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

概述：生物神经网络的控制为微创脑疗法的发展以及网络物理系统的自适应学习奠定了基础，但由于涉及的动力学不规律，它仍然具有挑战性。这些系统还具有稀疏和弱的联系，以及一系列无法完全探测的动态。迫切需要确定未建模动态对这些网络的可控性的影响，并开发相应的鲁棒控制，否则控制器将仍然表现不佳、脆弱且难以校准。这就是深部脑刺激(DBS)的情况，它遵循保守的“一刀切”的范例，尽管有可能治疗全球数百万人，但仍然没有得到充分利用。这一职业计划的目标是开发识别方法，以估计未建模动态对神经元电路的影响，并为这些电路提供稳健的控制框架。帕金森氏症和星展银行针对的大脑回路将被视为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，以解决目前神经工程师培训中的限制，并扩大STEM中第一代大学生的存在。智力优势：这项研究将填补知识库中的关键空白，该知识库提供神经网络的全局动力学和受控神经元的个体动力学之间的联系。它还将为神经种群和大脑电路提供一个强大的控制框架。教育活动将通过在神经假体设计过程中集成建模和控制来填补神经工程师培训的关键空白。应用于DBS，这项研究将有助于将DBS个性化到目前被排除在这种治疗之外的PD人群，从而为慢性病患者提供新的选择。广泛的影响：这项研究将有益于帕金森氏症患者的福祉，包括现在被排除在DBS之外的患者。神经工程师的培训也将得到改善，从而有助于形成一支训练有素、更具全球竞争力的劳动力队伍。研究和推广的结合最终将创造一条管道，吸引高中生进入STEM领域，并促进大学前水平的工程学原理的学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.