The response of a neuron when receiving a periodic input current signal is a periodic spike firing rate signal. The frequency of an input sinusoidal current and the surrounding environment such as background noises are two important factors that affect the firing rate output signal of a neuron model. This study focuses on the phase shift between input and output signals, and here we present a new concept: the agility of a neuron, to describe how fast a neuron can respond to a periodic input signal. In this study, we derived three agility score functions for the balanced leaky integrate-and-fire model, the Hodgkin–Huxley model, and the Connor–Stevens neuron model. By applying the score of agility, we are capable of characterizing the surrounding environment; once the frequency of the periodic input signal is given, the actual angle of phase shift can then be determined and, therefore, different neuron models can be normalized and compared with each other.

中文翻译：

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神经元的敏捷性：正弦电流输入和放电率曲线之间的相移

神经元在接收周期性输入电流信号时的响应是周期性尖峰放电率信号。输入正弦电流的频率和周围环境（如背景噪声）是影响神经元模型放电率输出信号的两个重要因素。这项研究侧重于输入和输出信号之间的相移，在这里我们提出了一个新概念：神经元的敏捷性，以描述神经元对周期性输入信号的响应速度。在这项研究中，我们为平衡泄漏积分和发射模型、Hodgkin-Huxley 模型和 Connor-Stevens 神经元模型推导出了三个敏捷性评分函数。通过应用敏捷性分数，我们能够表征周围环境；一旦给定周期输入信号的频率，