A new theory of population coding in the cerebellum

小脑群体编码的新理论

• 批准号: 10005617
• 负责人: REZA SHADMEHR
• 金额: $ 124.86万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005617
• 关键词: Action Potentials; Algorithms; Anatomy; Animals; Behavior; Behavioral; Brain; Callithrix; Cell Nucleus; Cells; Cerebellar Nuclei; Cerebellum; Classification; Code; Complex; Computational algorithm; Computer software; Consumption; Data; Detection; Development; Effectiveness; Electrodes; Electrophysiology (science); Event; Eye; Frequencies; Hand; Human; Individual; Inferior; Label; Learning; Lesion; Macaca; Manuals; Measurement; Measures; Methods; Modeling; Motion; Motor; Movement; Mus; Neuroanatomy; Neurons; Olives - dietary; Output; Pattern; Population; Population Theory; Probability; Problem Solving; Procedures; Process; Purkinje Cells; Saccades; Sensory; Smooth Pursuit; Statistical Methods; Structure; Techniques; Testing; Time; Visual; Work; Wrist; arm movement; automated algorithm; awake; base; density; experimental study; eye velocity; improved; novel strategies; open source; predictive modeling; preference; relating to nervous system; response; temporal measurement; theories; vector

A theory of population coding in the cerebellum In order to move accurately, the brain relies on internal models that predict the sensory consequences of motor commands. Evidence for this idea comes from human behavioral experiments [1-7] and animal lesion studies [8- 11], suggesting that the critical structure for forming internal models is the cerebellum. However, in the cerebellum it is often difficult to relate spiking activity of individual Purkinje cells (P-cells) with behavior: while for some tasks like smooth pursuit eye movements the activity of P-cells is a simple function of eye velocity [12], for most other movements such as saccades [13,14], wrist movements [15], or arm movements [16-19], it is difficult to associate activity of individual P-cells to behavior. Anatomy of the cerebellum suggests that P-cells organize in small groups, together projecting onto a single output nucleus neuron [20]. This anatomy implies that the fundamental computational unit of the cerebellum is not a single P-cell, but a population of P-cells that together converges onto a single output neuron. Thus, population coding in the cerebellum has a specific anatomical meaning: P-cells that converge onto a single output neuron together encode an aspect of behavior [21]. The critical problem is to identify the membership of each population in the living brain. Recently, we demonstrated a way to approach this problem [22]: P-cells that share the same complex spike tuning likely belong to the same population. However, identification of complex spike tuning is exceptionally difficult: complex spikes are rare events that have variable waveform durations. Indeed, the current approach relies on manual labeling of complex spikes, something that cannot be scaled to multi-contact probes. Here, three labs with expertise in marmosets, mice, and macaques have come together to develop algorithms that automate detection and attribution of complex spikes. These algorithms focus on the frequency-domain classification of spikes, and will be tested on high density multi-contact probes. Together, the algorithms and experimental procedures should significantly improve the ability of neuroscientists to tackle the question of population coding in the cerebellum, ultimately resulting in better understanding of how the cerebellum learns to precisely control movements of our body.

小脑中的人口编码理论 为了准确移动，大脑依赖于预测电动机的感觉后果的内部模型 命令。这个想法的证据来自人类行为实验[1-7]和动物病变研究[8-- 11]，表明形成内部模型的临界结构是小脑。但是，在小脑中 通常很难将单个Purkinje细胞（P细胞）的峰值活动与行为联系起来：而对于某些任务 像平滑的追捕眼动作一样，P细胞的活动是眼速度的简单功能[12] 扫视[13,14]，手腕动作[15]或手臂运动[16-19]等运动 单个P细胞对行为的活性。小脑解剖学表明，P细胞以小组组织为单位 共同投射到单个输出核神经元上[20]。这种解剖学意味着基本 小脑的计算单元不是单个P细胞，而是一群p单元，它们共同收敛到 单个输出神经元。因此，小脑中编码的人口编码具有特定的解剖含义：P细胞 收敛到单个输出神经元一起编码行为方面[21]。关键问题是 确定活大脑中每个人群的成员。最近，我们展示了一种方法 问题[22]：具有相同复杂尖峰调整的P细胞可能属于同一人群。然而， 复杂尖峰调整的识别非常困难：复杂的尖峰是具有可变的罕见事件 波形持续时间。确实，当前的方法依赖于复杂尖峰的手动标记，这是 不能缩放到多接触探针。在这里，有三个具有果酱，老鼠和猕猴专业知识的实验室 聚集在一起开发自动化检测和复杂尖峰归因的算法。这些算法 专注于尖峰的频域分类，并将在高密度多接触探针上进行测试。 一起，算法和实验程序应显着提高神经科学家的能力 解决小脑中人口编码的问题，最终使人们更好地了解 小脑学会精确控制我们身体的运动。