  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.
