CAREER: Neural circuit mechanisms of inference: how brains learn and use hidden structure

职业：推理的神经回路机制：大脑如何学习和使用隐藏结构

• 批准号: 2042796
• 负责人: Christine Constantinople
• 金额: $ 125万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042796&HistoricalAwards=false
• 关键词: CAREER; Neural; circuit; mechanisms; inference

Remarkably, animals can exhibit sophisticated behaviors in novel environments, despite having limited experience with them. How does the brain make inferences about the underlying statistics and generative structure of environments, and use those inferences to guide behavior? While specific brain regions have been implicated in this capacity, it is unclear how patterns of neural connections and activity in these regions mediate inference and sophisticated reasoning. This project delineates the neural circuit mechanisms spanning multiple brain regions by which animals make inferences and use those inferences to guide their behavior. The research group of this study has recently developed a high-throughput behavioral training facility for rats that trains dozens of rats (~60) per day on sophisticated cognitive behaviors. The group leverages this high-throughput approach to relate individual differences in rats’ strategies to differences in neural connectivity and neural activity recorded during behavior. In parallel, the principal investigator develops a workshop for high school students in New York City that applies lessons from neuroscience, psychology, and economics to improve financial literacy and decision-making. Financial literacy provides a unique outreach opportunity for increasing awareness of neuroscience research and communicating cutting edge neuroscientific results to the broader public, in the context of a highly salient and relatable topic: personal finance. While the research project seeks to understand the neural mechanisms by which brains make inferences to guide decision-making, the educational activities synergistically seek to educate students about the neuroscience behind their economic choices, and empower them to make better ones.This project examines whether and elucidates how neurons in the lateral orbitofrontal cortex (lOFC) make synaptic connections with genetically-defined cell types in the dorsomedial striatum (DMS) in service of model-based reinforcement learning. Rats are trained on a novel task with hidden structure that, if inferred, provides a clear behavioral read-out of model-based reasoning. Viral tracing, multi-regional paired recordings during behavior, and projection-specific recordings and inactivations are deployed to identify the dynamics and connectivity of subcircuits within the lOFC that generate model-based inferences. These experiments are performed in dozens of rats, and leverage individual differences in behavior to identify projection-specific prefrontal subnetworks that subserve model-based reinforcement learning. The project provides a circuit- and synaptic-level understanding of a core computation, that of an economic reference point against which outcomes are evaluated, compared, and chosen, in rats that infer reference points according to an internal model of the task. The results of this work will have broad implications for the fields of neuroscience, psychology, and machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

值得注意的是，动物可以在新的环境中表现出复杂的行为，尽管他们的经验有限。大脑如何对环境的基本统计数据和生成结构进行推断，并使用这些推断来指导行为？虽然特定的大脑区域与这种能力有关，但目前还不清楚这些区域的神经连接和活动模式如何介导推理和复杂的推理。这个项目描绘了跨越多个大脑区域的神经回路机制，动物通过这些机制进行推理并使用这些推理来指导它们的行为。这项研究的研究小组最近开发了一种高通量的大鼠行为训练设施，每天训练数十只大鼠（约60只）进行复杂的认知行为。该小组利用这种高通量方法将大鼠策略的个体差异与行为期间记录的神经连接和神经活动的差异联系起来。与此同时，首席研究员为纽约市的高中生开设了一个研讨会，应用神经科学、心理学和经济学的课程来提高金融知识和决策能力。金融知识提供了一个独特的外展机会，以提高对神经科学研究的认识，并在一个非常突出和相关的主题：个人理财的背景下，向更广泛的公众传达尖端的神经科学成果。虽然该研究项目旨在了解大脑做出推断以指导决策的神经机制，但教育活动协同努力，旨在教育学生了解他们经济选择背后的神经科学，本项目旨在研究外侧眶额皮质（IOFC）中的神经元是否以及如何与遗传性神经元建立突触联系，在背内侧纹状体（DMS）中定义细胞类型，以服务于基于模型的强化学习。老鼠被训练完成一项具有隐藏结构的新任务，如果推断出该任务，则可以提供基于模型的推理的清晰行为读数。部署病毒追踪、行为期间的多区域配对记录以及投射特定记录和失活，以识别IOFC内生成基于模型的推断的子电路的动态和连接性。这些实验在数十只大鼠中进行，并利用行为的个体差异来识别有助于基于模型的强化学习的投射特定的前额叶子网络。该项目提供了一个核心计算的电路和突触水平的理解，即对结果进行评估，比较和选择的经济参考点，在大鼠中根据任务的内部模型推断参考点。这项工作的结果将对神经科学、心理学和机器学习领域产生广泛的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。