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

摘要 小脑对于学习和执行协调、适时的动作至关重要。小脑 大脑皮层似乎在学习运动计时方面有着特殊的作用。从60年代到70年代， 我们知道小脑微电路的结构，但大多数对行为过程中小脑功能的分析 主要研究浦肯野细胞在这里，我们建议在一个全新的水平上研究小脑皮层 通过询问完整的小脑微电路-苔藓纤维，颗粒细胞，高尔基体细胞，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。 我们通过识别所有元素，记录它们在运动过程中的电活动， 以及学习和重建再现生物数据的神经回路模型。我们将使用三个 建立学习系统，所有人都可以学习预测时间：眼睑反应的经典条件反射 （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 猴子（Monkeys）我们的建议有六个主要特点。首先，光遗传学（在小鼠中）将放电 不同的小脑中间神经元在运动和学习过程中的分子细胞类型。二是 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，并将分类神经元根据其假定的细胞类型的基础上， 在运动和运动学习期间记录非浦肯野细胞的许多参数。第三，多 接触电极将允许我们同时记录多个相邻的单个神经元， 计算尖峰时序交叉相关图（CCG）来识别连接的符号;我们还将寻找 CCG的变化提供了学习过程中可塑性特定部位的证据。第四，gCAMP成像 颗粒细胞层的研究将揭示小脑微回路输入的时间结构， 确定这些输入是否与运动学习相关地被修改。第五，模型神经网络， 真实的小脑结构将揭示一组模型参数， 输入到小脑在我们的三个运动系统中的所有神经元的测量反应， 小脑皮质第六，该模型将阐明突触和细胞可塑性的机制， 小脑微回路中的不同部位共同作用以引起运动学习。

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.