Neurophysiology underlying neural representations of value

了解决策背后的神经机制很重要，因为许多患者 精神疾病会做出适应不良的决定，影响执行功能，包括情绪和情绪 调控从历史上看，决策背后的机制大多是使用行为模型来研究的。 受试者反复对控制良好的刺激或选项做出决定的范式， 感知、评价、记忆或其他过程。这些研究提供了重要的洞察力，但许多 并非真实的世界中的大多数决策都是在与实验室中实现的情况截然不同的情况下发生的。 这类决策的两种典型类型是在以前从未遇到过的新情况下做出的决策， 那些要求受试者对熟悉的刺激产生新的反应，即在新的刺激中灵活地调整行为。 路上了在这项研究中，我们测试了这两种决策形式的潜在机制的假设， 通过检查神经表征的“几何学”并将其与任务调用中的行为联系起来， 两种类型的决定。制图表达的几何由点之间的所有距离的集合定义 在活动空间中，它们代表了多个神经元在不同条件下的反应。措施的 代表性几何学包括其维数评估。在新情况下的决策需要 从过去的经验归纳到新的经验，这是一种依赖于抽象的能力。抽象结构 变量描述由情况内和跨情况的实例共享的特征，捕获特征， 世界上的结构。抽象变量的神经表征与广泛研究的 机器学习中的解纠缠表示，并且具有较低的维度。另一方面，高 三维神经表征支持生成许多不同反应的能力，而不改变 底层代表。最近的数据表明，在海马神经合奏（HPC）和 前额叶皮层（PFC）实现几何与足够低维的“支架”，以支持泛化 在新的情况下，但支架是嵌入在一个更高的维度表示的任务变量。这 几何学具有特定的计算能力，但它们真的与决策有关吗？这里我们 联合收割机结合高通道计数电生理学、神经网络建模和精心设计的任务， 第一次证明几何体的这两个方面实际上用于支持两种不同的类型 决策过程我们研究了HPC和PFC中的几何表示， 两种类型。我们问，依赖抽象做出决策的能力是否与关键任务相关 变量表示在一个低维的支架（目标1）。然后我们测试表示是否编码了许多 其他具有更高维度的变量，如果这预测了学习产生新响应的能力（Aim 2）。最后，我们在相同的任务上训练神经网络模型，以阐明HPC中的神经编码原理， PFC，以及介导这两种决策形式的领域之间的相互作用（目标3）。

Understanding the neural mechanisms underlying decision-making is important because patients with many psychiatric disorders make mal-adaptive decisions, impacting executive functioning, including emotion and mood regulation. Historically, the mechanisms underlying decision-making have been most studied using behavioral paradigms in which subjects repeatedly make decisions about well-controlled stimuli or options, invoking perceptual, valuation, memory, or other processes. These studies have provided significant insight, but many if not most decisions in the real world occur in very different circumstances than those realized in the laboratory. Two paradigmatic types of such decisions are those made in novel situations never encountered before, and those that require subjects to generate new responses to familiar stimuli, i.e. to flexibly adjust behavior in a new way. In this grant, we test the hypothesis that mechanisms underlying these 2 forms of decision-making can be revealed by examining the 'geometry' of neural representations and relating them to behavior in tasks invoking the 2 types of decisions. The geometry of a representation is defined by the set of all distances between points in the activity space that represent responses of multiple neurons in different conditions. Measures of a representational geometry include assessment of its dimensionality. Decisions in novel situations require generalizing from past experiences to a new one, an ability relying on abstraction. Abstraction constructs variables describing features shared by instances within and across situations, capturing regularities and structure in the world. Neural representations of abstracted variables are similar to the widely studied disentangled representations in machine learning and have lower dimensionality. On the other hand, high dimensional neural representations support the ability to generate many different responses without changing the underlying representation. Recent data indicate that neural ensembles in the hippocampus (HPC) and prefrontal cortex (PFC) achieve geometries with a sufficiently low dimensional ‘scaffold’ to support generalization in new situations, but the scaffold is embedded in a higher dimensional representation of task variables. This geometry has specific computational capabilities, but do they actually relate to decision-making? Here we combine high-channel count electrophysiology, neural network modeling, and carefully designed tasks to provide evidence for the first time that these 2 aspects of the geometry actually are used to support the 2 distinct types of decision-making. We examine the geometry of representations in HPC and PFC in relation to decisions of both types. We ask if the ability to make decisions relying on abstraction correlates with how a key task-relevant variable is represented in a low-dimensional scaffold (Aim 1). Then we test if the representation encodes many other variables with higher dimensionality, and if this predicts the ability to learn to generate new responses (Aim 2). Finally, we train neural network models on the same tasks to elucidate neural coding principles in HPC and PFC, and interactions between areas mediating these 2 forms of decision-making (Aim 3).