University of Ottawa, Center for Neural Dynamics, Ottawa, ON, Canada.
Adv Exp Med Biol. 2022;1359:69-86. doi: 10.1007/978-3-030-89439-9_3.
The generalized integrate-and-fire (GIF) neuron model accounts for some of the most fundamental behaviours of neurons within a compact and extensible mathematical framework. Here, we introduce the main concepts behind the design of the GIF model in terms that will be familiar to electrophysiologists, and show why its simple design makes this model particularly well suited to mimicking behaviours observed in experimental data. Along the way, we will build an intuition for how specific neuronal behaviours, such as spike-frequency adaptation, or electrical properties, such as ionic currents, can be formulated mathematically and used to extend integrate-and-fire models to overcome their limitations. This chapter will provide readers with no previous exposure to modelling a clear understanding of the strengths and limitations of GIF models, along with the mathematical intuitions required to digest more detailed and technical treatments of this topic.
广义积分和点火（GIF）神经元模型在一个紧凑且可扩展的数学框架内解释了一些神经元最基本的行为。在这里，我们将用生理学家熟悉的术语来介绍 GIF 模型设计背后的主要概念，并展示为什么它的简单设计使这个模型特别适合模拟实验数据中观察到的行为。在此过程中，我们将建立一种直觉，即特定的神经元行为，如尖峰频率适应，或电特性，如离子电流，如何在数学上进行公式化，并用于扩展积分和点火模型以克服其局限性。本章将为没有建模经验的读者提供一个清晰的理解，包括 GIF 模型的优缺点，以及消化更详细和技术性的处理这个主题所需的数学直觉。
