Scale-covariant spiking wavelets
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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.
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References · 13
- 2016Group Equivariant Convolutional Networkscited by 1,154
- 2019B-Spline CNNs on Lie Groupscited by 97
- 2020PDE-based Group Equivariant Convolutional Neural Networkscited by 28
- 2023Spiking Structured State Space Model for Monaural Speech Enhancementcited by 23
- 2023Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computingcited by 22
- 2019Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detectioncited by 9
- 2022A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past timecited by 8
- 2015Time-causal and time-recursive spatio-temporal receptive fieldscited by 7
- 2024sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networkscited by 4
- 2025Neuromorphic Computing: A Theoretical Framework for Time, Space, and Energy Scalingcited by 4
- 2024Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding schemecited by 3
- 2024Covariant spatio-temporal receptive fields for spiking neural networkscited by 3
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