On the Future of Training Spiking Neural Networks

Katharina Bendig, Katharina Bendig, René Schuster, Didier Stricker, Didier Stricker

2023

Abstract

Spiking Neural Networks have obtained a lot of attention in recent years due to their close depiction of brain functionality as well as their energy efficiency. However, the training of Spiking Neural Networks in order to reach state-of-the-art accuracy in complex tasks remains a challenge. This is caused by the inherent nonlinearity and sparsity of spikes. The most promising approaches either train Spiking Neural Networks directly or convert existing artificial neural networks into a spike setting. In this work, we will express our view on the future of Spiking Neural Networks and on which training method is the most promising for recent deep architectures.

Paper Citation

in Harvard Style

Bendig K., Schuster R. and Stricker D. (2023). On the Future of Training Spiking Neural Networks. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 466-473. DOI: 10.5220/0011745500003411

in Bibtex Style

@conference{icpram23,
author={Katharina Bendig and René Schuster and Didier Stricker},
title={On the Future of Training Spiking Neural Networks},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011745500003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - On the Future of Training Spiking Neural Networks
SN - 978-989-758-626-2
AU - Bendig K.
AU - Schuster R.
AU - Stricker D.
PY - 2023
SP - 466
EP - 473
DO - 10.5220/0011745500003411