ABSTRACT
Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch‐Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID‐19 prediction problem show the superiority of the proposed method in comparison with other state‐of‐the‐art ones. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ FROM AUTHOR] Copyright of IEEJ Transactions on Electrical & Electronic Engineering is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)