” Following Helmholtz, we view the human perceptual system as a statistical inference engine whose function is to infer the probable causes of sensory input. We show that a device of this kind can learn how to perform these inferences without requiring a teacher to label each sensory input vector with its underlying causes. A recognition model is used to infer a probability distribution over the underlying causes from the sensory input, and a separate generative model, which is also learned, is used to train the recognition model.
The Helmholtz machine fits comfortably within the framework of Grenander’s pattern theory (Grenander 1976) in the
form of Mumford’s (1994) proposals for the mapping onto the brain.”