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Model Order and Identifiability of Non-Linear Biological Systems in Stable Oscillation

机译:稳定振荡中非线性生物系统的模型阶数和可辨识性

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The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data.
机译:本文提出了一个理论结果,阐明了何时有可能从测量数据确定稳定振荡的生物系统的非线性动力学方程。事实证明,为此所需的最小顺序取决于系统的稳定轨道自身不相交的最小维度。用模拟的四阶霍奇金-赫克斯利峰神经元模型对此进行了说明,该模型使用非线性二阶微分方程模型进行了识别。仿真结果说明,仅给定周期性测量数据,即可唯一确定尖峰神经元的基础高阶模型。本文的结果在确定稳定振荡下的生物系统动力学时具有普遍的有效性,并说明了在基于周期数据验证模型时需要仔细解决非线性可识别性方面的需求。

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