Scale-covariant spiking wavelets
Abstract
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.
Fields of Study & Subjects
Mathematical Analysis and Transform Methods
Advanced Memory and Neural Computing
Neural Networks and Applications
Publication details
- Journal / source
- KTH Publication Database DiVA (KTH Royal Institute of Technology)
- Publication date
- 2 February 2026
- Language
- EN
- Document type
- Preprint