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CORRECTION article

Front. Neurosci., 16 May 2023
Sec. Neuromorphic Engineering
Volume 17 - 2023 | https://doi.org/10.3389/fnins.2023.1204334

Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

This article is a correction to:

  1. SCTN: Event-based object tracking with energy-efficient deep convolutional spiking neural networks

    1. Read original article
\r\nMingcheng JiMingcheng Ji1Ziling WangZiling Wang1Rui YanRui Yan2Qingjie LiuQingjie Liu3Shu Xu Shu Xu3*Huajin Tang, Huajin Tang1,4*
  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, China
  • 2College of Computer Science, Zhejiang University of Technology, Hangzhou, China
  • 3Machine Intelligence Laboratory, China Nanhu Academy of Electronics and Information Technology, Jiaxing, China
  • 4Zhejiang Lab, Hangzhou, China

A corrigendum on
SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

by Ji, M., Wang, Z., Yan, R., Liu, Q., Xu, S., and Tang, H. (2023). Front. Neurosci. 17:1123698. doi: 10.3389/fnins.2023.1123698

In the published article, there was an error in the Funding statement that we omitted the acknowledgment information in this article. We would like to add this acknowledgment after the conclusion section. The correct funding statement appears below.

Funding

This work was supported by the Key Research Project of Zhejiang Lab under Grant: 2021KC0AC01 and the National Natural Science Foundation of China under Grant: 62236007.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: spiking neural networks, event cameras, object tracking, exponential IoU, event-based tracking dataset

Citation: Ji M, Wang Z, Yan R, Liu Q, Xu S and Tang H (2023) Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks. Front. Neurosci. 17:1204334. doi: 10.3389/fnins.2023.1204334

Received: 12 April 2023; Accepted: 24 April 2023;
Published: 16 May 2023.

Edited by:

Guoqi Li, Tsinghua University, China

Reviewed by:

Michel Paindavoine, Université de Bourgogne, France
Michael Wynn Hopkins, The University of Manchester, United Kingdom

Copyright © 2023 Ji, Wang, Yan, Liu, Xu and Tang. 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.

*Correspondence: Huajin Tang, aHRhbmcmI3gwMDA0MDt6anUuZWR1LmNu; Shu Xu, eHVzaHUmI3gwMDA0MDtjbmFlaXQuY29t

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.