然后将老鼠雇用 独特地解决特定的学习缺陷。 这项拟议的项目员工是实验技术和计算技术的一部分 发展计划成为独立研究者。电力学的特定实验训练 来自行为动物的机学记录计算训练，以建模神经活动 深神经网络。职业发展计划还包括合作的结构化机会 实验者。它通过研究会议结合了科学沟通的广度 以及当地培训机构中的公众会谈。重要的是，职业发展计划纳入了目标 为独立调查员申请做准备，包括粉笔 - 谈论机会，开发研讨会 独特的研究问题，并接触教师搜索过程。君士坦丁堡，savin和Glim- 纽约大学神经科学中心的Cher Labs是一个独特地定位的培训环境 提供此跨学科培训：他们对决策，动物行为，增强的尖端研究 - 男性学习和系统神经科学使其成为理想的机构和一套实验室，以开发与健康有关的机构 职业研究神经科学中的决策策略。