CRCNS: Inferring reference points from OFC population dynamics

CRCNS：从 OFC 人口动态推断参考点

基本信息

批准号：
10675077
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
项目类别：
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/10675077
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 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 Persons Population Population Dynamics Process Rattus Recording of previous events Retirement Rewards Savings Schizophrenia Silicon Structure Techniques Testing Training Wages Water Work behavior measurement behavioral economics dynamic system expectation experience experimental study improved neural neural circuit neuropsychiatric disorder novel preservation recurrent neural network reward processing tool

项目摘要

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一项关键计算是确定不同结果的价值。动物模型将结果评估为相对于内部参考点的增益或损失，可能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一个新工作的薪水特别高，但是当他们开始工作时，发现薪水少了很多，他们会将工资（即财富的净增加）视为相对于参考点的损失。依赖是一种必然的、普遍存在的现象，推动有关保险、金融的决策拟议的工作旨在揭示有多少人口。神经元代表基于价值的决策过程中的认知变量——参考点。工作涉及实验室中实验学家和理论学家之间互补、协同的互动分别是克里斯蒂娜·君士坦丁堡博士和克里斯蒂娜·萨文博士。该提案将开发一种新的行为范式来研究大鼠的参考依赖性，能够应用强大的工具来监测大规模的神经动力学。训练将产生数十个经过训练的受试者并行进行实验。量化大鼠行为关键方面的行为模型，包括行为的个体差异我们将使用具有高通道数的新型硅探针（“Neuropixels”探针）来进行跨动物研究。从数十只老鼠的神经元群体的行为记录中获得记录。眶额皮层（OFC），一个与基于价值的决策有关的关键大脑结构。开发新颖的潜在动力学模型，该模型将直接从群体中推断出参考点同时记录 OFC 中的神经元，而无需了解该模型的任务或行为。还将能够识别数十只老鼠常见的神经动力学方面，并且动物之间存在差异，反映了行为的个体差异（目标 2）。将使用补充的、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进行分析，这种方法将生成关于行为和神经数据的具体假设。神经电路架构在我们的任务中执行依赖于参考的主观评估（目标 3）。