2017
DOI: 10.1016/j.neunet.2017.08.011
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A new approach to optimal control of conductance-based spiking neurons

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 opti… Show more

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Cited by 6 publications
(4 citation statements)
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References 36 publications
(67 reference statements)
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“…In addition, such a way is robust against the effect of noise. An extension effort that extends the results for systems with hybrid characteristics as in [7,[22][23][24] is possible, which remains an open problem.…”
Section: Adaptive Control Schemementioning
confidence: 99%
“…In addition, such a way is robust against the effect of noise. An extension effort that extends the results for systems with hybrid characteristics as in [7,[22][23][24] is possible, which remains an open problem.…”
Section: Adaptive Control Schemementioning
confidence: 99%
“…Therefore, control algorithms which are robust enough to disturbance are able to guarantee the synchronization. Secondly, low power electrical stimulations play a critical role in the synchronization [16]. As we know, a high power electrical stimulation is harmful to biological tissues.…”
Section: Introductionmentioning
confidence: 99%
“…However, in real chaotic circuits, it is often the case that only partial information about the states (for instance, voltage) is available in the system outputs. Therefore, in order to utilize the memristive chaotic circuit systems, one often needs to estimate the system state through available measurement, and then use the estimated system to achieve synchronization, optimal control [19], or tracking performances. In addition, general results on state estimation and observer-based control for such memristive systems do not seem to have received much attention so far.…”
Section: Introductionmentioning
confidence: 99%