Team Odyssée

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Section: Contracts and Grants with Industry

Fondation d'Entreprise EADS: A multi-scale investigation of the operating brain with an eye on visual perception.

Participants : Maureen Clerc, Rachid Deriche, Olivier Faugeras, Renaud Keriven, Pierre Kornprobst, Théo Papadopoulo.

This project deals with the problem of better measuring, modeling and simulating the set of representations that are used and the flow of processing that is performed in the human brain to achieve efficient visual perception. This is indeed a challenge because despite all the knowledge that has been accumulated on the functioning of the brain over the last years, many very basic questions still remain open, e.g.: What is the “information” conveyed by neuronal electrical and chemical activity? How is the information encoded in this activity? How is the information distributed among brain areas? In particular, what are the respective roles of feedforward and feedback connections between brain areas? Can we infer any “computational” paradigms from the observation of the functioning of the brain and the computer simulation of parts of this functioning? Most of these questions arise from the fact that it has proven to be extremely difficult to connect 1) the small scale knowledge of the functioning of one neuron or a small population of neurons (chemical/electrical models) to 2) the large scale (in space and/or in time) knowledge (spatial organisation, main connections, spatial and temporal activations, . . . ) provided by brain imagery observations (functional Magnetic Resonance Images (fMRI), MagnetoEncephalography (MEG), ElectroEncephalography (EEG), Diffusion Magnetic Resonance Images (DMRI), optical imaging). Similarly, the large scale knowledge of the brain activations has turned out to be difficult to relate to 3) the mathematical and computational principles underlying their (somewhat) equivalent computer implementations (when they exist). As an example, what we know about the processing of visual motion in humans has hardly ever been compared with the field of motion analysis in computer vision. But certainly the abilities of the best computer programs in terms of the analysis of 2D and 3D motions of objects in video sequences of images are way behind the state of the art of most mammalian brains. The intent of this project is double. First we want to build some connections between these three levels of description, particularly for the low-level vision areas of the brain and the feedback loops between these areas. Second we want to show that this increase of knowledge can be put to good use from the technological standpoint and opens the door to new ways of interacting with the machines our societies build. The project covers some of the parts of the current research program of the Odyssée laboratory which are not covered by other grants. The potential impacts of our research are multifold:

  1. By combining single-neuron models (microscopic scale) which can reproduce large numbers of observed spiking behaviours (see point 6 below) into medium size networks (containing of the order of 105 individuals and their connections), we will be able to reach the so-called mesoscopic scale of what seems to be the elementary processing unit in the human cortex, the cortical column. The computer simulation of a few of these units can be achieved using existing simulators such as mvaspike. The results can be confronted with optical imaging measurements which can also be used to estimate the parameters. Bridging the gap between the microscopic and mesoscopic levels of description is an important challenge in neuroscience.

  2. By combining these neural-mass models with a description of the cortex geometry such as the one obtained from anatomical MRI and anatomical connectivity such as the one obtained from DMRI we will be able to reach the macroscopic level of description of a signicant part of a brain area. The computer simulation of these parts can then be confronted with fMRI, MEG and EEG measurements since they operate at comparable spatio-temporal scales. Bridging the gap between the mesoscopic and macroscopic levels of description can have an important impact for the understanding of such aspects of brain disfunctioning as epilepsy. Related to this remark this will also provide better electrical source models which are much needed in MEG and EEG.

  3. Still at the macroscopic level, the role of feedback connections between brain areas is much less well known and understood than that of feedforward ones. They seem to be central for some fundamental visual processes such as figure-ground segregation and attention where they are likely to carry learned or innate priors. Furthermore, they are a generic organizational feature of the cortex, therefore the knowledge acquired in the context of the processing of visual information can potentially be transferred to other areas than vision; this may contribute to dene new computational paradigms for information processing, e.g., in computer vision where the use of priors is becoming essential.

  4. Pushing the level of sophistication of brain descriptions (electrical source models, geometry, physical properties of tissues) used into the imaging methods can lead to better tools or at the very least to a better understanding of the limitations of the existing ones and thus ways to improve them. This may contribute to enhance currently available medical imaging techniques, in a broad sense, and therefore have a strong impact on Health programs.

  5. Low-level vision areas in the brain correspond to functions that have fairly well-denned counterparts in the computer vision field. It would therefore be very interesting to compare the performances of biologically inspired and computer vision based algorithms in particular to investigate whether the latter have intrinsic limitations with respect to the former or/and to assess the level of details absolutely necessary to reproduce interesting aspects of brain behaviour.

  6. Models of “computation” should also be compared. Traditional neural networks process continuous quantities in a way that resembles how an analog or digital computer using oating point arithmetic would solve a minimization problem or compute the solution of a partial differential equation. Real neurons deal with action potentials which are discrete events (their duration is of the order of 1ms), spikes, that are produced every few milliseconds in the 1011 neurons of a human brain, propagate along the 1015 connections between them and create or inhibit electrical activity here and there. The way such huge asynchronous networks can embody the kind of computation that seems to be necessary to achieve, e.g., visual perception, is very different from that of traditional neural network technology (which failed in this program) and essentially unknown. Unveiling some of these mysteries can potentially have a strong impact on computation paradigms for many real time applications and for such emerging areas as Brain Computer Interface.

In this project we focus on the points 2-5 above, points 1 and 6 being partly supported by another grant (European project FACETS). Bullier has shown that the time scale at which the feedback connections referred to above occur in the visual system is of the order of a few tens of milliseconds. This is way beyond what can currently be achieved using fMRI in humans. Moreover, fMRI reects neuronal activity only very indirectly via such physiological parameters as blood oxygenation and it is still unclear how accurately these reect neuronal activity and to what detail. On the other hand such modalities as Electroencephalography (EEG) and Magnetoencephalography (MEG) do offer the kind of time resolution that is needed to observe cortical feedbacks. However, to get the feedback information, the MEG and EEG techniques must be enhanced to incorporate connectivity and better spatio-temporal source models. This observation is central to our project.


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