Spiking Neural Networks: A Survey

• Type: Article in International Scientific Journal
• Date: 2022

Authors

João D. Nunes (Author) Other; Marcelo Carvalho (Author) Other; Diogo Carneiro (Author) Other; Jaime S. Cardoso (Author) FEUP

Journal

Title: IEEE Access

Vol. 10

Pages: 60738-60764

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 60738 Citations

Other information

Authenticus ID: P-00W-PPN

DOI: 10.1109/access.2022.3179968

Abstract (EN): The field of Deep Learning (DL) has seen a remarkable series of developments with increasingly accurate and robust algorithms. However, the increase in performance has been accompanied by an increase in the parameters, complexity, and training and inference time of the models, which means that we are rapidly reaching a point where DL may no longer be feasible. On the other hand, some specific applications need to be carefully considered when developing DL models due to hardware limitations or power requirements. In this context, there is a growing interest in efficient DL algorithms, with Spiking Neural Networks (SNNs) being one of the most promising paradigms. Due to the inherent asynchrony and sparseness of spike trains, these types of networks have the potential to reduce power consumption while maintaining relatively good performance. This is attractive for efficient DL and, if successful, could replace traditional Artificial Neural Networks (ANNs) in many applications. However, despite significant progress, the performance of SNNs on benchmark datasets is often lower than that of traditional ANNs. Moreover, due to the non-differentiable nature of their activation functions, it is difficult to train SNNs with direct backpropagation, so appropriate training strategies must be found. Nevertheless, significant efforts have been made to develop competitive models. This survey covers the main ideas behind SNNs and reviews recent trends in learning rules and network architectures, with a particular focus on biologically inspired strategies. It also provides some practical considerations of state-of-the-art SNNs and discusses relevant research opportunities.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 27