We present a simple rate-reduced neuron model that captures a wide range of complex, biologically plausible, and physiologically relevant spiking behavior. This includes spike-frequency adaptation, postinhibitory rebound, phasic spiking and accommodation, first-spike latency, and inhibition-induced spiking. Furthermore, the model can mimic different neuronal filter properties. It can be used to extend existing neural field models, adding more biological realism and yielding a richer dynamical structure. The model is based on a slight variation of the Rulkov map.

中文翻译：

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一个速率降低的神经元模型，用于复杂的尖峰行为。

我们提出了一个简单的速率降低的神经元模型，该模型捕获了范围广泛的复杂的，生物学上合理的和生理上相关的尖峰行为。这包括尖峰频率适应，抑制后反弹，阶段性尖峰和适应，第一次尖峰潜伏期以及抑制引起的尖峰。此外，该模型可以模拟不同的神经元过滤器属性。它可以用于扩展现有的神经场模型，增加更多的生物现实性并产生更丰富的动力学结构。该模型基于Rulkov映射的细微变化。