CRCNS: Inferring reference points from OFC population dynamics

所有哺乳动物进行的一项关键计算是确定不同结果的价值。人和 动物模型可能会以相对于内部参照点的得失来评估结果 反映了他们基于经验的期望。例如，如果有人被告知他们将收到 一份新工作的特定薪水，但当他们开始工作时，他们发现工资大幅下降，他们会 将工资(财富的净增长)视为相对于他们的参考点的损失。参考 依赖是一种随之而来的、无处不在的现象，推动着关于保险、金融 产品、劳动力和退休储蓄。这项拟议的工作旨在揭示 神经元代表了基于价值的决策过程中的一个认知变量--参照点。这 这项工作涉及实验室中实验者和理论家之间的互补、协同作用。 分别是克里斯汀·君士坦丁堡博士和克里斯蒂娜·萨文博士。 这一建议将开发一种新的行为范式来研究大鼠的参照依赖， 能够应用强大的工具来监测大规模的神经动力学。高吞吐量行为 培训将产生数十个受过训练的受试者，用于并行进行实验。我们还将开发一种 行为模型，量化大鼠行为的关键方面，包括行为的个体差异 跨动物(目标1)。我们将使用具有高通道数的新型硅探测器(“神经像素”探测器)来 记录了几十只大鼠在行为过程中的神经元数量。录音将从以下地点获得 眶前叶皮质(OFC)，这是一个关键的大脑结构，牵涉到基于价值的决策。我们会 开发新的潜在动力学模型，直接从种群中推断参考点 同时记录OFC中的神经元，而不知道任务或大鼠的行为。这款车 还将能够识别数十只大鼠共同存在的神经动力学方面，以及 不同动物的特征是不同的，反映个体行为的差异(目标2)。最后，我们 将使用互补的、最先进的机器学习技术来训练递归神经网络 (RNN)我们的行为和神经数据。这种方法将产生关于以下方面的具体假设 在我们的任务中，神经电路架构执行依赖于参考的主观评估(目标3)。

A key computation that all mammals perform is determining the value of different outcomes. People and animal models evaluate outcomes as gains or losses relative to an internal reference point, likely reflecting their experience-based expectations. For example, if someone is told they will receive a particular salary at a new job, but when they start, they find that the salary is substantially less, they will view that salary (which is a net increase in wealth) as a loss relative to their reference point. Reference dependence is a consequential, ubiquitous phenomenon, driving decisions about insurance, financial products, labor, and retirement savings. The proposed work seeks to uncover how large populations of neurons represent a cognitive variable –the reference point- during value-based decision-making. This work involves complementary, synergistic interactions between experimentalists and theorists in the labs of Dr. Christine Constantinople and Dr. Cristina Savin, respectively. This proposal will develop a novel behavioral paradigm for studying reference dependence in rats, enabling application of powerful tools to monitor large-scale neural dynamics. High-throughput behavioral training will generate dozens of trained subjects for experiments in parallel. We will also develop a behavioral model to quantify key aspects of rats' behavior, including individual differences in behavior across animals (Aim 1). We will use new silicon probes with high channel counts (“Neuropixels” probes) to record from populations of neurons in dozens of rats during behavior. Recordings will be obtained from the orbitofrontal cortex (OFC), a key brain structure implicated in value-based decision-making. We will develop novel latent dynamics models that will infer the reference point directly from populations of simultaneously recorded neurons in OFC, without any knowledge of the task or rats' behavior. This model will also be able to identify aspects of neural dynamics that are common across dozens of rats, and aspects that are variable across animals, reflecting individual differences in behavior (Aim 2). Finally, we will use complementary, state-of-the-art machine-learning techniques to train recurrent neural networks (RNNs) on our behavioral and neural data. This approach will generate concrete hypotheses about the neural circuit architectures performing reference-dependent subjective valuation in our task (Aim 3).