权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Frontocortical Signaling Signatures in Flexible Reinforcement Learning

灵活强化学习中的额皮质信号特征

基本信息

批准号：
10304186
负责人：
HUGH T BLAIR
金额：
$ 23.4万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
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：
2020-11-17 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10304186
关键词：
Animal Model Animals Anterior Area Behavior Behavioral Calcium Chronic Code Coupling Cues Custom Development Discrimination Discrimination Learning Electrodes Electrophysiology (science)Environment Exploratory/Developmental Grant Failure Feedback Image Impairment Lead Learning Light Mediating Modeling Monitor Outcome Palate Pattern Performance Phase Photons Predictive Value Probability Process Psychological reinforcement Psychopathology Rattus Reversal Learning Rewards Role Sensory Signal Transduction Slice Stimulus Testing Time Uncertainty Update Work adaptive learning base behavior change cingulate cortex design designer receptors exclusively activated by designer drugs expectation experience experimental study flexibility improved in vivo neuropsychiatric disorder neuropsychiatry neuroregulation new technology novel novel strategies relating to nervous system time use

项目摘要

Various neuropsychiatric conditions lead to failures in generating accurate models of the reward environment or inabilities in using those models to guide flexible behavior, very often manifesting as impaired reversal learning. The anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC) are frontocortical regions important for flexible reinforcement learning, and have been theorized to work in a hierarchy of parallel processes for reward- based choice. In OFC, there is priority encoding of lower-level attributes like reward-predictive value of sensory cues, the palatability of specific rewards, and the current stimulus-reward mappings relevant to behavior. In ACC, these variables are thought to be multiplexed for higher-level computations of reward prediction error (RPE) and confidence/uncertainty of predictions, which are used to monitor performance and update behavioral strategies when necessary (particularly overall trial strategy following positive feedback, i.e., WinStay). These computations may depend upon propagation of spikes from OFC to ACC. However, it remains poorly understood how flexible reward learning is mediated by interactions between OFC and ACC. Here we will investigate this question using a robust animal model of adaptive learning under uncertainty: stimulus-based probabilistic reversal learning (PRL). In freely behaving rats, we will use a combination of in vivo 1-photon calcium imaging and electrophysiology, chemogenetics, and closed-loop neural control of reward delivery to examine how OFC and ACC regulate PRL. Using new technology that we have recently developed for online decoding of calcium activity we will use a novel strategy of regulating reward delivery based upon neural activity in ACC and OFC to test whether flexible reward learning depends upon accurate neural representations in these frontocortical areas. To date, we have: demonstrated effective DREADDs manipulation in vivo and in transduced cortical slices; designed and tested custom electrode arrays to perform chronic in vivo electrophysiological recordings in these areas simultaneously; and imaged ensemble activity time-locked to behavior, which has proven stable over multiple sessions, ideal to study learning. Leveraging these technical advances and using this capacity as a platform, we propose to identify the precise cortico-cortical mechanisms of encoding variables in flexible reinforcement learning across two Aims. Collectively, these experiments will: 1) shed new light on the signaling signatures of cortical regions and their respective roles in flexible reinforcement learning, 2) accelerate groundbreaking experiments as they would be performed in closed-loop: control of reversal learning in real-time using decoded neural expectation, and 3) these signals would eventually be compared in animal models of psychopathology because of their known failures in reversal learning. These novel and unconventional approaches make the R21 mechanism ideal for the proposed work.

各种神经精神疾病导致无法生成准确的奖励环境模型