Evaluating the Contribution of Top-Down Feedback and Post-Learning Reconstruction

Deep generative models and their associated top-down architecture are gaining popularity in neuroscience and computer vision. In this paper we link our previous work with regulatory feedback networks to generative models. We show that generative model’s and regulatory feedback model’s equations can share the same fixed points. Thus, phenomena observed using regulatory feedback can also apply to generative models. This suggests that generative models can also be developed to identify mixtures of patterns, address problems associated with binding, and display the ability to estimate numerosity.