Prefrontal-Hippocampal Interactions during Model-Based Learning

项目摘要 前额叶皮质(PFC)和海马体(HPC)功能障碍与许多 神经精神障碍，包括精神分裂症、严重抑郁症和创伤后应激障碍。许多 这些障碍的行为症状可以被建模为功能障碍强化学习(RL) 流程。例如，未能最佳地平衡目标导向(“基于模型”或MB)和习惯性 (“无模型”，或称MF)控制可以解释强迫症患者的沉思。我们研究的首要目标是让人们 以一种原则性的方式与神经回路相互作用治疗神经精神疾病的设备的未来发展 精神错乱。这种方法的一个障碍是，这些电路中的许多神经编码仍然很差 明白了。目前拨款的目的是研究HPC和PFC的神经元特性，以及如何 这些结构相互作用。 对于依赖于模型的基于奖励的学习过程，HPC-PFC电路可能起到重要作用 对环境的影响。长期以来，HPC一直与代表编码 环境的结构。它与内侧PFC(MPFC)是双向连接的，已经被牵连 在基于奖励的学习和基于价值的决策方面。我们的假设是HPC-PFC对MB至关重要 RL，通过HPC表示任务的预测性地图，并将该地图传送到mPFC以允许价值推断 引导人们的行为。我们建模这个过程的理论构造是后续表示(SR)，它 同时学习任务的地图和奖励的偶发事件，然后通过整合来评估潜在的行动 带有学习的奖赏偶发事件的预测地图。 为了验证这一假设，我们开发了一个抽象的觅食任务，要求受试者导航到 隐藏的状态空间来寻找奖励。要以最佳方式解决这一任务，主体必须参与MB RL并开发 状态空间的内部映射。该地图允许受试者存储最近奖励的位置 然后正确选择到达奖励位置所需的动作。首先，我们将从单曲开始录制 MPFC和HPC中的神经元，然后同时记录mPFC和海马区的神经元，以检查这些 在MB RL期间，区域之间相互通信。 综上所述，这项建议的结果将扩大我们对高性能混凝土的角色和互动的理解 和灵长类动物大脑中的PFC。这一知识不仅将有助于改进临床诊断工具的努力 精神病学，但也可以为开发神经假体设备奠定基础，这种设备将与 以一种有原则的方式治疗神经精神障碍的神经回路。

Project Summary Dysfunction of the prefrontal cortex (PFC) and the hippocampus (HPC) has been implicated in many neuropsychiatric disorders, including schizophrenia, major depression, and post-traumatic stress disorder. Many of the behavioral symptoms of these disorders can be modeled as dysfunctional reinforcement learning (RL) processes. For example, a failure to optimally balance goal-directed (“model-based”, or MB) and habitual (“model-free”, or MF) control can explain rumination in OCD. An overarching goal of our research is to inform the future development of devices that will interact with neural circuits in a principled way to treat neuropsychiatric disorders. One impediment to this approach is that the neural coding in many of these circuits remains poorly understood. The aim of the current grant is to investigate the neuronal properties of HPC and PFC, and how these structures interact with each other. The HPC-PFC circuit may play an important role for reward-based learning processes that depend on a model of the environment. The HPC has long been associated with representing a ‘cognitive map’ that encodes the structure of the environment. It is bidirectionally connected with medial PFC (mPFC), which has been implicated in reward-based learning and value-based decision-making. Our hypothesis is that the HPC-PFC is critical for MB RL, via the HPC representing a predictive map of task, and communicating this map to mPFC to allow value inferences that guide behavior. Our theoretical construct for modeling this process is the successor representation (SR), which learns a map of the task in parallel with reward contingencies, and then evaluates potential actions by integrating the predictive map with the learned reward contingencies. To test this hypothesis, we have developed an abstract foraging task that requires the subject to navigate a hidden state space to find a reward. To solve this task optimally, the subject must engage in MB RL and develop an internal map of the state space. This map allows the subject to store the location of the most recent reward and then correctly select the necessary actions to reach the rewarded location. First, we will record from single neurons in mPFC and HPC, then record simultaneously from mPFC and the hippocampus to examine how these regions communicate with each other during MB RL. Taken together, the results of this proposal will expand our understanding of the roles and interaction of HPC and PFC in the primate brain. This knowledge will not only inform efforts to improve diagnostic tools in clinical psychiatry but can also lay the groundwork for the development of neuroprosthetic devices that will interact with neural circuits in a principled way to treat neuropsychiatric disorders.