Circuit mechanisms of arbitration between distinct reinforcement learning systems

不同强化学习系统之间的仲裁电路机制

• 批准号: 10608739
• 负责人: Margaret Louise DeMaegd
• 金额: $ 7.41万
• 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: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608739
• 关键词: Address; Anatomy; Animals; Arbitration; Behavior; Behavior monitoring; Behavioral Model; Brain; Chronic; Computing Methodologies; Confocal Microscopy; Corpus striatum structure; Data; Decision Making; Disease; Dorsal; Electrophysiology (science); Environment; Excitatory Postsynaptic Potentials; Exhibits; Future; Genetic; Goals; Human; Implant; In Vitro; Injections; Interneurons; Knowledge; Lateral; Lead; Learning; Logic; Measures; Medial; Mediating; Methods; Microelectrodes; Modeling; Neurons; Obsessive-Compulsive Disorder; Pattern; Population; Prefrontal Cortex; Process; Psyche structure; Psychological reinforcement; Rattus; Reporting; Structure; Synapses; System; Techniques; Testing; Theoretical Studies; Tracer; Uncertainty; Viral; Visual; Whole-Cell Recordings; Work; behavioral study; cell type; cognitive task; experience; flexibility; free behavior; in vivo; microscopic imaging; neural; neural circuit; novel; novel therapeutics; optogenetics; response; simulation; statistics

PROJECT SUMMARY Animals can exhibit goal-directed behaviors in novel environments, despite limited experience with them. How does the brain make and use inferences about the underlying statistics and generative structure of environments to guide behavior? The field of reinforcement learning refers to this capacity as “model-based” reasoning, meaning that it relies on an internal model of the structure of the world. Critically, this internal model can be used to flexibly estimate the best actions by mental simulation or planning, without direct experience. In contrast, in “model-free” reinforcement learning, an agent chooses the best action based on direct experience, without explicit knowledge of the underlying sequential transition structure of a task or environment. Model-based and model-free mechanisms coexist in the brain and are mediated by distinct circuits, although the neural circuit mechanisms by which the brain arbitrates between these decision systems remains unknown. Theoretical and behavioral studies suggest that human brains use the system that yields value estimates with the lowest uncertainty. The lateral orbitofrontal cortex (lOFC) is a compelling candidate to perform arbitration because while it is implicated in model-based reasoning, for instance by enabling inferences about hidden task states, it lies upstream of the dorsal striatum, which is critical for both model-based and model- free decision making. Intriguingly, we have found that lOFC neurons project exclusively to the dorsolateral striatum (DLS), a region critical for model-free behavior, and not the dorsomedial striatum (DMS), which is critical for model-based behavior. We hypothesize that projection specific neural circuits in lOFC arbitrate between these systems by suppressing the model-free system. I will use state-of-the-art viral, electrophysiological, and computational methods to determine whether DLS-projecting lOFC neurons mediate uncertainty-based arbitration between decision-making systems (Aim 1) and characterize the underlying circuit logic that supports arbitration (Aim 2). By optogenetically tagging DLS-projecting lOFC neurons I will selectively characterize and perturb their activity while monitoring the behavioral strategy rats use in a task with latent structure. To determine how arbitration is instantiated in the dorsal striatum I will optogenetically activate OFC→DLS neurons while recording from different genetic cell types in the striatum, in vivo and in vitro. We predict that OFC→DLS neurons enable model-based behavior by activating inhibitory interneurons to suppress the DLS and the model-free system.

项目概要 尽管经验有限，动物仍可以在新环境中表现出目标导向的行为 大脑如何对基础统计数据做出并使用推论？ 强化学习领域指的是环境的生成结构？ 将这种能力称为“基于模型”的推理，这意味着它依赖于事物的内部模型 至关重要的是，这个内部模型可以用来灵活地估计最佳结果。 相比之下，在“无模型”中，可以通过心理模拟或计划来采取行动，而无需直接经验。 强化学习，代理根据直接经验选择最佳动作，无需 任务或环境的底层顺序转换结构的显式知识。 基于模型和无模型的机制在大脑中共存，并由不同的介导 电路，尽管大脑在这些电路之间进行仲裁的神经电路机制 理论和行为研究表明人类的决策系统仍然未知。 大脑使用的系统可以产生不确定性最低的价值估计。 眶额皮层 (lOFC) 是执行仲裁的有力候选者，因为虽然它 涉及基于模型的推理，例如通过启用有关隐藏任务的推理 指出，它位于背侧纹状体的上游，这对于基于模型和基于模型的模型都至关重要 有趣的是，我们发现 lOFC 神经元只投射到 背外侧纹状体（DLS），一个对无模型行为至关重要的区域，而不是背内侧 纹状体（DMS），对于基于模型的行为至关重要。 lOFC 中的特定神经回路通过抑制无模型模型在这些系统之间进行仲裁 系统。 我将使用最先进的病毒、电生理学和计算方法来 确定 DLS 投射的 lOFC 神经元是否介导基于不确定性的仲裁 决策系统（目标 1）并表征支持的底层电路逻辑 通过光遗传学标记 DLS 投射的 lOFC 神经元，我将有选择地进行仲裁（目标 2）。 描述和扰乱它们的活动，同时监测大鼠在任务中使用的行为策略 为了确定仲裁如何在背侧纹状体中实例化，我将 光遗传学激活 OFC→DLS 神经元，同时记录不同遗传细胞类型 我们预测 OFC→DLS 神经元能够实现基于模型的纹状体。 通过激活抑制性中间神经元来抑制 DLS 和无模型系统来控制行为。