 In this manuscript, I review and extend recent work on the  relationship between predictive coding and backpropagation for training  artificial neural networks on supervised learning tasks. I also discuss some implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning and I describe a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.
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 offered as a potentially more biologically realistic alternative to backpropagation for training neural networks. In this manuscript, I review and extend recent work on the mathematical relationship between predictive coding and backpropagation for training feedforward artificial neural networks on supervised learning tasks. I discuss some implications of these results for the interpretation of predictive coding and deep neural networks as models of biological learning and I describe a repository of functions, Torch2PC, for performing predictive coding with PyTorch neural network models.
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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 offered as a potentially more biologically realistic alternative to backpropagation for training neural networks.
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mathematical
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feedforward
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also