# Scale-covariant spiking wavelets

arXiv: https://arxiv.org/abs/2602.02020 · HTML: https://mlsanity.com/paper/2602.02020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [B-Spline CNNs on Lie Groups](https://mlsanity.com/paper/1909.12057/) (2019)
- [PDE-based Group Equivariant Convolutional Neural Networks](https://mlsanity.com/paper/2001.09046/) (2020)
- [Spiking Structured State Space Model for Monaural Speech Enhancement](https://mlsanity.com/paper/2309.03641/) (2023)
- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection](https://mlsanity.com/paper/1910.09993/) (2019)
- [A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time](https://mlsanity.com/paper/2202.09209/) (2022)
- [Time-causal and time-recursive spatio-temporal receptive fields](https://mlsanity.com/paper/1504.02648/) (2015)
- [sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks](https://mlsanity.com/paper/2403.05772/) (2024)
- [Neuromorphic Computing: A Theoretical Framework for Time, Space, and Energy Scaling](https://mlsanity.com/paper/2507.17886/) (2025)
- [Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme](https://mlsanity.com/paper/2402.11748/) (2024)
- [Covariant spatio-temporal receptive fields for spiking neural networks](https://mlsanity.com/paper/2405.00318/) (2024)
- [A time-causal and time-recursive analogue of the Gabor transform](https://mlsanity.com/paper/2308.14512/) (2023)

## Abstract (arXiv)

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.