Scale-covariant spiking wavelets
This work addresses the problem of enabling more energy-efficient signal processing algorithms for researchers in neuromorphic computing, but it appears incremental as it builds on existing scale-covariant guarantees.
The paper tackled the problem of connecting wavelet transforms with spiking neural networks using scale-space theory, resulting in a novel spiking signal representation that demonstrated feasibility in a reconstruction experiment, though with current approximation errors.
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.