Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1M8.
Neural Netw. 2017 Dec;96:128-136. doi: 10.1016/j.neunet.2017.08.011. Epub 2017 Sep 11.
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.
本文提出了一种求解基于电导的尖峰神经元最小能量最优控制问题的算法。基本步骤为:(1)将基于电导的尖峰神经元振荡器构建为仿射非线性系统;(2)基于庞特里亚金极大值原理,将仿射非线性系统的最优控制问题表述为边值问题;(3)利用同伦摄动法求解边值问题。在同伦摄动技术框架下构建最小能量最优控制是新颖的,适用于广泛的非线性基于电导的神经元模型。通过仿真验证了我们的方法在 FitzHugh-Nagumo 和 Hindmarsh-Rose 模型中的适用性。