A new approach to optimal control of conductance-based spiking neurons. (December 2017)

• Record Type: Journal Article
• Authors: Lou, Xuyang; Swamy, M.N.S.
• Is Part Of: Neural networks. Volume 96(2017)
• Page Start: 128
• Page End: 136
• Publication Date: 2017-12
• Subjects: Spiking neurons -- Optimal control -- Homotopy perturbation method; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.08.011
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents an algorithm for solving the minimum-energy optimal control problem of conductance-based spiking neurons. The basic procedure is (1) to construct a conductance-based spiking neuron oscillator as an affine nonlinear system, (2) to formulate the optimal control problem of the affine nonlinear system as a boundary value problem based on Pontryagin's maximum principle, and (3) to solve the boundary value problem using the homotopy perturbation method. The construction of the minimum-energy optimal control in the framework of the homotopy perturbation technique is novel and valid for a broad class of nonlinear conductance-based neuron models. The applicability of our method in the FitzHugh–Nagumo and Hindmarsh–Rose models is validated by simulations.