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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

基本信息

批准号：
10155611
负责人：
Nicolas Brunel
金额：
$ 127.53万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10155611
关键词：
Adult Affect Anatomy Architecture Behavior Behavioral Biological Brush Cell Cells Cerebellar Cortex Cerebellum Cluster Analysis Collection Computer Models Data Data Set Development Electrodes Elements Fiber Forelimb Goals Golgi Apparatus Health Human Image Interneurons Learning Link Machine Learning Measures Modeling Modernization Molecular Monkeys 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 behavior motor control motor disorder motor learning neural circuit novel operation optogenetics prevent relating to nervous system response statistics theories

项目摘要

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.

摘要