The cerebro-cerebellar-basal-gangliar network for visuomotor learning

视觉运动学习的大脑-小脑-基底神经节网络

• 批准号: 9983219
• 负责人: Stefano Fusi
• 金额: $ 102.52万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983219
• 关键词: Affect; Agonist; Anatomy; Area; Association Learning; Basal Ganglia; Behavior; Brain; Cerebellar Cortex; Cerebellum; Cerebral cortex; Cognition; Cognitive; Computational Technique; Computer Analysis; Computing Methodologies; Cues; Data Set; Dentate nucleus; Dimensions; Electronic Medical Records and Genomics Network; Exhibits; Failure; Fractals; Grant; Hand; Human; Injections; Learning; Left; Light; Machine Learning; Methods; Modeling; Monkeys; Motor; Movement; Muscimol; Neurons; Outcome; Parietal; Pathway Analysis; Pattern; Performance; Pharmaceutical Preparations; Physiological; Prefrontal Cortex; Process; Property; Purkinje Cells; Rabies virus; Reaction Time; Reading; Reflex action; Reporting; Role; Short-Term Memory; Signal Transduction; Site; Source; Suggestion; Techniques; Testing; Time; To specify; Viral; Virus; Visual; Walking; Work; classical conditioning; cognitive function; cognitive process; experimental study; gamma-Aminobutyric Acid; human disease; kinematics; motor behavior; motor control; motor learning; nonhuman primate; novel; relating to nervous system; signal processing; success; visual learning; visual motor

ABSTRACT Visual learning is critical to the lives of human and non-human primates. Visuomotor association, the assignment of an arbitrary symbol to a particular movement (like a red light to a braking movement), is a well- studied form of visual learning. This proposal tests the hypothesis that the brain accomplishes visuomotor associative learning using an anatomically defined closed-loop network, including the prefrontal cortex, the basal ganglia, and the cerebellum. In our preliminary work we have developed a task that studies how monkeys learn to associate one of two novel fractal symbols with a right hand movement, and the other symbol with a left hand movement. Every experiment begins with the monkeys responding to two overtrained symbols that they have seen hundreds of thousands of times. At an arbitrary time we change the symbols to two fractal symbols that the monkey has never seen. It takes the monkey 40 to 70 trials to learn the new associations. In our preliminary results we have discovered that Purkinje cells in the midlateral cerebellar hemisphere track the monkeys’ learning as they as they figure out the required associations. The neurons signal the result of the prior decision. Half of the neurons respond more when the prior decision was correct; the others respond more when the prior decision was wrong. The difference between the activity of these two types of neurons provides a cognitive error signal that is maximal when the monkeys are performing at a chance level, and gradually becomes not different from zero as the monkeys learn the task. The neurons do not predict the result of the impending decision. Although the neurons change their activity dramatically at the symbol switch, the kinematics of the movements do not change at all. This proposal takes this discovery as the starting point for four aims: 1) to use viral transynaptic tract tracing to discover the cortical and basal ganglia regions that project to the cerebellar visuomotor association area. 2) to record from the four nodes of the network as anatomically defined (midlateral cerebellar hemisphere, dentate nucleus, basal ganglia, prefrontal cortex), simultaneously, using multiple single neuron recordings, to see if these areas also have information about the process of visuomotor association 3) to inactivate each node, to see how their inactivation affects the monkey’s ability to learn new associations, and whether the inactivation affects the activity of the neurons at the other nodes. 4) to develop computational methods to analyze the activity of neural activity recorded simultaneously in all four nodes of the network (Aim 2) in the midlateral cerebellar cortex with regard to parameters such as prior outcome and movement, hand, symbol, and the intensity and epoch of the prior cognitive error signal. We will use dimensional reduction techniques to answer questions like whether hand or symbol can be decoded from network activity. We will model how the cerebellum simple spike cognitive error signal might propagate through the network and be used to facilitate visuomotor association learning and the processing of signals in the cerebellum, basal ganglia and cerebral cortex

摘要 视觉学习对人类和非人类灵长类动物的生活至关重要。视觉协会 将任意符号分配给特定的运动（如红灯分配给制动运动），是一个很好的方法。 研究视觉学习的形式。这项提议验证了大脑完成视觉功能的假设 联想学习使用解剖学定义的闭环网络，包括前额叶皮层， 基底神经节和小脑。在我们的初步工作中，我们已经开发了一个任务，研究如何 猴子学会将两个新的分形符号中的一个与右手运动联系起来， 用左手移动。每个实验都是从猴子对两个过度训练的符号做出反应开始的 他们已经看过成百上千次了。在任意时刻，我们将符号变为两个分形 猴子从未见过的符号猴子需要40到70次尝试来学习新的关联。在 我们的初步结果是，我们发现小脑中外侧半球的浦肯野细胞追踪着大脑中的神经元。 猴子的学习是因为他们找出了所需的关联。神经元发出信号， 事先决定。当先前的决定是正确的时，一半的神经元反应更大;其他神经元反应更大。 因为之前的决定是错误的。这两种类型神经元的活动差异提供了 当猴子在偶然水平上表演时，认知错误信号最大， 在猴子学习任务的过程中，神经元不能预测 即将做出的决定虽然神经元在符号转换时会显著改变它们的活动， 运动的运动学根本不改变。该提案以这一发现为起点， 四个目的：1）使用病毒跨突触束追踪来发现皮质和基底神经节区域， 投射到小脑视觉联合区。2)从网络的四个节点记录为 解剖学定义（小脑半球中外侧、齿状核、基底神经节、前额皮质）， 同时，使用多个单神经元记录，看看这些区域是否也有关于神经元的信息。 3）观察每个节点，看看它们的失活如何影响猴子的视觉联想过程。 学习新关联的能力，以及失活是否会影响另一个神经元的活动。 结4)开发计算方法来分析同时记录的神经活动 在小脑中外侧皮层的所有四个网络节点（Aim 2）中，关于参数， 先前结果和运动、手、符号以及先前认知错误信号的强度和时期。 我们将使用降维技术来回答诸如手或符号是否可以 从网络活动中解码出来我们将模拟小脑简单尖峰认知错误信号如何可能 通过网络传播，并用于促进视觉联想学习和处理 小脑、基底神经节和大脑皮层中的信号