Unraveling constraints on motor cortical activity exploration and shaping during structural skill learning using large-scale 2-photon imaging and holographic optogenetic stimulation

项目摘要 在学习新技能时，使用以前学过的技能的经验可以通过约束来促进更快的学习 行为探索和塑造，这一概念被称为“结构性学习”。运动皮质扮演着一种 在学习新技能方面的重要作用，其最初可变的活动是在 学习。然而，先前的经验如何调节大脑皮层网络活动的探索和形成 促进新技能的学习还没有被很好地理解。当大脑学会控制脑机接口时 (BMI)，在BMI-幼稚受试者中，皮质网络活动的探索和塑造是广泛的(高维度) 在BMI经历过的受试者中具有约束性(低维)，这暗示了以下假设。 假设：以前的经验通过限制运动方式来促进新的相关技能的更快学习 皮层网络活动被探索和塑造，有效地减少了要学习的神经参数的数量。 该假说的预测是，在更快地学习相关技能的过程中，神经维度将降低 并与之前学习到的神经模式保持一致。这一项目通过利用小说来检验预测 闭环范式，慢性大范围双光子钙成像，高维数据分析， 和全息光发生刺激研究和操纵结构技能的神经基础 学习。首先，肌肉结构学习和大脑皮层网络活动之间的对应关系 探索和成形将使用大规模双光子钙成像进行研究。第二，要因果联系 神经变异到学习神经模式，将开发一种高性能的基于钙成像的BMI， 结构神经假体学习与神经探索和塑造之间的关系将是 分析过了。最后，将使用全息技术人工塑造大脑皮层网络活动的结构 光遗传刺激和神经假体技能学习测试。这项提议的长期目标是 整合了大脑计划的几个核心目标。这项提案将产生一幅动态的图景 学习大脑，并使用BMI和全息光遗传刺激演示因果关系。这部作品是 结果将有助于技能学习和记忆的概念原则，并指导BMI的设计 恢复运动和辅助学习的系统。 目的1：探讨结构运动学习与大脑皮层网络活动探索的关系 并使用新的运动任务和大规模双光子钙成像进行塑形。 目的2：探讨结构性神经假体学习与皮层网络活动的关系 探索和塑造使用高性能，钙成像为基础的BMI。 目的3：利用闭合全息光生技术人工塑造大脑皮层网络活动结构 刺激和测试对神经假体学习的影响。

Project Summary When learning new skills, experience with previously-learned skills can facilitate faster learning by constraining behavioral exploration and shaping, a concept known as “structural learning”. The motor cortex plays an essential role in learning new skills, and its initially variable activity is shaped and consolidated over learning. However, how previous experience modulates exploration and shaping of cortical network activity to facilitate new skill learning is not well understood. When the brain learns to control a brain-machine interface (BMI), cortical network activity exploration and shaping is broad (high-dimensional) in BMI-naïve subjects and constrained (low-dimensional) in BMI-experienced subjects, suggesting the following hypothesis. Hypothesis: Previous experience facilitates faster learning of new, related skills by constraining how motor cortical network activity is explored and shaped, effectively reducing the number of neural parameters to learn. The hypothesis’ prediction is that during faster learning of related skills, neural dimensionality will be decreased and aligned with previously learned neural patterns. This project tests the prediction by leveraging novel closed-loop paradigms, chronic large-scale 2-photon calcium imaging, high-dimensional data analysis, and holographic optogenetic stimulation to study and manipulate the neural basis of structural skill learning. First, the correspondence between structural learning of muscle patterns and cortical network activity exploration and shaping will be studied using large-scale 2-photon calcium imaging. Second, to causally link neural variance to learning neural patterns, a high-performance, calcium imaging-based BMI will be developed, and the relationship between structural neuroprosthetic learning and neural exploration and shaping will be analyzed. Finally, the structure of cortical network activity will be artificially shaped using holographic optogenetic stimulation and tested on neuroprosthetic skill learning. The long-term objective of this proposal integrates several core goals of the BRAIN initiative. The proposal will produce a dynamic picture of the learning brain and demonstrate causality using BMIs and holographic optogenetic stimulation. This work’s outcome will contribute conceptual principles underlying skill learning and memory and guide the design of BMI systems to restore movement and assist learning. Aim 1: Investigate the relationship between structural motor learning and cortical network activity exploration and shaping using a novel motor task and large-scale 2-photon calcium imaging. Aim 2: Investigate the relationship between structural neuroprosthetic learning and cortical network activity exploration and shaping using a high-performance, calcium imaging-based BMI. Aim 3: Artificially shape structure of cortical network activity using closed-loop holographic optogenetic stimulation and test effect on neuroprosthetic learning.