Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding

Predictive coding has been previously introduced as a hierarchical coding framework for the visual system. At each level, activity predicted by the higher level is dynamically subtracted from the input, while the difference in activity continuously propagates further. Here we introduce modular predictive coding as a feedforward hierarchy of prediction modules without back-projections from higher to lower levels. Within each level, recurrent dynamics optimally segregates the input into novelty and familiarity components. Although the anatomical feedforward connectivity passes through the novelty-representing neurons, it is nevertheless the familiarity information which is propagated to higher levels. This modularity results in a twofold advantage compared to the original predictive coding scheme: the familiarity-novelty representation forms quickly, and at each level the full representational power is exploited for an optimized readout. As we show, natural images are successfully compressed and can be reconstructed by the familiarity neurons at each level. Missing information on different spatial scales is identified by novelty neurons and complements the familiarity representation. Furthermore, by virtue of the recurrent connectivity within each level, non-classical receptive field properties still emerge. Hence, modular predictive coding is a biologically realistic metaphor for the visual system that dynamically extracts novelty at various scales while propagating the familiarity information.