CRCNS: Refining computational models of motor sequence learning and execution

这项提议旨在通过实验和计算研究大脑如何学习和生成 复杂的动作序列。由于行为是由大脑许多区域的动态相互作用产生的， 允许研究分布式电路的计算模型是重要的。这样的模型可以统一 多个实验和大脑区域的结果，以提供对神经电路计算的见解， 重要的是，产生实验预测。为了给这些模型提供实验约束， 需要一种运动学习范式，在这种范式中，受试者可以得到可靠的训练，他们的行为可以量化， 测量和操纵相关回路的神经活动。啮齿动物的运动序列任务 并在Olveczky实验室研究符合这些标准，因此可以用来约束，完善， 在不同的计算模型之间进行仲裁，并作为他们的实验测试平台。 预测。由Escola实验室领导的计算工作将从电路模型的探索开始 与现有数据一致。对这些模型的进一步分析将产生相互竞争的预测， 在它们之间进行仲裁的实验的想法。此次合作的目标是实现电路级 描述了复杂的运动序列是如何学习和产生的形式， 合理的计算模型，可以完善和更新新的实验结果到达。 来自纹状体（一个主要的运动系统结构）神经元的记录揭示了“序列”的存在 “细胞”--神经元是稀疏活跃的，并且精确地与运动输出时间锁定。另夕h Olveczky的实验室表明，运动皮层对于学习任务至关重要，但如果没有 削弱任务执行。这些结果提出了一些重要的问题：1）纹状体中是否存在序列细胞活动 驱动学习到的运动序列，如果是这样，这种动力学是独立于运动皮层产生的吗？ 2)其他与运动相关的回路如何影响纹状体动力学并最终影响行为？3)是如何 控制复杂运动序列的回路会学习，也就是说，这些回路中的可塑性位点在哪里 什么样的学习规则支配着它们的适应性重组？一种计算建模框架， 受严格和定量地解决这些问题的实验结果的约束， 推进我们对运动系统如何产生习得运动序列的理解。这 理解将提供一个新的框架，在其中考虑运动的发病机制 <ii>&lt;or&lt;iAro：; &gt;&lt;11r.h“”P“rkin&gt;&lt;nn'&gt;&lt;“nrl H11ntinntn'&gt;< rli>&lt;A”D 相关性（见说明）： 运动障碍如帕金森氏症和亨廷顿氏症构成了主要的疾病负担。贡献 理解大脑如何控制运动并因此在疾病中可能紊乱的困难， 许多大脑区域相互作用产生运动的事实。在这项提案中，动物实验将被 再加上大脑这些部分的计算机建模，以促进我们理解它们的能力。 在疾病发展中的相互作用和作用。n

This proposal aims to experimentally and computationally study how the brain learns and generates complex sequences of actions. Since behaviors emerge from the dynamic interplay of many brain areas, computational models that permit the study of distributed circuits are important. Such models can unify results across multiple experiments and brain areas to provide insights into neural circuit computations and, importantly, to generate experimental predictions. To provide experimental constraints for such models, a motor learning paradigm is needed in which subjects can be reliably trained, their behavior quantified, and neural activity in relevant circuits measured and manipulated. The rodent motor sequence task developed and studied in the Olveczky lab conforms to these criteria, and hence can serve to constrain, refine, and arbitrate between different computational models, and serve as an experimental test-bed for their predictions. The computational effort, led by the Escola lab, will start with an exploration of circuit models consistent with available data. Further analysis of these models will generate competing predictions and ideas for experiments that arbitrate between them. The goal of this collaboration is to arrive at a circuit-level description of how complex motor sequences are learned and produced in the form of a biologically plausible computational model that can be refined and updated as new experimental results arrive. Recordings from neurons in the striatum (a major motor system structure) reveal the existence of "sequence cells"-- neurons that are sparsely active and precisely time-locked to the motor output. Additionally, the Olveczky lab showed that motor cortex is essential for learning the task, but can be lesioned without impairing task execution. These results raise important questions: 1) Is sequence cell activity in the striatum driving the learned motor sequences, and if so, is this dynamics generated independently of motor cortex? 2) How do other motor-related circuits contribute to striatal dynamics and ultimately behavior? 3) How do the circuits controlling complex motor sequences learn, i.e. where are the sites of plasticity within these circuits and what learning rules govern their adaptive reorganization? A computational modeling framework, constrained by experimental results, which rigorously and quantitatively addresses these questions, will advance our understanding of how the motor system produces learned motor sequences. This understanding will provide a new framework within which to consider the pathogenesis of movement <ii><or<iAro:; ><11r.h "" P"rkin><nn'>< "nrl H11ntinntnn'>< rli><A"""'" D RELEVANCE (See Instructions): Movement disorders such as Parkinson's and Huntington's constitute a major disease burden. Contributing to the difficulty in understanding how the brain controls movement and thus may be disordered in disease is the fact that many brain areas interact to generate movements. In this proposal, animal experiments will be coupled with computer modeling of these parts of the brain to facilitate our ability understand their interactions and role in the develooment of disease. n