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Predictive coding is an influential theory of cortical function which positsthat the principal computation the brain performs, which underlies bothperception and learning, is the minimization of prediction errors. Whilemotivated by high-level notions of variational inference, detailedneurophysiological models of cortical microcircuits which can implements itscomputations have been developed. Moreover, under certain conditions,predictive coding has been shown to approximate the backpropagation of erroralgorithm, and thus provides a relatively biologically plausiblecredit-assignment mechanism for training deep networks. However, standardimplementations of the algorithm still involve potentially neurally implausiblefeatures such as identical forward and backward weights, backward nonlinearderivatives, and 1-1 error unit connectivity. In this paper, we show that thesefeatures are not integral to the algorithm and can be removed either directlyor through learning additional sets of parameters with Hebbian update ruleswithout noticeable harm to learning performance. Our work thus relaxes currentconstraints on potential microcircuit designs and hopefully opens up newregions of the design-space for neuromorphic implementations of predictivecoding.

Relaxing the Constraints on Predictive Coding Models