Learning hierarchical representations of natural images

par/by Mike Lewicki
Computer Science Department and
Center for the Neural Basis of Cognition
Carnegie Mellon University

What are the underlying computational principles that biology uses to transform the raw sensory signal into a hierarchy of representations that subserve higher-level perceptual tasks? One hypothesis in this work is that biological representations are optimal from the viewpoint of statistical information processing, and adapt to the statistics of the natural sensory environment. In vision, we perceive complex natural edges, contours, and textures, whose retinal image is inherently highly variable. For accurate perception, the visual system must form abstract representations that remain invariant across individual fixations, but how this is achieved is poorly understood. In this talk, I will discuss an approach that is based on learning statistical distributions of local regions in a visual scene. The central hypothesis is learning these local distributions allows the visual system to generalize across similar images. I will present a model in which the joint activity of neurons encodes the probability distribution over their inputs and forms stable representations across complex patterns of variation. Trained on natural images, the model learns a compact set of functions that act as dictionary elements for image distributions typically encountered in natural scenes. Neurons in the model exhibit a wide range of properties observed in cortical neurons. These results provide a novel functional explanation for non-linear effects in complex cells in the primary visual cortex (V1) and make predictions about coding in higher visual areas, such as V2 and V4.

This is joint work with Yan Karklin.

Bio:

Dr. Lewicki an associate professor in the Computer Science Department at Carnegie Mellon University and in the CMU-University of Pittsburgh Center for the Neural Basis of Cognition. He received his BS degree in mathematics and cognitive science from Carnegie Mellon University, his PhD degree in computation and neural systems from the California Institute of Technology, and did postdoctoral studies in the Computational Neurobiology Laboratory at the Salk Institute.