Computational Model of Motor Sequence Learning

项目4运动序列学习的计算建模 这个项目的总体目标是开发一个生物学上详细的学习计算模型。 离散顺序生产(DSP)任务，该任务将用于本PPG的所有项目。这个模型， 这将包括运动前皮质和运动皮质的多个区域，以及将整合的基底节 Ashby使用Houk的运动系统分布式处理模块模型在类别学习方面的工作。这个 模型中使用的最终神经体系结构将基于其他PPG项目的经验结果。即便如此， 基本架构将包括广泛的皮质-皮质投影，每个皮质区域将被连接 通过闭合环路到达纹状体。大脑皮质突触可塑性将由(2-因子)Hebbian介导 学习，而皮质-纹状体突触的可塑性将通过(3因素)强化学习来调节 (RL)。由于这种差异，一个基本的假设是，大脑皮层的顺序学习需要初始 来自基底神经节的帮助。其关键思想是，输入到皮质的基底节起着关键的支架作用。 来指导皮质可塑性。这种方法在解释类别学习方面非常成功，并将 在目前的提案中，将其扩展到顺序学习。目标1是构建模型并对其进行测试 几个定性基准。这些措施包括验证模型是否可以学习做出预测性反应 (即，在呈现下一个视觉提示之前响应)，并且它最终可以在没有来自 基底节。Aim 2将针对一些经典的序列学习数据来测试该模型。最终目标是 根据在其他PPG项目中收集的数据来测试完成目标1和2所产生的模型。在……里面 具体地说，目标是让相同的基本模型同时考虑收集的单个单元记录数据 由Strick和Turner在项目1和3中，为Strick的 黄素蛋白成像数据，用于Grafton在项目2中收集的fMRI和TMS数据，以及行为数据 在所有这些项目中收集的。此外，建模的一个关键目标将是考虑到所有 这些数据类型，以及每个项目中计划的广泛培训。 相关性(请参阅说明)： 拟议的工作是理解实践导致的机制的核心问题 面对衰老、神经再生、中风或脑损伤时人类运动系统的重组。 了解这些机制对旨在保护功能的疗法的设计有影响， 开发补偿器运动，并最终开发新的电机容量。

Project 4 Computational Modeling of Motor Sequence Learning The overall goal of this project is to develop a biologically detailed computational model of learning in the discrete sequence production (DSP) task, which is the task that will be used in all projects of this PPG. The model, which will include multiple regions in premotor and motor cortices, as well as the basal ganglia, will integrate Ashby's work on category learning with Houk's distributed processing module model of the motor system. The final neural architecture used in the model will be based on empirical results from the other PPG projects. Even so, the basic architecture will include extensive cortical-cortical projections, and each cortical region will be connected to the striatum via closed loop pathways. Synaptic plasticity in cortex will be mediated by (2-factor) Hebbian learning, whereas plasticity at cortical-striatal synapses will be mediated by (3-factor) reinforcement learning (RL). Because of this difference, a fundamental hypothesis is that sequence learning in cortex requires initial assistance from the basal ganglia. The key idea is that the basal ganglia input to cortex serves as a critical scaffold for directing cortical plasticity. This approach has been highly successful in explaining category learning and will be extended to sequence learning in the current proposal. Aim 1 is to construct the model and test it against several qualitative benchmarks. These include verifying that the model can learn to make predictive responses (i.e., respond before the next visual cue is presented), and that it can eventually respond without assistance from the basal ganglia. Aim 2 will test the model against some classic published sequence-learning data. The final goal is to test the model that results from completing Aims 1 and 2 against data collected in the other PPG projects. In particular, the goal is for the same basic model to account simultaneously for single-unit recording data collected by Strick and Turner in Projects 1 and 3, for data from their muscimol inactivation experiments, for Strick's flavoprotein imaging data, for fMRI and TMS data collected by Grafton in Project 2, and also for behavioral data collected in all of these projects. Furthermore, a critical goal of the modeling will be to account for changes in all of these data types with the extensive training that is planned in each project. RELEVANCE (See instructions): The proposed work is central to the problem of understanding the mechansims where practice leads to to reorganization of the human motor system in the face of aging, neurodeneration, stroke or brain injury. Understanding these mechansims has an impact on the design of therapies directed at preserving function, developing compensator movements and ultimately, developing novel motor capacity.