Firing patterns in the adaptive exponential integrate-and-fire model

By Richard Naud, Nicolas Marcille, Claudia Clopath, Wulfram Gerstner. Biological cybernetics, 2008

347 citations recorded by the source corpus.

For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/127a768c6e6664830301675c0ad17dfdeec4a3b5

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Citations in the corpus

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, methodology): “We modeled somatostatin-positive interneurons [116–118] using the adaptive exponential integrate-and-fire (AdEx) model [119]:”