🤖 AI Summary
This study addresses the lack of a systematic review of single-compartment spiking neuron models, which has hindered a clear understanding of the trade-offs between biological plausibility and computational efficiency. For the first time, this work provides a unified survey and classification of mainstream single-compartment spiking neuron models, establishing a taxonomy based on membrane potential dynamics, discrete versus continuous simulation approaches, and mechanisms for abstracting biological behaviors. By delineating the strengths, limitations, and representative applications of each model category, this paper offers a coherent theoretical foundation and practical guidance for model selection in neuromorphic computing and brain-inspired modeling.
📝 Abstract
In this work, we reviewed different approaches in mathematical modeling of biologically plausible neural systems. Models are characterized and classified based on their common features and special use cases. In addition to spiking models, different types of discrete and continuous analogs are considered to accurately simulate biological processes, including membrane potential dynamics. The models under investigation include neurons and various components encountered in neural systems and affected the dynamics. The selection of specific approaches was driven by their prevalence and innovative perspectives in order to enhance the relevance of the presented information.