Deep predictive coding accounts for emergence of complex neural response properties along the visual cortical hierarchy

Abstract

Predictive coding provides a computational paradigm for modelling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we develop a scalable, deep predictive coding network that is trained using a Hebbian learning rule. Without a priori constraints that would force model neurons to respond like biological neurons, the model exhibits properties similar to those reported in experimental studies. We analyze low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increases systematically across ascending areas in the model hierarchy. A further emergent network property is that representations for different object classes become more distinguishable from lower to higher areas. Thus, deep predictive coding networks can be effectively trained using biologically plausible principles and exhibit emergent properties that have been experimentally identified along the visual cortical hierarchy. Significance StatementUnderstanding brain mechanisms of perception requires a computational approach based on neurobiological principles. Many deep learning architectures are trained by supervised learning from large sets of labeled data, whereas biological brains must learn from unlabeled sensory inputs. We developed a Predictive Coding methodology for building scalable networks that mimic deep sensory cortical hierarchies, perform inference on the causes of sensory inputs and are trained by unsupervised, Hebbian learning. The network models are well-behaved in that they faithfully reproduce visual images based on high-level, latent representations. When ascending the sensory hierarchy, we find increasing image selectivity, sparseness and generalizability for object classification. These models show how a complex neuronal phenomenology emerges from biologically plausible, deep networks for unsupervised perceptual representation.