  If modern computers are sometimes superior to humans in some specialized
tasks such as playing chess or browsing a large database, they can't beat the
efficiency of biological vision for such simple tasks as recognizing and
following an object in a complex cluttered background. We present in this paper
our attempt at outlining the dynamical, parallel and event-based representation
for vision in the architecture of the central nervous system. We will
illustrate this on static natural images by showing that in a signal matching
framework, a L/LN (linear/non-linear) cascade may efficiently transform a
sensory signal into a neural spiking signal and we will apply this framework to
a model retina. However, this code gets redundant when using an over-complete
basis as is necessary for modeling the primary visual cortex: we therefore
optimize the efficiency cost by increasing the sparseness of the code. This is
implemented by propagating and canceling redundant information using lateral
interactions. We compare the efficiency of this representation in terms of
compression as the reconstruction quality as a function of the coding length.
This will correspond to a modification of the Matching Pursuit algorithm where
the ArgMax function is optimized for competition, or Competition Optimized
Matching Pursuit (COMP). We will in particular focus on bridging neuroscience
and image processing and on the advantages of such an interdisciplinary
approach.
