CRCNS: Network mechanisms of the learning and encoding of timed motor responses

CRCNS：定时运动反应学习和编码的网络机制

• 批准号: 9242196
• 负责人: DEAN V BUONOMANO
• 金额: $ 29.46万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9242196
• 关键词: Address; Animals; Area; Axon; Behavior; Behavioral; Behavioral trial; Biological Neural Networks; Brain; Brain region; Code; Collection; Complex; Computational Technique; Computer Simulation; Computing Methodologies; Corpus striatum structure; Cues; Data; Data Analyses; Data Set; Electrophysiology (science); Engineering; Environment; Event; Evolution; Exhibits; Future; Goals; Head; Instruction; Learning; Linear Models; Link; Machine Learning; Medial; Mental disorders; Motor; Motor Activity; Mus; Neural Network Simulation; Neurons; Output; Pattern; Prefrontal Cortex; Process; Reaction Time; Reading; Recurrence; Rewards; Stimulus; Structure; Techniques; Time; Training; Uncertainty; Work; analytical method; awake; base; behavioral response; behavioral study; computer framework; conditioning; driving force; dynamic system; in vivo; innovation; millisecond; nervous system disorder; neural circuit; neural patterning; novel; optogenetics; pressure; relating to nervous system; response; spatiotemporal; temporal measurement

The brain is an inherently dynamic system, it evolved under strong selective pressures to allow animals to interact with the environment in real-time, and predict and prepare for future events. For these reasons, understanding neural dynamics, and how the brain tells and encodes time is fundamental to understanding brain function. The importance of neural dynamics and timing to brain function emphasizes the need for techniques that allow for the collection and analysis of massively parallel single neuron recordings across multiple structures in behaving animals. This project will combine novel electrophysiological, behavioral, analytical and computational methods to reverse engineer the neural circuits underlying learning and timing. The first aim is to combine large-scale neural recordings with computational approaches to determine how time is represented in the striatum and prefrontal cortex, two interacting brain areas that are closely implicated in temporal processing. We will specifically examine whether encoding of time relies on absolute, relative, or stimulus-specific coding mechanisms. Recordings will be carried out in awake, head-fixed mice trained on a classical trace reward conditioning task in which two cues predict reward with a different delay period. When animals learn the cue-reward association, they engage in robust anticipatory licking that precedes the reward presentation; moreover, the timing of this behavior is dependent on the cue-reward delay time. The second aim is to combine electrophysiology and optogenetics to determine if temporal coding in the striatum and prefrontal cortex is perturbed by transiently disrupting network activity. The hypothesis is that if dynamics of the timing circuits are perturbed then the ensuing activity patterns will be irreversibly altered, thus reducing the accuracy or precision of timed behavioral responses. The third aim is to develop a novel computational framework based on recurrent neural networks models that can predict "future" patterns of neural ensemble activity based on "present" patterns. The ultimate goal of this work is to integrate highly innovative electrophysiological and computational methods for reverse engineering brain circuit function at the level of networks of hundreds of neurons in the striatum and prefrontal cortex. RELEVANCE (See instructions): Learning to produce appropriately timed actions is fundamental to many aspects of behavior, and disruption of the brain circuits underlying this process is implicated in many neurological and psychiatric disorders. This project will develop an integrated approach to studying the mechanisms of timed motor behavior by combining large-scale neural recordings from multiple brain areas and computational modeling of neural networks.

大脑本质上是一个动态系统，它在强烈的选择压力下进化，使动物能够 与环境实时交互，预测未来事件并为之做好准备。由于这些原因， 理解神经动力学，以及大脑如何讲述和编码时间是理解的基础 大脑功能。神经动力学和时序对大脑功能的重要性强调了 允许收集和分析大规模并行单个神经元记录的技术 行为动物的多重结构。该项目将结合新颖的电生理学、行为学、 对学习和计时背后的神经回路进行逆向工程的分析和计算方法。 第一个目标是将大规模神经记录与计算方法相结合，以确定如何 时间由纹状体和前额叶皮层代表，这两个相互作用的大脑区域紧密相连 涉及时间处理。我们将专门检查时间编码是否依赖于绝对， 相对的或刺激特异性的编码机制。录音将在清醒、头部固定的小鼠中进行 接受经典跟踪奖励调节任务的训练，其中两个线索以不同的延迟预测奖励 时期。当动物学会了提示-奖励关联时，它们会进行强烈的预期舔舐， 在颁发奖励之前；此外，这种行为的时间取决于提示奖励 延迟时间。第二个目标是结合电生理学和光遗传学来确定时间是否 纹状体和前额叶皮层中的编码受到短暂中断的网络活动的干扰。这 假设是，如果定时电路的动态受到扰动，那么随后的活动模式将是 不可逆转地改变，从而降低定时行为反应的准确性或精确度。第三个目标是 开发一种基于循环神经网络模型的新型计算框架，该模型可以预测 基于“当前”模式的神经集合活动的“未来”模式。这项工作的最终目标是 整合高度创新的电生理学和计算方法进行大脑逆向工程 纹状体和前额叶皮层数百个神经元网络水平的电路功能。 相关性（参见说明）： 学习在适当的时机采取行动对于行为和破坏的许多方面都是基础 这一过程背后的大脑回路与许多神经和精神疾病有关。这 项目将开发一种综合方法来研究定时运动行为的机制 结合来自多个大脑区域的大规模神经记录和神经计算模型 网络。