Sparse Spike Coding : applications of Neuroscience to the processing of natural images

Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00276638
Contributor : Laurent Perrinet <>
Submitted on : Thursday, January 22, 2009 - 9:52:56 AM
Last modification on : Thursday, January 18, 2018 - 2:09:39 AM
Long-term archiving on : Thursday, September 23, 2010 - 5:47:45 PM

Files

perrinet08spie.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00276638, version 3
  • ARXIV : 0804.4830

Collections

Citation

Laurent Perrinet. Sparse Spike Coding : applications of Neuroscience to the processing of natural images. SPIE Photonics Europe, Apr 2008, Strasbourg, France. ⟨hal-00276638v3⟩

Share

Metrics

Record views

123

Files downloads

258