Topic: generative modeling

Understanding how the brain encodes stimuli is a fundamental problem in computational neuroscience. This has inspired the development of artificial neural networks that learn representations by incorporating brain-like learning abilities. Recent work has focused on learning representations (latents) by capturing similarity between inputs and latents, but these methods have typically been used to learn downstream features from the input and have not been studied in the context of a generative paradigm, where one can map the representations back to the input space, incorporating not only bottom-