Neurophysiologically-informed Design of Flexible, 2-learner Brain-Machine Interfaces for Robust and Skillful Performance

项目总结 这项建议旨在阐明神经假体技能学习的计算和神经基础 利用闭环式脑机接口科学和工程方面的最新进展。这个 拟议工作的成果有可能指导下一代 用于神经科患者的神经生理学信息、皮质控制的神经假体系统 精神错乱。最先进的脑机接口(BMI)利用机器学习快速校准到 个体的神经活动，但表现也得益于受试者学习可靠地产生所需的 神经活动模式。设计“2-学习者体质指数”的基本科学和工程原则 大脑和机器协同学习的东西并没有被很好地理解。因此，这项建议旨在 研究当机器经历不同程度的学习时，大脑是如何学习的，如何不同 大脑学习的程度影响长期的BMI表现、健壮性和概括性，以及这些是如何实现的 原则可以指导设计一个两个学习者的BMI系统，以促进大脑学习。该提案是 研究目的有三：1)研究解码者适应对神经编码发展的影响 神经假体技能的模型；2)研究解码者适应和由此产生的神经编码 模型通过扰动影响BMI性能(稳健性)，并影响未练习任务的BMI性能 (泛化)；以及3)下一代灵活的2-Learner解码器架构的设计和验证。 在这些目标中提出的分析和实验将利用所获得的关于 大脑是如何学习和获得神经假体技能的神经生理学信息设计 以及适用于新任务的高性能闭环式运动神经假体。

PROJECT SUMMARY This proposal aims to elucidate the computational and neural basis of neuroprosthetic skill learning by leveraging recent advances in the science and engineering of closed-loop brain-machine interfacing. The outcome of the proposed work has the potential to guide the development of the next generation of neurophysiologically-informed, cortically-controlled neuroprosthetic systems for patients with neurological disorders. State-of-the-art brain-machine interfaces (BMIs) leverage machine learning to rapidly calibrate to the neural activity of individuals, but performance also benefits from subjects learning to reliably produce desired neural activity patterns. The basic science and engineering principles of designing such a “2-learner BMI” in which the brain and machine synergistically learn are not well understood. Hence, this proposal aims to investigate how the brain learns when the machine undergoes different degrees of learning, how different degrees of brain learning affect long-term BMI performance, robustness, and generalization, and how these principles can guide the design of a 2-learner BMI system which facilitates brain learning. The proposal is structured in three aims: 1) To study the impact of decoder adaptation on the development of neural encoding models underlying neuroprosthetic skill; 2) To Study how decoder adaptation and resultant neural encoding model influences BMI performance with perturbations (robustness) and BMI performance on unpracticed tasks (generalization); and 3) Design and validation of the next-generation Flexible 2-Learner Decoder architecture. The analyses and experiments proposed in these aims will leverage the fundamental knowledge gained about how the brain learns and acquires neuroprosthetic skills into the neurophysiologically-informed design of robust and high-performance closed-loop motor neuroprosthetics that generalize to new tasks.