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Neuroscience-inspired models, such as predictive coding, have the potentialto play an important role in the future of machine intelligence. However, theyare not yet used in industrial applications due to some limitations, such asthe lack of efficiency. In this work, we address this by proposing incrementalpredictive coding (iPC), a variation of the original framework derived from theincremental expectation maximization algorithm, where every operation can beperformed in parallel without external control. We show both theoretically andempirically that iPC is much faster than the original algorithm originallydeveloped by Rao and Ballard, while maintaining performance comparable tobackpropagation in image classification tasks. This work impacts several areas,has general applications in computational neuroscience and machine learning,and specific applications in scenarios where automatization and parallelizationare important, such as distributed computing and implementations of deeplearning models on analog and neuromorphic chips.

Incremental Predictive Coding: A Parallel and Fully Automatic Learning Algorithm