Recent Advances and New Frontiers in Spiking Neural Networks
Duzhen Zhang, Shuncheng Jia, Qingyu Wang
Abstract
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.
BibTeX
@inproceedings{ijcai2022p790,
title = {Recent Advances and New Frontiers in Spiking Neural Networks},
author = {Zhang, Duzhen and Zhang, Tielin and Jia, Shuncheng and Wang, Qingyu and Xu, Bo},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5670--5677},
year = {2022},
month = {7},
note = {Survey Track},
doi = {10.24963/ijcai.2022/790},
url = {https://doi.org/10.24963/ijcai.2022/790},
}