Neural signatures of learning complex environments in the amygdala-prefrontal network

杏仁核前额叶网络中学习复杂环境的神经特征

• 批准号: 9915876
• 负责人: David Barack
• 金额: $ 6.73万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9915876
• 关键词: Amygdaloid structure; Animals; Architecture; Attenuated; Behavior; Behavioral; Behavioral Mechanisms; Behavioral Model; Code; Cognitive; Complex; Computer Models; Decision Making; Drops; Educational process of instructing; Electrophysiology (science); Environment; Equilibrium; Event; Feedback; Foundations; Functional disorder; Goals; Human; Institutes; Intake; Learning; Lesion; Light; Location; Macaca mulatta; Maps; Memory; Mind; Modeling; Monkeys; Neurons; Outcome; Performance; Play; Population; Process; Property; Psychological reinforcement; Research; Rewards; Role; Route; Sensory; Shapes; Signal Transduction; Stimulus; Synapses; Techniques; Temporal Lobe; Testing; Time; To specify; Training; Undifferentiated; Universities; Update; Visuospatial; Weight; brain behavior; cognitive function; deep neural network; high dimensionality; improved; learning algorithm; neural circuit; neural model; neuroimaging; neuromechanism; neurotransmission; nonhuman primate; novel; relating to nervous system; response; statistics; theories

The ability to learn and think about complex situations is central to a range of human cognitive functions, including navigation, reasoning, and decision making. Numerous theories across these domains rely on representations of states of these external and internal environments, but how they acquire such representations remains unknown. My overall goal is to understand how animals, including humans, can reason and learn in such complex environments. In this project, we propose to investigate how animals are able to learn these representations in a complex sequential decision making task in monkeys. Using a novel behavioral task inspired by the board game battleship, monkeys search for hidden shapes on a screen. There are millions of possible shapes, and yet monkeys are capable learners, vastly outperforming classic reinforcement learning algorithms. How monkeys can learn the shapes so quickly remains mysterious. In addition to these unknown computational foundations for learning, the neural mechanisms that support this behavior are also unexplored. Recent studies including electrophysiology and lesion research have found signatures of state representations in the amygdala (AMYG) and the orbitofrontal cortex (OFC). However, these studies have only used very few states that only require associations to learn. Moreover, the interactions and computational roles of the regions have not been characterized. In light of these gaps in our understanding of learning in complex tasks, we will use the battleship task to elucidate 1) the aspects of the environment that drive learning representations of complex states, 2) the computational foundations of this learning using behavioral model fitting and deep neural networks, and 3) the neural mechanisms that underwrite this capacity in the AMYG-OFC circuit. We hypothesize that OFC represents hidden task states, those that cannot be fully defined in terms of perceptible stimuli and outcomes. We further hypothesize that AMYG plays a central role in learning and updating these representations by constructing an online representation of the current environment using input from OFC as well as from sensory processing and memory regions, representing current stimuli, outcomes, and associations. We posit an observer-critic architecture underlies learning representations of complex tasks, with AMYG activity computing and sending a teaching signal to OFC that learns and updates task state representations. As part of this planned research, I will be trained in advanced modeling and neural analysis techniques, and complete a course of study on the use of deep neural networks. This training will take place under the guidance of Dr. Stefano Fusi and Dr. C Daniel Salzman in the Zuckerman Mind Brain Behavior Institute at Columbia University.

学习和思考复杂情况的能力是一系列人类认知的核心 功能，包括导航、推理和决策。围绕这些问题有许多理论 域依赖于这些外部和内部环境的状态表示，但是如何 他们获得这样的表征仍然未知。我的总体目标是了解如何 包括人类在内的动物可以在如此复杂的环境中进行推理和学习。在这个项目中， 我们建议研究动物如何能够在复杂的环境中学习这些表征 猴子的顺序决策任务。使用受董事会启发的新颖行为任务 游戏战舰，猴子在屏幕上寻找隐藏的形状。有数以百万计的可能 形状，但猴子是有能力的学习者，远远优于经典的强化 学习算法。猴子如何能够如此迅速地学习形状仍然是个谜。在 除了这些未知的学习计算基础之外，神经机制 支持这种行为也尚未探索。最近的研究包括电生理学和病变 研究发现杏仁核（AMYG）和大脑中的国家代表特征 眶额皮质（OFC）。然而，这些研究仅使用了极少数状态，仅 需要协会来学习。此外，区域的相互作用和计算作用 尚未被表征。鉴于我们对复杂学习的理解存在这些差距 任务中，我们将使用战舰任务来阐明 1) 驱动环境的各个方面 学习复杂状态的表示，2）这种学习的计算基础 使用行为模型拟合和深度神经网络，以及 3）神经机制 在 AMYG-OFC 电路中承保此容量。我们假设 OFC 代表隐藏 任务状态，那些无法根据可感知的刺激和结果来完全定义的任务状态。我们 进一步假设 AMYG 在学习和更新这些表示方面发挥着核心作用 通过使用 OFC 的输入构建当前环境的在线表示 来自感觉处理和记忆区域，代表当前的刺激、结果和 协会。我们假设观察者批评家架构是学习表示的基础 复杂的任务，通过 AMYG 活动计算并向 OFC 发送学习信号 并更新任务状态表示。作为这项计划研究的一部分，我将接受以下方面的培训： 先进的建模和神经分析技术，并完成使用的学习课程 深度神经网络。本次培训将在 Stefano Fusi 博士和 Dr. Stefano Fusi 的指导下进行。 哥伦比亚大学祖克曼心脑行为研究所的 C Daniel Salzman。