On the relationship between predictive coding and backpropagation
This work addresses the problem of biological realism in neural network training for researchers in computational neuroscience and machine learning, but it is incremental as it reviews and extends existing results.
The paper examines the mathematical relationship between predictive coding and backpropagation for training feedforward neural networks in supervised learning, discussing implications for biological modeling and providing a tool, Torch2PC, for implementation.
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.