TY - GEN
T1 - Towards model-based control of Parkinson's disease
T2 - 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
AU - Schiff, Steven J.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84860699338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860699338&partnerID=8YFLogxK
U2 - 10.1109/CDC.2011.6160870
DO - 10.1109/CDC.2011.6160870
M3 - Conference contribution
AN - SCOPUS:84860699338
SN - 9781612848006
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6487
EP - 6491
BT - 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2011 through 15 December 2011
ER -