Neural circuit mechanisms of arithmetic for economic decision-making

项目摘要 神经科学的一个核心挑战是了解神经元的连接模式和动力学是如何变化的。 局部和远程突触输入使得能够在各个神经元中进行行为相关的计算。一 所有哺乳动物进行的基本计算是确定不同结果的价值，包括 他们的价值，或他们是否被视为一个增益或损失。行为经济学提供了一个有用的 描述人类和动物如何主观地为结果赋值的定量框架， 这些价值评估来做决定。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 在大脑里。 经济选择行为的一个标志是人们表现出“参照依赖”， 评估结果，作为相对于内部参考点的收益或损失。一个相关的现象，称为 “损失厌恶”指的是大多数人对损失比对等价物更敏感的观察。 收益.这项建议将结合联合收割机国家的最先进的病毒和转基因方法的电路解剖，在体内 配对记录的长程突触连接的神经元，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， 定量行为建模，以确定可量化的认知变量的神经表征- 参考点和损失规避-从局部和远程突触的动力学和模式导出。 具体来说，拟议的工作将描绘丘脑皮层回路支持参考依赖 计算，确定从值中算术减去参考点的电路机制 信号，并确定神经调节系统驱动的个体差异性损失厌恶。结果将 桥接细胞、电路和系统层面的神经机制描述， 经济判断，同时揭示了支持算术计算的一般神经回路图案，包括 求和、减法和乘法。

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.