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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：
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
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/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

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).

所有哺乳动物进行的一个关键计算是确定不同结果的值。人,