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On the relationship between predictive coding and backpropagation

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  • Robert Rosenbaum

Abstract

Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to backpropagation for training neural networks. This manuscript reviews and extends recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. Implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning are discussed along with a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.

Suggested Citation

  • Robert Rosenbaum, 2022. "On the relationship between predictive coding and backpropagation," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-27, March.
    • Handle: RePEc:plo:pone00:0266102
    DOI: 10.1371/journal.pone.0266102
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    References listed on IDEAS

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    1. Timothy P. Lillicrap & Daniel Cownden & Douglas B. Tweed & Colin J. Akerman, 2016. "Random synaptic feedback weights support error backpropagation for deep learning," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
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