This talk explores sparse coding of natural images in the highly overcomplete regime. I show that as the overcompleteness ratio approaches 10x, new types of dictionary elements emerge beyond the classical Gabor function shape obtained from complete or only modestly overcomplete sparse coding. These more diverse dictionaries allow images to be approximated with lower L1 norm (for a fixed SNR), and the coefficients exhibit steeper decay. I also evaluate the learned dictionaries in a denoising task, showing that higher degrees of overcompleteness yield modest gains in performance. These results are of relevance to neuroscience, because the neural representation of images in cortical area V1 is also highly overcomplete. Possible advantages of overcompleteness in image representation will be discussed. Host: Richard Baraniuk