Topic: nonlinear generative models

A venerable tradition in neuroscience seeks to understand sensory processing, and in particular vision, through unsupervised learning of natural statistics. Generative models have provided invaluable insights into the way neural response statistics of low level vision (including nonlinearities in response means, variability, oscillations) is shaped by probabilistic inference. However, progress has been hampered by the limited capabilities of generative models to learn both nonlinear and hierarchical representations of natural images. Here we harness the inspirations coming from neuroscience to