Connectionists: Research Position at INRIA Lorraine, France

[Frederic Alexandre]

The INRIA (the french national institute for research in computer 
science and control) will be hiring five full-time (3 junior, 2 senior) 
research scientists at its INRIA Lorraine research unit in 2007. These 
are tenured positions. In this context, the Cortex group 
(http://cortex.loria.fr) is looking for candidates having a background 
in Computational Neuroscience and Machine Learning (cf. Scientific 
Context below).

The application deadline is February 15th. Nancy is a mid-sized city 
located in eastern France, close to Belgium, Germany and Luxembourg. The 
INRIA research unit is located on the university campus. In case of 
interest please contact Frederic.Alexandre at loria.fr

For more information about the application procedure please check this 
page:
http://www.inria.fr/travailler/opportunites/chercheurs.en.html

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Scientific Context:

A recent and very strong trend in computer science is to develop models 
and algorithms in interaction with biology and medical science. Today, 
computing resources allow to deal with the huge amount of data and the 
complexity of biological phenomena. Biologists now have great 
expectations from computer science, through both data mining and 
computational modeling. Several multidisciplinary research labs have 
been thus created, which illustrates the increasing significance of this 
collaboration between biologists and computer scientists.

Neuroscience is a very active field of research, where the 
multidisciplinary aspect is prominent. Even if they still often work 
separately, physiologists, neuropsychologists, computer scientists, 
physicists, anatomists bring together different means to study the wide 
complexity of the brain.

Among these lines of research, computational neuroscience more 
specifically aims at using computational principles to better understand 
the brain. With regards to the progress that has been made in 
mathematics, computer science, anatomy, neuro-biology, physiology, 
imaging, and behavioral science, computational neuroscience provides a 
new and unique interdisciplinary cooperation framework between 
researchers of these scientific domains. It combines experiments with 
data analysis and computer simulation on the basis of strong theoretical 
concepts, and it aims at modelling, simulating and also understanding 
mechanisms that underlie neural processes such as perception, action, 
learning, memory or cognition. Two fields of research are generally 
considered:

   1. The first one corresponds to understanding the adaptative and
      distributed computation mode that is used by neural systems. This
      requires a computational study of the properties of these
      mechanisms, such as: emergence, asynchronism, temporality,
      genericity, modularity, robustness and adaptability. Two levels of
      description are studied in the field.
          * Spiking models focus on very specific data and brain
            functions, at the neuronal level, and organize computation
            around fundamental neuronal events: spikes.
          * Behavioral models are elaborated from integrated data and
            multimodal functionalities and wish to understand more
            complex functions, described in terms of information flow,
            at the level of populations of neurons and more global
            neuronal activity.
   2. The second field of research deals with experimental data (from
      cellular recordings to behavioral analysis) which are today
      available in huge quantities, more and more precise but also more
      and more complex to analyze. Such data can be exploited to extract
      new knowledge directly from living neuronal structures and to feed
      computational models with real information. Data mining approaches
      and other signal interpretation techniques are reconsidered and
      adapted to the specific nature of such data, i.e. temporal, highly
      multidimensional, noisy, multiscale and often sparse data.

Results obtained from these researches are twofold:

   1. Building and assessing models, as well as mining experimental data
      can lead to predictions and other hypotheses that can orient
      further research in experimental neuroscience. Today,
      computational models are able to offer new approaches of the
      complex relations between the structural and the functional level
      of the brain.
   2. Inspiration from these elementary biological mechanisms can bring
      new and powerful algorithms and computation paradigms to computer
      science. Similarly, the fundamental duality of neurons seen as
      processing units and elementary data storage is a major source of
      inspiration to adapt processing architecture to algorithms and to
      embed neuronal processing in fine grain distributed processing.