Corrigendum: A Curiosity-Based Learning Method for Spiking Neural Networks

In the original article, there was an error. In the original main text, there was an inaccurate statement sentence the result of NETalk in Table 3.

A correction has been made to Experiments, The validation of CBSNN on otherdatasets:

• NETtalk (Sejnowski and Rosenberg, 1987) is usually used for speech generation, consisting 5,033 training and 500 test. The input is a string of letters with fixed length of 7, which is encoded into 189 dimensions (each character has a 27 length one-hot vector). The output is 26 dimensions which represent 72 phonetic principles. For this mapping task with strong global regularities, VPSNN reaches 0.8680 accuracy. Although CBSNN is only slightly higher than VPSNN, it saves about half of the computation cost.

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.

Summary

Keywords

curiosity, spiking neural network, novelty, STDP, voltage-driven plasticity-centric SNN

Citation

Shi M, Zhang T and Zeng Y (2020) Corrigendum: A Curiosity-Based Learning Method for Spiking Neural Networks. Front. Comput. Neurosci. 14:28. doi: 10.3389/fncom.2020.00028

Received

21 February 2020

Accepted

23 March 2020

Published

15 April 2020

Volume

14 - 2020

Edited and reviewed by

Huajin Tang, Zhejiang University, China

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Copyright

© 2020 Shi, Zhang and Zeng.

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: Yi Zeng yi.zeng@ia.ac.cn

†These authors have contributed equally to this work

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