Section: Partnerships and Cooperations

International Initiatives

CORTINA

• Title: Retina neural network coding

• Inria principal investigator: Bruno CESSAC

• International Partner (Institution - Laboratory - Researcher):

  • Technical University Federico Santa Maria, Valparaíso (Chile) - Electronics Engeneering Department - Bruno CESSAC

• Duration: 2011 - 2013

• See also: http://cortex.loria.fr/Projects/Cortina

• Much progress has been made in the last decades in understanding the basic organization and function of the nervous system in general. Contributions to this end have come from various domains including computational neuroscience and numerical science of the information in general. The goal of this associate team is to combine our complementary expertise, from experimental biology and mathematical models (U de Valparaiso and U Federico Santa-Maria) to computational neuroscience (CORTEX and NEUROMATHCOMP), in order to develop numerical tools for the study and characterization of neural coding and related sensory-motor loops. Recording and modeling spike trains from the retina neural network, an accessible part of the brain, is a difficult task that our partnership can address, what constitute an excellent and unique opportunity to work together sharing our experience and to focus in developing computational tools for methodological innovations. To understand how the neural spike coding from natural image sequences works we are addressing the following issues: How visual signals are coded at earlier steps in the case of natural vision? What are their functions? What are the computational coding principles explaining (in artificial or biological system) the statistical properties of natural images? We wish to advance our actual knowledge in natural and artificial visual signals processing and apply it to the field of education; to foster better capacities for learning and memory; sensory prosthesis design, to will help unpaired sensory persons to sense the world and physical rehabilitation, among others. In the context of the cooperation between the Inria and Chile, we propose to develop new neural decoding algorithms that are transverse to several field and applications.

• Type: Algorithms for modeling the visual system: From natural vision to numerical applications.

• Principal Investigator: Thierry Viéville (Mnemosyne)

• International partner:

  • Institution: University of Valparaiso (Chile)

  • Laboratory: Centro Interdiciplinario de Neurociencia de Valparaiso

  • Researcher: Adrian PALACIOS

• International partner:

  • Institution: UTFSM Valparaiso (Chile)

  • Laboratory: Direccion General de Investigacion y Postgrado de Valparaiso

  • Researcher: Maria-Jose ESCOBAR

• Duration: 2011 - 2013

• See also: http://cortex.loria.fr/Research/Keops

• Abstract: KEOpS attempts to study and model the non-standard behavior of retinal (ganglion cells) sensors observed in natural scenarios. KEOpS also attempts to incorporate the resulting models into real engineering applications as new dynamical early-visual modules. The retina, an accessible part of the brain, is a unique model for studying the neural coding principles for natural scenarios. A recent study proposes that some visual functions (e.g. movement, orientation, anticipatory temporal prediction, contrast), thought to be the exclusive duty of higher brain centers, are actually carried at the retina level. The anatomical and physiological segregation of visual scenes into spatial, temporal and chromatic channels begins at the retina through the action of local neural networks. However, how the precise articulation of this neural network contributes to local solutions and global perception necessary to resolve natural task remains in general a mystery. KEOpS thus attempts to study the complexity of retinal ganglion cells (the output to the brain) behaviors observed in natural scenarios and to apply this result to artificial visual systems. We revisit both the retinal neural coding information sent to the brain, and at the same time, the development of new engineering applications inspired by the understanding of such neural encoding mechanisms. We develop an innovative formalism that takes the real (natural) complexity of retinal responses into account. We also develop new dynamical early-visual modules necessary to solve visual problems task.