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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，大脑有专门的神经调节机制