Connectionists: Research Position at INRIA Lorraine, France
Frederic Alexandre
frederic.alexandre at loria.fr
Fri Jan 19 10:39:36 EST 2007
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.
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