2020/12/07 by Alexander G. Ororbia, Alexander Ororbia, Daniel Kifer +2 · 5 citations
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #cs.AI #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2012.03405
Next round revisions made -- additional results, diagrams, naming convention and framework organization updated (see both main text and manuscript)
openalex publication_date 2020/12/07 · arxiv created 2022/01/04 · arxiv updated 2022/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive processing theory, the neurons in the brain form a hierarchy in which neurons in one level form expectations about sensory inputs from another level. These neurons update their local models based on differences between their expectations and the observed signals. In a similar way, artificial neurons in our generative models predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality. In this work, we show that the neural generative models learned within our framework perform well in practice across several benchmark datasets and metrics and either remain competitive with or significantly outperform other generative models with similar functionality (such as the variational auto-encoder).