Loading...
Please wait, while we are loading the content...
Direct learning-based deep spiking neural networks: a review.
| Content Provider | Europe PMC |
|---|---|
| Author | Guo, Yufei Huang, Xuhui Ma, Zhe |
| Copyright Year | 2023 |
| Abstract | The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected. |
| Page Count | 14 |
| ISSN | 16624548 |
| Journal | Frontiers in Neuroscience |
| Volume Number | 17 |
| PubMed Central reference number | PMC10313197 |
| PubMed reference number | 37397460 |
| e-ISSN | 1662453X |
| DOI | 10.3389/fnins.2023.1209795 |
| Language | English |
| Publisher | Frontiers Media S.A. |
| Publisher Date | 2023-06-16 |
| Access Restriction | Open |
| Rights License | This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Copyright © 2023 Guo, Huang and Ma. |
| Subject Keyword | spiking neural network brain-inspired computation direct learning deep neural network energy efficiency spatial-temporal processing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience |
