论文标题
基于电导模型的反馈标识
Feedback Identification of conductance-based models
论文作者
论文摘要
本文将经典预测误差法（PEM）应用于受输入添加噪声的神经元系统的非线性离散时间模型的估计。尽管非线性系统表现出兴奋性，分叉和极限周期振荡，但我们证明了输出反馈下参数估计过程的一致性。因此，本文为应用传统的非线性系统识别方法应用于离散时间随机神经元系统提供了严格的框架。主要结果利用了基本特性，即基于电导的神经元模型具有指数收缩的逆动力学。自霍奇金（Hodgkin）和赫兹利（Huxley）开创性工作以来，电压钳实验隐含了这种特性，这是神经元的基本建模实验。
This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise. While the nonlinear system exhibits excitability, bifurcations, and limit-cycle oscillations, we prove consistency of the parameter estimation procedure under output feedback. Hence, this paper provides a rigorous framework for the application of conventional nonlinear system identification methods to discrete-time stochastic neuronal systems. The main result exploits the elementary property that conductance-based models of neurons have an exponentially contracting inverse dynamics. This property is implied by the voltage-clamp experiment, which has been the fundamental modeling experiment of neurons ever since the pioneering work of Hodgkin and Huxley.
