The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from realistic Hodgkin-Huxley-type models with numerous detailed mechanisms to the phenomenological models. The adaptive exponential integrate-and-fire (AdEx) model has emerged as a convenient middle-ground model. With a low computational cost but keeping biophysical interpretation of the parameters, it has been extensively used for simulations of large neural networks. However, because of its current-based adaptation, it can generate unrealistic behaviors. We show the limitations of the AdEx model, and to avoid them, we introduce the conductance-based adaptive exponential integrate-and-fire model (CAdEx). We give an analysis of the dynamics of the CAdEx model and show the variety of firing patterns it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.

中文翻译：

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基于电导的自适应指数积分和点火模型

单个神经元的内在电生理特性可以通过广泛的模型来描述，从具有众多详细机制的现实霍奇金 - 赫胥黎型模型到现象学模型。自适应指数积分发射 (AdEx) 模型已成为一种方便的中间模型。由于计算成本低但保持参数的生物物理解释，它已被广泛用于大型神经网络的模拟。然而，由于其基于电流的适应，它可能会产生不切实际的行为。我们展示了 AdEx 模型的局限性，为了避免这些局限性，我们引入了基于电导的自适应指数积分和发射模型 (CAdEx)。我们对 CAdEx 模型的动力学进行了分析，并展示了它可以产生的各种发射模式。