This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized for classification tasks, their application to Scientific Machine Learning and regression remains underexplored. We focus on the membrane component of SNNs, comparing four neuron models: Leaky Integrate-and-Fire, FitzHugh-Nagumo, Izhikevich, and Hodgkin-Huxley. We investigate their effect on SNN accuracy and efficiency for function regression tasks, by using Euler and Runge-Kutta 4th-order approximation schemes. We show how more biologically plausible neuron models improve the accuracy of SNNs while reducing the number of spikes in the system. The latter represents an energetic gain on actual neuromorphic chips since it directly reflects the amount of energy required for the computations.

翻译：本文探讨了生物可解释神经元模型对脉冲神经网络（SNN）在回归任务中性能的影响。尽管SNN在分类任务中被广泛认可，但其在科学机器学习与回归领域的应用尚未得到充分探索。我们聚焦于SNN的膜组件，比较了四种神经元模型：泄露积分点火模型、菲茨休-南云模型、伊兹克维奇模型以及霍奇金-赫胥黎模型。通过采用欧拉和四阶龙格-库塔近似方案，研究了这些模型对函数回归任务中SNN精度与效率的影响。研究表明，更具生物可解释性的神经元模型能够在提升SNN精度的同时减少系统中的脉冲数量。后者对实际神经形态芯片具有能效优势，因其直接反映了计算所需的能量消耗。