CRCNS: Inferring reference points from OFC population dynamics

CRCNS：从 OFC 人口动态推断参考点

• 批准号: 10261540
• 负责人: Christine Marie Constantinople
• 金额: $ 33.72万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-11 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10261540
• 关键词: Affect; Animal Model; Animals; Architecture; Automobile Driving; Behavior; Behavioral; Behavioral Model; Behavioral Paradigm; Biological Models; Bipolar Disorder; Birds; Brain; Capuchin Monkey; Choice Behavior; Chronic; Cognitive; Collaborations; Complex; Consequentialism; Data; Decision Making; Decision Theory; Dependence; Dimensions; Disease; Future; Health; Human; Impairment; Implant; Individual; Individual Differences; Insurance; Knowledge; Machine Learning; Mammals; Mental disorders; Modeling; Modernization; Monitor; Neurons; Occupations; Outcome; Population; Population Dynamics; Process; Rattus; Recording of previous events; Retirement; Rewards; Savings; Schizophrenia; Silicon; Structure; Techniques; Testing; Training; Wages; Water; Work; base; behavior measurement; behavioral economics; dynamic system; expectation; experience; experimental study; improved; neural circuit; neuropsychiatric disorder; novel; preservation; recurrent neural network; relating to nervous system; reward processing; tool

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).

所有哺乳动物执行的关键计算是确定不同结果的值。人们和 动物模型评估结果是相对于内部参考点的收益或损失，可能 反映他们基于经验的期望。例如，如果有人告诉有人会收到 在新工作中特别薪水，但是当他们开始时，他们发现薪水要少得多，他们将 认为薪水（这是财富的净增加）是相对于其参考点的损失。参考 依赖性是一种随之而来的普遍现象，推动有关保险，财务的决定 产品，人工和退休储蓄。拟议的工作旨在发现大量人口 神经元代表认知变量 - 基于价值决策期间的参考点。这 工作涉及实验者与实验室理论家之间的完整性，协同互动 克里斯汀·君士坦丁（Christine Constantinople）和克里斯蒂娜·萨维（Cristina Savin）博士的作品。 该建议将开发出一种新的行为范例，用于研究大鼠的参考依赖性， 实现强大的工具来监视大规模的神经动力学。高通量行为 培训将产生数十名训练有素的受试者，以并联实验。我们还将开发一个 量化大鼠行为关键方面的行为模型，包括行为的个体差异 跨动物（目标1）。我们将使用具有高频道计数的新硅问题（“神经偶像”问题） 在行为过程中，来自数十只大鼠的神经元种群的记录。录音将从 Orbitrontal Cortex（OFC），这是一种在基于价值的决策中实现的关键大脑结构。我们将 开发新型潜在动力学模型，这些模型将直接从人群中推断出参考点 类似地记录在OFC中的神经元，而没有任何对任务或大鼠行为的了解。这个模型 还将能够识别几十只大鼠和 各种动物变化的方面，反映了行为的个体差异（AIM 2）。最后，我们 将使用完整的，最先进的机器学习技术来训练经常性的神经网络 （RNN）关于我们的行为和神经数据。这种方法将产生有关该方法的具体假设 神经电路体系结构在我们的任务中执行参考依赖性主观值（AIM 3）。