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Neural generative models can be used to learn complex probabilitydistributions from data, to sample from them, and to produce probabilitydensity estimates. We propose a computational framework for developing neuralgenerative models inspired by the theory of predictive processing in the brain.According to predictive processing theory, the neurons in the brain form ahierarchy in which neurons in one level form expectations about sensory inputsfrom another level. These neurons update their local models based ondifferences between their expectations and the observed signals. In a similarway, artificial neurons in our generative models predict what neighboringneurons will do, and adjust their parameters based on how well the predictionsmatched reality. In this work, we show that the neural generative modelslearned within our framework perform well in practice across several benchmarkdatasets and metrics and either remain competitive with or significantlyoutperform other generative models with similar functionality (such as thevariational auto-encoder).

The Neural Coding Framework for Learning Generative Models