Optimal control of neurons using the homotopy perturbation method

Isuru Dasanayake; Anatoly Zlotnik; Wei Zhang; Jr Shin Li

doi:10.1109/CDC.2013.6760401

Optimal control of neurons using the homotopy perturbation method

Isuru Dasanayake, Anatoly Zlotnik, Wei Zhang, Jr Shin Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The behavior of many natural and engineered systems is determined by oscillatory phenomena for which the input-output relationship can be described using phase models. The use of such models significantly reduces the complexity of control design, and enables the application of powerful semi-analytical methods for optimal control synthesis. In this paper, we examine the optimal control of a collection of neuron oscillators described by phase models. In particular, we employ Pontryagin's maximum principle to formulate the optimal control problem as a boundary value problem, which we then solve using the homotopy perturbation method. This iterative optimization-free technique is promising for neural engineering applications that involve nonlinear oscillatory systems for which phase model representations are feasible.

Original language	English
Title of host publication	2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3385-3390
Number of pages	6
ISBN (Print)	9781467357173
DOIs	https://doi.org/10.1109/CDC.2013.6760401
State	Published - 2013
Event	52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy Duration: Dec 10 2013 → Dec 13 2013

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	52nd IEEE Conference on Decision and Control, CDC 2013
Country/Territory	Italy
City	Florence
Period	12/10/13 → 12/13/13

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10.1109/CDC.2013.6760401

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Dasanayake, I., Zlotnik, A., Zhang, W., & Li, J. S. (2013). Optimal control of neurons using the homotopy perturbation method. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 3385-3390). Article 6760401 (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760401

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title = "Optimal control of neurons using the homotopy perturbation method",

abstract = "The behavior of many natural and engineered systems is determined by oscillatory phenomena for which the input-output relationship can be described using phase models. The use of such models significantly reduces the complexity of control design, and enables the application of powerful semi-analytical methods for optimal control synthesis. In this paper, we examine the optimal control of a collection of neuron oscillators described by phase models. In particular, we employ Pontryagin's maximum principle to formulate the optimal control problem as a boundary value problem, which we then solve using the homotopy perturbation method. This iterative optimization-free technique is promising for neural engineering applications that involve nonlinear oscillatory systems for which phase model representations are feasible.",

author = "Isuru Dasanayake and Anatoly Zlotnik and Wei Zhang and Li, {Jr Shin}",

year = "2013",

doi = "10.1109/CDC.2013.6760401",

language = "English",

isbn = "9781467357173",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3385--3390",

booktitle = "2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013",

note = "52nd IEEE Conference on Decision and Control, CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",

}

Dasanayake, I, Zlotnik, A, Zhang, W & Li, JS 2013, Optimal control of neurons using the homotopy perturbation method. in 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013., 6760401, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 3385-3390, 52nd IEEE Conference on Decision and Control, CDC 2013, Florence, Italy, 12/10/13. https://doi.org/10.1109/CDC.2013.6760401

Optimal control of neurons using the homotopy perturbation method. / Dasanayake, Isuru; Zlotnik, Anatoly; Zhang, Wei et al.
2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 3385-3390 6760401 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - The behavior of many natural and engineered systems is determined by oscillatory phenomena for which the input-output relationship can be described using phase models. The use of such models significantly reduces the complexity of control design, and enables the application of powerful semi-analytical methods for optimal control synthesis. In this paper, we examine the optimal control of a collection of neuron oscillators described by phase models. In particular, we employ Pontryagin's maximum principle to formulate the optimal control problem as a boundary value problem, which we then solve using the homotopy perturbation method. This iterative optimization-free technique is promising for neural engineering applications that involve nonlinear oscillatory systems for which phase model representations are feasible.

AB - The behavior of many natural and engineered systems is determined by oscillatory phenomena for which the input-output relationship can be described using phase models. The use of such models significantly reduces the complexity of control design, and enables the application of powerful semi-analytical methods for optimal control synthesis. In this paper, we examine the optimal control of a collection of neuron oscillators described by phase models. In particular, we employ Pontryagin's maximum principle to formulate the optimal control problem as a boundary value problem, which we then solve using the homotopy perturbation method. This iterative optimization-free technique is promising for neural engineering applications that involve nonlinear oscillatory systems for which phase model representations are feasible.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 10 December 2013 through 13 December 2013

ER -

Optimal control of neurons using the homotopy perturbation method

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