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。这些理论遍布这些理论 域依赖于这些外部和内部环境的状态的表示，但是如何 他们获得此类代表仍然未知。我的总体目标是了解如何 包括人在内的动物可以在如此复杂的环境中推理和学习。在这个项目中， 我们建议调查动物如何在复杂中学习这些表示 猴子中的顺序决策任务。使用董事会启发的新颖行为任务 游戏战舰，猴子在屏幕上搜索隐藏的形状。有数百万可能 形状，但猴子却具有能力的学习者，大大优于经典增强 学习算法。猴子如何如此迅速地学习形状仍然是神秘的。在 除了这些未知的学习计算基础，以及神经机制 支持此行为也未开发。最近的研究包括电生理学和病变 研究发现，杏仁核（Amyg）和 眶额皮质（OFC）。但是，这些研究只使用了极少数的状态 需要协会学习。此外，区域的相互作用和计算角色 尚未表征。鉴于我们对复杂学习的理解 任务，我们将使用战舰任务阐明1）驱动环境的各个方面 复杂状态的学习表示，2）该学习的计算基础 使用行为模型拟合和深神经网络，以及3）神经机制 在Amyg-Of-Ofc电路中承销此容量。我们假设OFC表示隐藏 任务状态，那些不能完全根据可感知的刺激和结果来完全定义的。我们 进一步假设Amyg在学习和更新这些表示方面起着核心作用 通过使用OFC的输入来构建当前环境的在线表示 从感觉处理和记忆区域，表示当前刺激，结果和 协会。我们认为观察者批评的架构是 复杂的任务，通过AMYG活动计算并向OFC发送教学信号 并更新任务状态表示。作为这项计划研究的一部分，我将接受培训 高级建模和神经分析技术，并完成有关使用的研究课程 深神经网络。这项培训将在Stefano Fusi博士和博士的指导下进行。 C Daniel Salzman在哥伦比亚大学的Zuckerman Mind Brain行为研究所。