Predictive coding model can detect novelty on different levels of representation hierarchy

Abstract

Novelty detection, also known as familiarity discrimination or recognition memory, refers to the ability to distinguish whether a stimulus has been seen before. It has been hypothesized that novelty detection can naturally arise within networks that store memory or learn efficient neural representation, because these networks already store information on familiar stimuli. However, computational models instantiating this hypothesis have not been shown to reproduce high capacity of human recognition memory, so it is unclear if this hypothesis is feasible. This paper demonstrates that predictive coding, which is an established model previously shown to effectively support representation learning and memory, can also naturally discriminate novelty with high capacity. Predictive coding model includes neurons encoding prediction errors, and we show that these neurons produce higher activity for novel stimuli, so that the novelty can be decoded from their activity. Moreover, the hierarchical predictive coding networks uniquely perform novelty detection at varying abstraction levels across the hierarchy, i.e., they can detect both novel low-level features, and novel higher-level objects. Overall, we unify novelty detection, associative memory, and representation learning within a single computational framework.