CRCNS: Inferring reference points from OFC population dynamics

所有哺乳动物都会进行的一个关键计算是确定不同结果的价值。人民和 动物模型评估结果是相对于内部参考点的收益或损失， 反映他们基于经验的期望。例如，如果有人被告知他们将收到 他们在新工作中获得了一份特别的薪水，但当他们开始工作时，他们发现薪水要少得多，他们会 将工资（财富的净增加）视为相对于参考点的损失。参考 依赖是一种必然的、普遍存在的现象，推动着有关保险、金融、 产品、劳动力和退休储蓄。这项拟议中的工作旨在揭示如何大量的人口 在基于价值的决策过程中，神经元代表认知变量--参考点。这 工作涉及实验室中实验者和理论家之间的互补、协同互动 分别是克莉丝汀·君士坦丁堡医生和克里斯蒂娜·萨文医生 这一提议将为研究大鼠的参照依赖性开发一种新的行为范式， 从而能够应用强大的工具来监测大规模神经动力学。High-throughput behavioral 训练将产生几十个训练过的受试者，用于并行实验。我们还将开发一个 行为模型，用于量化大鼠行为的关键方面，包括行为的个体差异 动物（目标1）。我们将使用具有高通道数的新型硅探针（“神经像素”探针）， 记录了几十只老鼠在行为过程中的神经元群体。录音将从 眶额皮层（OFC），一个与基于价值的决策有关的关键大脑结构。我们将 开发新的潜在动力学模型，直接从人口中推断参考点， 同时记录OFC中的神经元，而对任务或大鼠的行为没有任何了解。该模型 还将能够识别几十只老鼠共同的神经动力学方面， 在动物之间是可变的，反映了行为的个体差异（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循环神经网络 （RNN）对我们的行为和神经数据。这种方法将产生关于 在我们的任务中执行依赖于参考的主观评价的神经电路架构（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).