Neural circuit mechanisms of arithmetic for economic decision-making

经济决策算法的神经回路机制

• 批准号: 10002804
• 负责人: Christine Marie Constantinople
• 金额: $ 237.75万
• 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: 2020-09-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10002804
• 关键词: Animals; Arithmetic; Automobile Driving; Behavior; Behavioral; Behavioral Model; Brain; Choice Behavior; Cognitive; Consequentialism; Decision Making; Dependence; Dissection; Economics; Exhibits; Goals; Human; Individual; Interneurons; Judgment; Mammals; Neurons; Neurosciences; Outcome; Pattern; Process; Rattus; Signal Transduction; Synapses; System; Techniques; Training; Transgenic Organisms; Viral; Work; behavioral economics; design; in vivo; individual variation; neural circuit; neurochemistry; neuromechanism; neuroregulation; novel; relating to nervous system; response

Project Summary A central challenge in neuroscience is to understand how the connectivity patterns and dynamics of local and long-range synaptic inputs enable behaviorally-relevant computations in individual neurons. A fundamental computation that all mammals perform is determining the value of different outcomes, including their valence, or whether they are perceived as a gain or a loss. Behavioral economics provides a useful quantitative framework for describing how people and animals subjectively assign value to outcomes, and use those value estimates to make decisions. Here, we aim to understand the multi-regional neural circuit mechanisms by which economic variables driving decision-making are computed and represented by neurons in the brain. A hallmark of economic choice behavior is that people exhibit “reference dependence,” wherein they evaluate outcomes as gains or losses relative to an internal reference point. A related phenomenon, called “loss aversion,” refers to the observation that most people are more sensitive to losses than to equivalent gains. This proposal will combine state-of-the-art viral and transgenic approaches for circuit dissection, in vivo paired recordings of long-range synaptically connected neurons whose responses have been characterized during behavior, novel techniques for neurochemical sensing, high-throughput behavioral training of rats, and quantitative behavioral modeling to identify how neural representations of quantifiable cognitive variables -the reference point and loss aversion- derive from dynamics and patterns of local and long range synapses. Specifically, the proposed work will delineate the thalamocortical circuitry supporting reference-dependent computations, determine the circuit mechanisms of arithmetic subtraction of the reference point from value signals, and identify neuromodulatory systems driving individual variability in loss aversion. The results will bridge cellular, circuit, and systems-level descriptions of neural mechanisms underlying consequential economic judgments, while revealing general neural circuit motifs supporting arithmetic computations including summation, subtraction, and multiplication.

项目概要 神经科学的一个核心挑战是了解神经网络的连接模式和动态如何 局部和远程突触输入可以在单个神经元 A 中进行行为相关的计算。 所有哺乳动物执行的基本计算是确定不同结果的值，包括 它们的效价，或者它们是否被视为收益或损失，行为经济学提供了有用的信息。 用于描述人和动物如何主观地为结果分配价值的定量框架，并使用 在这里，我们的目标是了解多区域神经回路。 驱动决策的经济变量由神经元计算和表示的机制 在大脑中。 经济选择行为的一个标志是人们表现出“参考依赖”，他们 将结果评估为相对于内部参考点的收益或损失。 “损失厌恶”是指大多数人对损失比对同等损失更敏感的观察结果 该提案将结合最先进的病毒和转基因方法进行体内电路解剖。 长程突触连接神经元的配对记录，其反应已被表征 在行为过程中，神经化学传感的新技术，大鼠的高通量行为训练，以及 行为建模来确定可量化认知变量的神经表征如何 参考点和损失厌恶——源自局部和远程突触的动态和模式。 具体来说，拟议的工作将描绘支持参考依赖性的丘脑皮质电路 计算，确定从值中算术减去参考点的电路机制 信号，并识别驱动损失厌恶个体差异的神经调节系统。 桥接细胞、电路和系统级神经机制的描述 经济判断，同时揭示支持算术计算的一般神经回路图案，包括 加法、减法和乘法。