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是如何学习和生成 复杂的动作序列。由于行为产生于许多大脑区域的动态相互作用， 允许研究分布式电路的计算模型很重要。这样的模式可以统一 多个实验和大脑区域的结果，以提供对神经电路计算的洞察， 重要的是，为了产生实验预测。为了为这类模型提供实验约束，一个 运动学习范式是需要的，在这种范式中，受试者可以得到可靠的训练，他们的行为被量化，并且 测量和操纵相关电路中的神经活动。开发了啮齿动物运动序列任务 在Olvezky实验室中进行的研究符合这些标准，因此可以用来约束、提炼和 在不同的计算模型之间进行仲裁，并作为其 预测。由Escola实验室领导的计算工作将从探索电路模型开始 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 在他们之间进行仲裁的实验的想法。这种合作的目标是在电路层面上实现 描述复杂的运动序列如何以生物的形式学习和产生 可信的计算模型，可以随着新的实验结果的到来而改进和更新。 纹状体(一种主要的运动系统结构)中神经元的记录揭示了这种序列的存在 细胞“--神经元是稀疏活跃的，精确地与运动输出相关联。 奥尔维茨基的实验室表明，运动皮质对于学习这项任务是必不可少的，但在没有大脑的情况下，运动皮质也可以受损 影响任务执行。这些结果提出了重要的问题：1)纹状体中的序列细胞活动 驱动学习到的运动序列，如果是这样的话，这种动力学是独立于运动皮质产生的吗？ 2)其他运动相关回路如何对纹状体动力学和最终行为做出贡献？3)如何 控制复杂运动序列的电路学习，即这些电路中的可塑性位置在哪里 什么学习规则管理着他们的适应性重组？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制约，将 加深我们对运动系统如何产生习得的运动序列的理解。这 理解将提供一个新的框架，在其中考虑运动的发病机制 帕金森氏症和亨廷顿氏症等疾病。

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 disorders such as Parkinson's and Huntington's diseases.