TY - GEN
T1 - Synchronization between outputs of neurons and neuron populations with discrete control algorithm basing on least-square method
AU - Jia, Chenhui
AU - Wang, Jiang
AU - Deng, Bin
AU - Wei, Xile
AU - Dong, Feng
AU - Che, Yanqiu
PY - 2012/12/1
Y1 - 2012/12/1
N2 - As a new method on curing mental diseases, Deep Brain Stimulation (DBS) gives great help to patients who do not respond to drug therapies. However, most of the DBS therapies used at present are using high-frequency signals as open-loop stimulating signals, whose mechanism is not sufficiently understood. In this paper, basing on the synchronization mechanism and the close-loop stability theory, we have designed a close-loop method to propose a potential therapy for curing mental diseases with deep brain stimulation. Through reconstruct the input-output dynamics with least square method, we can use a new regressive input-output model to describe the relationship between the input and output of the abnormal neuron population. Using the parameters estimated in the regressive model, we can design a set of DBS signals to make the output of abnormal neuron population accurately track the desired output signal. The method is robust and can be applied even when the abnormal neuron population is disturbed by heavy noise.
AB - As a new method on curing mental diseases, Deep Brain Stimulation (DBS) gives great help to patients who do not respond to drug therapies. However, most of the DBS therapies used at present are using high-frequency signals as open-loop stimulating signals, whose mechanism is not sufficiently understood. In this paper, basing on the synchronization mechanism and the close-loop stability theory, we have designed a close-loop method to propose a potential therapy for curing mental diseases with deep brain stimulation. Through reconstruct the input-output dynamics with least square method, we can use a new regressive input-output model to describe the relationship between the input and output of the abnormal neuron population. Using the parameters estimated in the regressive model, we can design a set of DBS signals to make the output of abnormal neuron population accurately track the desired output signal. The method is robust and can be applied even when the abnormal neuron population is disturbed by heavy noise.
UR - http://www.scopus.com/inward/record.url?scp=84872354500&partnerID=8YFLogxK
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U2 - 10.1109/WCICA.2012.6359426
DO - 10.1109/WCICA.2012.6359426
M3 - Conference contribution
AN - SCOPUS:84872354500
SN - 9781467313988
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 5001
EP - 5006
BT - WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
T2 - 10th World Congress on Intelligent Control and Automation, WCICA 2012
Y2 - 6 July 2012 through 8 July 2012
ER -