Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks

Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in va...

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Bibliographic Details
Main Authors:	Jangho Lee, Jeonghee Jo, Byounghwa Lee, Jung-Hoon Lee, Sungroh Yoon
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2022-11-01
Series:	Frontiers in Computational Neuroscience
Subjects:	brain-inspired learning biologically plausible learning deep learning backpropagation predictive coding
Online Access:	https://www.frontiersin.org/articles/10.3389/fncom.2022.1062678/full

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