A new theory of population coding in the cerebellum

小脑群体编码的新理论

基本信息

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
项目类别：
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

项目摘要

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.

小脑群体编码理论为了准确地运动，大脑依赖于预测运动的感觉后果的内部模型命令。这一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关键结构是小脑。然而，在小脑通常很难将单个浦肯野细胞（P 细胞）的尖峰活动与行为联系起来：而对于某些任务就像平滑追踪眼球运动一样，P 细胞的活动是眼球速度的简单函数 [12]，对于大多数其他眼跳 [13,14]、手腕运动 [15] 或手臂运动 [16-19] 等运动，很难将其关联起来个体 P 细胞的活动对行为的影响。小脑的解剖表明 P 细胞以小组形式组织，一起投射到单个输出核神经元上[20]。这种解剖学意味着基本的小脑的计算单位不是单个 P 细胞，而是聚集在一起的一群 P 细胞单个输出神经元。因此，小脑中的群体编码具有特定的解剖学意义：P 细胞汇聚到单个输出神经元上，共同编码行为的一个方面[21]。关键问题是识别活体大脑中每个群体的成员资格。最近，我们演示了一种解决此问题的方法问题 [22]：共享相同复杂尖峰调节的 P 细胞可能属于同一群体。然而，复杂尖峰调节的识别异常困难：复杂尖峰是具有可变性的罕见事件波形持续时间。事实上，当前的方法依赖于复杂尖峰的手动标记，这无法扩展到多接触探头。在这里，三个拥有狨猴、小鼠和猕猴专业知识的实验室共同开发自动检测和归因复杂尖峰的算法。这些算法重点关注尖峰的频域分类，并将在高密度多接触探针上进行测试。算法和实验程序一起应该显着提高神经科学家的能力解决小脑中的群体编码问题，最终更好地理解小脑如何小脑学习精确控制我们身体的运动。