Control Theoretic Model of the Cerebellum

小脑的控制理论模型

• 批准号: RGPIN-2019-05728
• 负责人: Broucke, Mireille
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751569
• 关键词: Control; Theoretic; Model; Cerebellum

My research concerns the development of a control theoretic model of the cerebellum. A complete understanding of the cerebellum is one of the great open problems of neuroscience today, not very different from Kepler's problem on the motion of the planets, in its day. The cerebellum has remarkable features. It contains about half the neurons of the brain (more than the cerebral cortex), it has a purely feedforward neural architecture (in contrast with the recurrent architecture of the cerebral cortex), it operates on a timescale of milliseconds, 1-2 orders of magnitude faster than the cerebral cortex. Most importantly, despite the many regulatory functions it performs and independently of the particular function it is regulating (e.g. motor control, locomotion, eye movement, speech timing, emotion regulation), the computations are identical. What are those computations? Since the 1960's, control theory and robotics have offered plausible explanations. Yet no complete model has been found, to date. I initiated research on modeling the cerebellum during my sabbatical in 2017. I began with an experiment in cognitive psychology on adaptation to visuomotor rotations. A human subject is presented with a task to make a fast reach with the hand to a target disk on a horizontal screen. When the cursor angle is rotated from the hand angle by an amount unknown to the subject, an implicit (subconscious) process of adaptation takes place. This process is believed to reside in the cerebellum. We developed a model that predicts the dynamic phenomena observed in the experiments. The cerebellum is also involved in eye movement. Behaviours of the oculomotor system include the vestibuloocular reflex, smooth pursuit, saccades, and the optokinetic reflex. The computations (in the cerebellum) are the same for both the visuomotor experiment and the oculomotor system. We developed a second generation model that both recovers the behaviours of the oculomotor system and reveals the computations of the cerebellum. Rather than deriving a mathematical model based on analysis of experimental data, I approach the cerebellum modeling problem as one of synthesis: to synthesize a control law using tools from control theory. An advantage of applying control theory in this way is that, like Newton's laws that tell us about immutable limits on the motion of bodies in a gravitational field, so too control theory explains the immutable limits of a control system to achieve tracking in the presence of unknown disturbances. In essence, control theory narrows the search for a plausible mathematical model. The first phase of our research to develop a control-theoretic model of the cerebellum is well underway. The second phase is to relate the model to the neurophysiology of the cerebellum. We hope our work can contribute to basic science as well as to understanding neurological disorders such as ataxia, Huntington's disease, and Parkinson's disease.

我的研究涉及小脑控制理论模型的发展。对小脑的全面了解是当今神经科学的重大开放问题之一，与当时开普勒关于行星运动的问题没有太大区别。小脑具有显著的特征。它包含大约一半的大脑神经元（比大脑皮层多），它有一个纯粹的前馈神经结构（与大脑皮层的循环结构相反），它在毫秒的时间尺度上运作，比大脑皮层快1-2个数量级。最重要的是，尽管它执行许多调节功能，并且独立于它所调节的特定功能（例如运动控制，运动，眼动，言语计时，情绪调节），但计算是相同的。这些计算是什么？自20世纪60年代以来，控制理论和机器人技术提供了合理的解释。然而，到目前为止，还没有找到一个完整的模型。我在2017年休假期间开始了小脑建模的研究。我从认知心理学中关于视觉运动旋转适应的实验开始。人类受试者被要求用手快速到达水平屏幕上的目标磁盘。当光标的角度从手的角度旋转一个未知的量时，一个内隐的（潜意识的）适应过程就会发生。这个过程被认为存在于小脑中。我们建立了一个模型来预测实验中观察到的动态现象。小脑也参与眼球运动。动眼肌系统的行为包括前庭反射、平滑追踪、扫视和光动反射。在小脑中，视觉运动实验和眼动系统的计算是相同的。我们开发了第二代模型，既恢复了动眼肌系统的行为，又揭示了小脑的计算。我不是在分析实验数据的基础上推导出一个数学模型，而是将小脑建模问题作为一个综合问题来处理：使用控制理论的工具来综合一个控制律。以这种方式应用控制理论的一个优点是，就像牛顿定律告诉我们物体在引力场中运动的不变极限一样，控制理论也解释了控制系统在存在未知干扰时实现跟踪的不变极限。从本质上讲，控制理论缩小了对合理数学模型的搜索范围。我们开发小脑控制理论模型的第一阶段研究正在顺利进行。第二阶段是将模型与小脑的神经生理学联系起来。我们希望我们的工作可以为基础科学做出贡献，同时也有助于理解神经系统疾病，如共济失调、亨廷顿氏病和帕金森病。