Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learning

解码学习过程中中皮质纹状体回路奖励预测的动态表示

基本信息

批准号：
10153745
负责人：
Yael Niv
金额：
$ 28.35万
依托单位：
PRINCETON 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：
2020-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10153745
关键词：
Adaptive Behaviors Affect Animals Area Back Basal Ganglia Bayesian Modeling Behavior Behavioral Behavioral Mechanisms Behavioral Model Brain Cocaine Collaborations Computer Models Data Data Set Disease Dopamine Event Exposure to Functional disorder Future Impairment Impulsivity International Joints Learning Lesion Link Machine Learning Neuromodulator Neurons Odors Outcome Pattern Poverty Process Rattus Reporting Research Rewards Role Shapes Signal Transduction Source Structure System Techniques Testing Theoretical model Time Update Ventral Striatum Ventral Tegmental Area Work addiction base brain circuitry cocaine exposure cognitive function computer framework cost dopamine system drug of abuse experience frontal lobe innovation machine learning method neural circuit neural correlate neuromechanism neurophysiology neuroregulation novel programs relating to nervous system reward circuitry synergism theories tool

项目摘要

Reward learning is a fundamental cognitive function, and the brain has a dedicated neuromodulatory system – based on dopamine – that supports this process. Changes to the dopamine system that are triggered by exposure to drugs of abuse are thought to underlie the behavioral changes observed in addiction. Here we propose to use a treasure trove of previously recorded neural data from throughout the mesocorticostriatal circuitry that supports reward learning, to elucidate the computational role of each component of the circuit, their interactions, and how these components are affected by cocaine. Our brains constantly generate predictions about what rewards might be available, and compare these predictions to actual outcomes. The neuromodulator dopamine is thought to report these ‘prediction error’ signals, the result of the ongoing comparison between expected and obtained rewards, that are key to updating predictions so they are more accurate in the future. Predicting the timing of rewards, and not just their identity or value, is an important component of this process, but it remains a mystery how the brain forms and uses predictions about time in reward learning. Based on a novel theoretical model we recently developed, we will test the computational role of three key brain areas that comprise the brain circuit critical for reward learning, using a state-of-the- art methods from machine learning to jointly decode the learning processes that drive neural activity from multiple brain areas along with behavior as rats perform a reward learning task. In Aim 1, we hypothesize that neural activity in the orbitofrontal cortex is uniquely important for representing high level ‘task states’ and will test for patterns in OFC neural activity that follow the hidden structure of the task. In Aim 2, we will decode the representation of reward predictions about the amount and timing of rewards, and test whether they are separable in VS neural activity. In Aim 3, we will test how activity in VS and OFC controls dopamine activity, and in particular how each input component enables prediction errors to be temporally precise. In Aim 4, we will test how exposure to cocaine changes neural activity that represents reward predictions in the VS, and the impact of this disruption on dopamine prediction errors in the VTA. This innovative multi-level study will leverage numerous existing neural and behavioral data from rats performing a well-validated reward-learning task, to reveal the computational, neural and behavioral mechanisms of the reward prediction and learning circuitry in the brain, and the source of their disruption in addiction.

奖励学习是一种基本的认知功能，大脑具有专用的神经调节性基于多巴胺的系统支持此过程。更改多巴胺系统被认为是通过暴露于滥用药物而触发的，这是在观察到的行为变化的基础瘾。在这里，我们建议使用先前记录的神经数据的宝库在整个中皮层纹状体电路中，支持奖励学习，以阐明电路的每个组件的计算作用，它们的相互作用以及这些组件如何受可卡因的影响。我们的大脑不断产生有关可能可用的奖励的预测，并比较这些对实际结果的预测。人们认为神经调节剂多巴胺报告了 “预测误差”信号，预期与获得的结果持续比较的结果奖励，这是更新预测的关键，因此它们将来更加准确。预测奖励的时机，而不仅仅是其身份或价值，是此过程的重要组成部分，但它是仍然是一个神秘的大脑如何形成和使用有关奖励学习时间的预测。基于我们最近开发的新型理论模型，我们将测试使用最先进从机器学习到共同解码驱动神经活动的学习过程从多个大脑区域和行为以及老鼠执行奖励学习任务。在AIM 1中，我们假设眶额皮质中的中性活性对于代表高 “任务状态”等级，并将测试OFC神经活动中的模式，以遵循任务。在AIM 2中，我们将解码有关数量和时机的奖励预测的表示奖励，并测试它们是否在神经活动中是分开的。在AIM 3中，我们将测试如何 VS和OFC中的活性控制多巴胺活性，尤其是每个输入组件使预测错误暂时精确。在AIM 4中，我们将测试如何接触可卡因改变代表VS中奖励预测的神经活动以及这种破坏的影响在VTA中的多巴胺预测错误上。这项创新的多层次研究将利用大鼠的众多现有神经和行为数据执行精心验证的奖励学习任务，以揭示计算，神经和行为奖励预测和学习电路的机制及其来源成瘾中的破坏。