CAREER: Robust Identification and Multi-Objective Control Methods for Neuronal Networks Under Uncertainty

概述：生物神经网络的控制支撑着微创脑疗法的发展，以及网络物理系统的自适应学习，但由于所涉及的不规则动力学，它仍然具有挑战性。这些系统还具有稀疏和弱连接，以及无法完全探测的一系列动态。迫切需要确定未建模的动态对这些网络的可控性的影响，并相应地开发鲁棒控制，否则控制器将继续表现不佳，脆弱，难以校准。脑深部电刺激（DBS）就是这种情况，它遵循保守的“一刀切”模式，尽管有可能治疗全球数百万人，但仍未得到充分利用。本CAREER计划的目标是开发识别方法，以估计未建模动力学对神经元回路的影响，并为这些回路提供鲁棒控制框架。将考虑帕金森病和DBS针对的脑回路，以最大限度地发挥研究的影响。这项工作将与教育计划相结合，以解决目前在神经工程师培训方面的局限性，并扩大第一代大学生在STEM领域的存在。智力优势：这项研究将填补知识库中的关键空白，该知识库提供了神经网络的全局动态与受控下单个神经元动态之间的联系。它还将为神经群和脑回路提供一个强大的控制框架。通过在神经假体的设计过程中整合建模和控制，教育活动将填补神经工程师培训中的关键空白。应用于DBS，这项研究将有助于为目前被排除在DBS治疗之外的PD人群个性化DBS，从而为慢性疾病患者提供新的选择。更广泛的影响：这项研究将有利于帕金森病患者的健康，包括那些现在被排除在DBS之外的患者。神经工程师的培训也将得到改善，从而帮助形成一支训练有素、更具全球竞争力的劳动力队伍。研究和推广的整合将最终创造一条管道，吸引高中生进入STEM领域，并促进大学预科阶段工程原理的学习。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Overview: The control of biological neural networks underpins the development of minimally-invasive brain therapies as well as adaptive learning for cyber-physical systems, but it remains challenging because of the irregular dynamics involved. These systems also have sparse and weak connections and a range of dynamics that cannot be fully probed. There is an urgent need to determine the impact of unmodeled dynamics on the controllability of these networks and develop robust controls accordingly, otherwise controllers will remain underperforming, fragile, and hard to calibrate. This is the case for deep brain stimulation (DBS), which follows a conservative "one-size-fits-all" paradigm and remains underutilized despite having the potential to treat millions of people worldwide. The objective of this CAREER program is to develop identification methods that estimate the impact of unmodeled dynamics on neuronal circuits and a robust control framework for these circuits. Brain circuits targeted by Parkinson's disease and DBS will be considered to maximize the impact of the research. The work will be paired with educational plans that address current limitations in the training of neural engineers and broaden the presence of first-generation college students in STEM.Intellectual Merits: This research will fill critical gaps in the knowledge base that provides linkage between global dynamics of a neural network and dynamics of individual neurons under control. It will also contribute a robust control framework for neural populations and brain circuits. Educational activities will fill critical gaps in the training of neural engineers by integrating modeling and control in the design process of neural prostheses. Applied to DBS, this research will help personalize DBS to PD populations who are currently excluded from this treatment, thus enabling new options for chronically ill patients.Broader Impacts: The research will benefit the well-being of Parkinson's disease patients, including patients who are now excluded from DBS. The training of neural engineers will also be improved, thus helping the formation of a better-trained and more globally-competitive workforce. The integration of research and outreach will finally create a pipeline to attract high-school students towards STEM fields and facilitate the learning of engineering principles at the pre-college level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.