Canonical computations for motor learning by the cerebellar cortex micro-circuit

小脑皮层微电路运动学习的规范计算

• 批准号: 9814049
• 负责人: Nicolas Brunel
• 金额: $ 129.05万
• 依托单位: DUKE UNIVERSITY
• 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/9814049
• 关键词: Adult; Affect; Anatomy; Architecture; Behavior; Behavioral; Biological; Brush Cell; Cells; Cerebellar cortex structure; Cerebellum; Cluster Analysis; Collection; Computer Simulation; Data; Data Set; Development; Electrodes; Elements; Fiber; Forelimb; Goals; Golgi Apparatus; Health; Human; Image; Indium-111; Interneurons; Learning; Link; Machine Learning; Measures; Modeling; Modernization; Molecular; Monkeys; Motor; Motor Activity; Movement; Movement Disorders; Mus; Nerve Degeneration; Neural Network Simulation; Neurons; Pattern; Physiological; Play; Population; Preparation; Property; Purkinje Cells; Rest; Role; Signal Transduction; Site; Smooth Pursuit; Stroke; Structure; Structure of molecular layer of cerebellar cortex; Synapses; System; Testing; Time; Vision; Weight; Work; base; cell type; classical conditioning; commune; design; experimental study; eyeblink conditioning; eyelid conditioning; granule cell; improved; learned behavior; mossy fiber; motor control; motor disorder; motor learning; neural circuit; novel; operation; optogenetics; prevent; relating to nervous system; response; statistics; theories

Abstract The cerebellum is critical for learning and executing coordinated, well-timed movements. The cerebellar cortex seems to have a particular role in learning to time movements. Since the 1960's and 70's, we have known the architecture of the cerebellar microcircuit, but most analyses of cerebellar function during behavior have focused on Purkinje cells. Here, we propose to investigate the cerebellar cortex at an entirely new level by asking how the full cerebellar microcircuit – mossy fiber, granule cells, Golgi cells, molecular layer interneurons, and Purkinje cells – performs neural computations during motor behavior and motor learning. We strive to “crack” the circuit by identifying all elements, recording their electrical activity during movement and learning, and reconstructing a neural circuit model that reproduces the biological data. We will use three established learning systems that all can learn predictive timing: classical conditioning of the eyelid response (mice), predictive timing of forelimb movements (mice), and direction learning in smooth pursuit eye movements (monkeys). Our proposal has six key features. First, optogenetics (in mice) will link the discharge of different cerebellar interneurons during movement and learning to their molecular cell types. Second, a machine-learning clustering analysis (in mice and monkeys) will find analogies among the cell populations recorded in our three preparations and will classify neurons according to their putative cell types based on recordings of many parameters of non-Purkinje cells during movement and motor learning. Third, multi- contact electrodes will allow us to record simultaneously from multiple neighboring single neurons and compute spike-timing cross-correlograms (CCGs) to identify the sign of connections; we also will look for changes in CCGs that provide evidence of specific sites of plasticity during learning. Fourth, gCAMP imaging of the granule cell layer will reveal the temporal structure of inputs to the cerebellar microcircuit, and determine whether those inputs are modified in relation to motor learning. Fifth, a model neural network with realistic cerebellar architecture will reveal a single set of model parameters that will transform the measured inputs to the cerebellum in our three movement systems to the measured responses of all neurons in the cerebellar cortex. Sixth, the model will elucidate how mechanisms of synaptic and cellular plasticity at different sites in the cerebellar microcircuit work together to cause motor learning.

抽象的 小脑对于学习和执行协调、适时的运动至关重要。 自 20 世纪 60 年代和 70 年代以来，大脑皮层似乎在学习运动计时方面发挥着特殊作用。 已知小脑微电路的结构，但大多数对行为过程中小脑功能的分析 在这里，我们建议在一个全新的水平上研究小脑皮质。 通过询问完整的小脑微电路——苔藓纤维、颗粒细胞、高尔基细胞、分子层 中间神经元和浦肯野细胞 - 在运动行为和运动学习期间执行神经计算。 我们努力通过识别所有元件、记录其运动过程中的电活动来“破解”电路 学习和重建重现生物数据的神经回路模型我们将使用三个。 建立了所有人都可以学习预测时间的学习系统：眼睑反应的经典调节 （小鼠）、前肢运动的预测时间（小鼠）以及平滑追踪眼中的方向学习 我们的建议有六个主要特征，光遗传学（在小鼠中）将放电联系起来。 不同小脑中间神经元在运动和学习其分子细胞类型期间的变化。 机器学习聚类分析（在小鼠和猴子中）将发现细胞群之间的相似性 记录在我们的三个准备工作中，并将根据神经元的假定细胞类型对神经元进行分类 在运动和运动学习过程中记录非浦肯野细胞的许多参数。 接触电极将使我们能够同时记录多个相邻的单个神经元 计算尖峰时序互相关图（CCG）来识别我们还将寻找的连接符号； CCG 的变化提供了学习过程中特定可塑性部位的证据。第四，gCAMP 成像。 颗粒细胞层的研究将揭示小脑微电路输入的时间结构，并且 第五，模型神经网络是否与运动学习相关。 真实的小脑结构将揭示一组模型参数，这些参数将改变测量的结果 我们三个运动系统中小脑的输入对所有神经元的测量反应 第六，该模型将阐明突触和细胞可塑性的机制。 小脑微电路中的不同部位协同工作以引起运动学习。