Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Weight Quantization
- DOI
- 10.1162/neco.a.1568
- Published
- 2026-09-18
- Container
- Neural Computation
- Publisher
- MIT Press
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1162/neco.a.1568,
title = {Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Weight Quantization},
author = {Lucas Deckers and Benjamin Vandersmissen and Ing Jyh Tsang and Werner Van Leekwijck and Steven Latré},
year = {2026},
journal = {Neural Computation},
doi = {10.1162/neco.a.1568},
url = {https://doi.org/10.1162/neco.a.1568}
}RIS
TY - JOUR TI - Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Weight Quantization AU - Lucas Deckers AU - Benjamin Vandersmissen AU - Ing Jyh Tsang AU - Werner Van Leekwijck AU - Steven Latré PY - 2026 JO - Neural Computation DO - 10.1162/neco.a.1568 UR - https://doi.org/10.1162/neco.a.1568 ER -
APA
Deckers, L., Vandersmissen, B., Tsang, I. J., Leekwijck, W. V., & Latré, S. (2026). Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Weight Quantization. Neural Computation. https://doi.org/10.1162/neco.a.1568
Source records
- crossref · retrieved 2026-09-24T23:40:40.869Z