Conductance-based Adaptive Exponential integrate-and-fire model.
Damien Depannemaecker, Tomasz Gorski and Alain Destexhe.

Neural Computation 33: 41-66, 2021.

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The intrinsic electrophysiological properties of single neurons can be described by a broad spectrum of models, from the most 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 simulation 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 dynamic of the CAdEx model and we show the variety of firing patterns that it can produce. We propose the CAdEx model as a richer alternative to perform network simulations with simplified models reproducing neuronal intrinsic properties.