  The representation of images in the brain is known to be sparse. That is, as
neural activity is recorded in a visual area ---for instance the primary visual
cortex of primates--- only a few neurons are active at a given time with
respect to the whole population. It is believed that such a property reflects
the efficient match of the representation with the statistics of natural
scenes. Applying such a paradigm to computer vision therefore seems a promising
approach towards more biomimetic algorithms. Herein, we will describe a
biologically-inspired approach to this problem. First, we will describe an
unsupervised learning paradigm which is particularly adapted to the efficient
coding of image patches. Then, we will outline a complete multi-scale framework
---SparseLets--- implementing a biologically inspired sparse representation of
natural images. Finally, we will propose novel methods for integrating prior
information into these algorithms and provide some preliminary experimental
results. We will conclude by giving some perspective on applying such
algorithms to computer vision. More specifically, we will propose that
bio-inspired approaches may be applied to computer vision using predictive
coding schemes, sparse models being one simple and efficient instance of such
schemes.
