Lateral predictive coding revisited: internal model, symmetry breaking, and response time

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception. It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors. Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multilayered networks but largely ignored lateral recurrent interactions. We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions. We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits. We find that learning will generally break the interaction symmetry between peer neurons, and that high input correlation between two neurons does not necessarily bring strong direct interactions between them. The optimized network responds to familiar input signals much faster than to novel or random inputs, and it significantly reduces the correlations between the output states of pairs of neurons.

Automated summary

Predictive coding is a significant theoretical framework in neuroscience that utilizes internal models and prediction errors to perceive and understand the external world. This study focuses on the effects of lateral interactions in predictive coding, and through experiments and simulations, interesting phenomena are discovered, such as learning breaking the symmetry between neurons and high input correlation not necessarily leading to strong direct interactions.