Towards model-based control of Parkinson's disease

A perspective

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Since the 1950s, we have developed mature theories of modern control theory and computational neuroscience with almost no interaction between these disciplines. With the advent of computationally efficient nonlinear Kalman filtering techniques, along with improved neuroscience models that provide increasingly accurate reconstruction of dynamics in a variety of important normal and disease states in the brain, the prospects for a synergistic interaction between these fields are now strong. I show recent examples of the use of nonlinear control theory for the assimilation and control of single neuron and network dynamics, as well as the modulation of oscillatory waves in the cortex, and the assimilation of epileptic seizures. A control framework for modulating Parkinsonian dynamics is presented, and a perspective offered. As the computational models of dynamical diseases such as Parkinson's disease improve, embedding those models within rigorous model-based control frameworks is now feasible.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages6487-6491
Number of pages5
DOIs
StatePublished - Dec 1 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

Fingerprint

Parkinson's Disease
Model-based Control
Control Theory
Computational Neuroscience
Nonlinear Filtering
Network Dynamics
Neuroscience
Kalman Filtering
Nonlinear Control
Cortex
Control theory
Interaction
Computational Model
Neuron
Modulation
Model
Neurons
Brain
Framework

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Schiff, S. (2011). Towards model-based control of Parkinson's disease: A perspective. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 (pp. 6487-6491). [6160870] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2011.6160870
Schiff, Steven. / Towards model-based control of Parkinson's disease : A perspective. 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011. 2011. pp. 6487-6491 (Proceedings of the IEEE Conference on Decision and Control).
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Schiff, S 2011, Towards model-based control of Parkinson's disease: A perspective. in 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011., 6160870, Proceedings of the IEEE Conference on Decision and Control, pp. 6487-6491, 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011, Orlando, FL, United States, 12/12/11. https://doi.org/10.1109/CDC.2011.6160870

Towards model-based control of Parkinson's disease : A perspective. / Schiff, Steven.

2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011. 2011. p. 6487-6491 6160870 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Schiff S. Towards model-based control of Parkinson's disease: A perspective. In 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011. 2011. p. 6487-6491. 6160870. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2011.6160870