CRCNS: Refining computational models of motor sequence learning and execution

该提案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。由于行为是由许多大脑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非常重要。此类模型可以统一 跨多个实验和大脑区域的结果，以提供对神经回路计算的见解， 重要的是，产生实验预测。为了为此类模型提供实验约束， 需要运动学习范式，其中受试者可以得到可靠的训练，他们的行为被量化，并且 测量和操纵相关回路中的神经活动。啮齿动物运动序列任务开发 Olveczky 实验室研究的结果符合这些标准，因此可以用于约束、细化和 在不同的计算模型之间进行仲裁，并作为其实验测试平台 预测。由 Escola 实验室领导的计算工作将从对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和 在它们之间进行仲裁的实验想法。此次合作的目标是实现电路级 描述复杂的运动序列如何以生物的形式学习和产生 合理的计算模型可以随着新的实验结果的出现而得到完善和更新。 纹状体（主要运动系统结构）神经元的记录揭示了“序列”的存在 细胞”——活动稀疏且与运动输出精确时间锁定的神经元。此外， Olveczky 实验室表明，运动皮层对于学习任务至关重要，但也可能在不发生损伤的情况下受到损伤。 损害任务执行。这些结果提出了重要的问题：1）对纹状体中的细胞活动进行测序吗？ 驱动学习到的运动序列，如果是这样，这种动力学是否独立于运动皮层产生？ 2）其他运动相关回路如何影响纹状体动力学和最终行为？ 3）如何做 控制复杂运动序列的电路学习，即这些电路中的可塑性位点在哪里 哪些学习规则控制着它们的适应性重组？计算建模框架， 受到实验结果的限制，严格且定量地解决这些问题，将 增进我们对运动系统如何产生学习运动序列的理解。这个 理解将提供一个新的框架来考虑运动的发病机制 <ii><或<iAro:; ><11r.h "" P"rkin><nn'>< "nrl H11ntinntnn'>< rli><A"""'" D 相关性（参见说明）： 帕金森氏症和亨廷顿氏症等运动障碍构成了主要的疾病负担。贡献 难以理解大脑如何控制运动，因此在疾病中可能会出现紊乱 事实上，许多大脑区域相互作用以产生运动。在本提案中，将进行动物实验 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 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