Center for Neural Engineering, Department of Neurosurgery, Pennsylvania State University, University Park, PA 16802, USA.
Philos Trans A Math Phys Eng Sci. 2010 May 13;368(1918):2269-308. doi: 10.1098/rsta.2010.0050.
Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson's disease is gaining increasing acceptance. Thus, the confluence of these three developments--control theory, computational neuroscience and deep brain stimulation--offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson's disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development.
现代基于模型的控制理论使我们能够跟踪所观察到的系统的非线性动态，并设计出前所未有的高效控制系统，从而实现了重大突破。与此同时，我们构建计算模型以体现我们对神经元及其网络生物物理学的不断扩展的知识的能力也在迅速成熟。在治疗人类动态疾病方面，我们使用深部脑刺激器治疗帕金森病的方法越来越被接受。因此，这三个方面的发展——控制理论、计算神经科学和深部脑刺激——为治疗这种疾病提供了一个独特的机会。本文探讨了科学、医学和工程领域的相关现状，并提出了一种基于模型的帕金森病控制策略。我们提出了一套使用基底神经节计算模型的初步计算方法，这些模型是基于无迹卡尔曼滤波器构建的，用于跟踪观测和规定控制。基于这些发现，我们将为未来的研究和发展提供建议。
