Analysing the impact of brain-inspired predictive coding dynamics through gradient based explainability methods

Multiple theories exist for the role of feedback connections in the brain and in the artificial neural networks, but remain untested using modern tools. In this work, we undertake this task by exploring the utility of explainability methods like GradCAMs[1] in investigating bio-inspired recurrent networks–provided with the predify[2] package–that perform hierarchical updates inspired by the predictive coding theory in neuroscience. We report an extensive search with different levels of feedforward and feedback information. Our preliminary results show that the dynamics are able to recover the GradCAMs on noisy images, providing promising avenues for future work aiming to understand the role of recurrence.