We establish a theoretical connection between wavelet transforms and spiking neural networks through scale-space theory. We rely on the scale-covariant guarantees in the leaky integrate-and-fire neurons to implement discrete mother wavelets that approximate continuous wavelets. A reconstruction experiment demonstrates the feasibility of the approach and warrants further analysis to mitigate current approximation errors. Our work suggests a novel spiking signal representation that could enable more energy-efficient signal processing algorithms.
Cite
@article{arxiv.2602.02020,
title = {Scale-covariant spiking wavelets},
author = {Jens Egholm Pedersen and Tony Lindeberg and Peter Gerstoft},
journal= {arXiv preprint arXiv:2602.02020},
year = {2026}
}