Incremental Predictive Coding: A Parallel and Fully Automatic Learning Algorithm

In recent years, deep learning has reached and surpassed human-level performance in a multitude of tasks, such as game playing [Silver et al., 2017, 2016], image recognition [Krizhevsky et al., 2012, He et al., 2016], natural language processing [Chen et al., 2020], and image generation [Ramesh et al., 2022]. These successes are achieved entirely using deep artificial neural networks trained via backpropagation (BP), which is a learning algorithm that is often criticized for its biological implausibilities [Grossberg, 1987, Crick, 1989, Abdelghani et al., 2008, Lillicrap et al., 2016, Roelfsema and Holtmaat, 2018, Whittington and Bogacz, 2019], such as lacking local plasticity and autonomy. In fact, backpropagation requires a global control signal required to trigger computations, since gradients must be sequentially computed backwards through the computation graph. These properties are not only important for biological plausibility: parallelization, locality, and automation are key to build efficient models that can be trained end-to-end on non Von-Neumann machines, such as analog chips [Kendall et al., 2020]. A learning algorithm with most of the above properties is predictive coding (PC). PC is an influential theory of information processing in the brain [Mumford, 1992, Friston, 2005], where learning happens by minimizing the prediction error of every neuron. PC can be shown to approximate backpropagation in layered networks [Whittington and Bogacz, 2017], as well as on any other model [Millidge et al., 2020], and can exactly replicate its weight update if some external control is added [Salvatori et al., 2022a]. Also the differences with BP are interesting, as PC allows for a much more flexible training and testing [Salvatori et al., 2022b], has a rich mathematical formulation [Friston, 2005, Millidge et al., 2022], and is an energy-based model [Bogacz, 2017].