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

职业：不确定性下神经网络的鲁棒识别和多目标控制方法

• 批准号: 1845348
• 负责人: Sabato Santaniello
• 金额: $ 50万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845348&HistoricalAwards=false
• 关键词: CAREER; Robust; Identification; Multi; Objective

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.

概述：生物神经网络的控制是微创脑治疗和网络物理系统自适应学习的基础，但由于涉及不规则的动力学，它仍然具有挑战性。这些系统也有稀疏和薄弱的连接，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，并相应地开发鲁棒控制，否则控制器将仍然表现不佳，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，但仍然没有得到充分利用。这个职业计划的目标是开发识别方法，估计未建模的动态神经元电路和这些电路的鲁棒控制框架的影响。帕金森病和DBS针对的脑回路将被考虑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，以解决当前神经工程师培训的局限性，并扩大第一代大学生在STEM中的存在。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与受控的单个神经元动力学之间的联系。它还将为神经群体和大脑回路提供一个强大的控制框架。教育活动将通过将建模和控制集成到神经假体的设计过程中，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这种治疗之外的PD人群提供个性化的DBS，从而为慢性病患者提供新的选择。更广泛的影响：这项研究将使帕金森病患者的福祉受益，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。研究和推广的整合最终将创造一个管道，以吸引高中学生对STEM领域，并促进在大学预科水平的工程原理的学习。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Role of cerebellar GABAergic dysfunctions in the origins of essential tremor

小脑 GABA 能功能障碍在特发性震颤起源中的作用

• DOI: 10.1073/pnas.1817689116
• 发表时间: 2019
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Zhang, Xu;Santaniello, Sabato
• 通讯作者: Santaniello, Sabato