Neural Regeneration Research ›› 2025, Vol. 20 ›› Issue (11): 3215-3216.doi: 10.4103/NRR.NRR-D-24-00629

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Inspires effective alternatives to backpropagation: predictive coding helps understand and build learning

Zhenghua Xu*, #, Miao Yu# , Yuhang Song   

  1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China (Xu Z, Yu M) Department of Computer Science, University of Oxford, Oxford, UK (Xu Z, Song Y)
  • Online:2025-11-15 Published:2025-02-23
  • Contact: Zhenghua Xu, PhD, xuzhenghua1987@gmail.com.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China, No. 62276089.

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Abstract: Artificial neural networks are capable of machine learning by simulating the hierarchical structure of the human brain. To enable learning by brain and machine, it is essential to accurately identify and correct the prediction errors, referred to as credit assignment (Lillicrap et al., 2020). It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.