Abstract

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.

Original languageEnglish (US)
Pages (from-to)2269-2308
Number of pages40
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume368
Issue number1918
DOIs
StatePublished - May 13 2010

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Parkinson disease
Parkinson's Disease
Model-based Control
Control Theory
Computational Model
control theory
Control theory
Computational Neuroscience
Biophysics
brain
Brain
Confluence
neurology
biophysics
Research and Development
Kalman Filter
Medicine
Nonlinear Dynamics
Efficacy
Neuron

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Engineering(all)
  • Physics and Astronomy(all)

Cite this

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title = "Towards model-based control of parkinson's disease",
abstract = "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.",
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