On the Sparse Structure of Natural Sounds and Natural Images: Similarities, Differences, and Implications for Neural Coding.

ABSTRACT: Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains to some degree, it is not yet clear which aspects of this structure are universal and which vary across sensory modalities. To address this, we fit complete and highly overcomplete sparse coding models to natural images and spectrograms of speech and report on differences in the statistics learned by these models. We find several types of sparse features in natural images, which all appear in similar, approximately Laplace distributions, whereas the many types of sparse features in speech exhibit a broad range of sparse distributions, many

SUBMITTER: Dodds EM 

PROVIDER: S-EPMC6606779 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature