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

学习和思考复杂情况的能力是人类认知能力的核心。 功能，包括导航，推理和决策。众多的理论 域依赖于这些外部和内部环境的状态的表示，但如何 他们获得这样的表示仍然是未知的。我的总体目标是了解 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， 我们打算研究动物是如何在一个复杂的环境中学习这些表征的。 猴子的顺序决策任务。使用一种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，大大优于经典的强化 学习算法猴子如何能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， 支持这种行为也是未知的。最近的研究包括电生理和损伤 研究发现，杏仁核（amygdala，AMYG）中的状态表征特征， 眶额皮质（OFC）。然而，这些研究只使用了很少的州， 需要社团来学习。此外，区域的相互作用和计算作用 还没有被定性。鉴于我们对复杂环境中学习的理解存在这些差距， 任务，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电路中承保该容量。我们假设眶额皮层代表了隐藏的 任务状态，那些不能完全用可感知的刺激和结果来定义的状态。我们 进一步假设AMYG在学习和更新这些表征中起着核心作用 通过使用OFC的输入构建当前环境的在线表示， 从感觉处理和记忆区域，代表当前的刺激，结果， 协会.我们认为，一个批评者-批评者体系结构是学习表征的基础， 复杂的任务，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， 先进的建模和神经分析技术，并完成一个关于使用 深度神经网络本次培训将在Stefano Fusi博士和Dr. 哥伦比亚大学朱克曼大脑行为研究所的C.丹尼尔.萨尔兹曼。

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.