Brain-inspired Computational Intelligence via Predictive Coding
It addresses the problem of biologically implausible learning algorithms in AI for researchers seeking neuroscience-inspired methods, but is incremental as it reviews existing efforts.
The paper surveys predictive coding (PC) as a biologically plausible alternative to backpropagation for training deep neural networks, highlighting its applications in machine learning, control, and robotics, but does not report specific numerical results.
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with a learning algorithm called error backpropagation, always considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and AI at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide common ground for the understanding of the recent developments, then by describing current efforts and results, and concluding with a large discussion of possible implications and ways forward.