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理解连接模式和动力学是如何 局部和远程突触输入能够在单个神经元中进行与行为相关的计算。一个 所有哺乳动物进行的基本计算都是决定不同结果的价值，包括 它们的价值，或者它们是否被视为一种收益或损失。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和使用的量化框架 这些价值估计用来做出决定。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被神经元计算和表示的机制 在大脑里。 经济选择行为的一个特征是人们表现出“参照依赖”，其中他们 以相对于内部参照点的得失来评估结果。一种相关的现象，称为 “损失厌恶”指的是观察到，大多数人对损失比同等价值更敏感。 收获。这项提议将结合最先进的病毒和转基因方法在体内进行电路分离 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 定量行为建模，以确定可量化认知变量的神经表示- 参考点和损失厌恶-源自局部和远程突触的动力学和模式。 具体地说，拟议的工作将描绘支持参考依赖的丘脑皮质回路 计算，确定参考点从值中算术减去的电路机制 信号，并确定神经调节系统驱动个体在损失厌恶中的变异性。结果将会是 桥接细胞、电路和系统级别的神经机制的描述 经济判断，同时揭示支持算术计算的一般神经电路主题，包括 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.