[Comp-neuro] Postdoctoral positions available in Group for Neural Theory, ENS Paris

Sophie Deneve sophie.deneve at ens.fr
Thu Sep 6 18:47:52 CEST 2012


*THREE POSTDOCTORAL POSITIONS* are available in Sophie Deneve’s team at the
Group Neural Theory, Paris, France (see www.gnt.ens.fr).  The GNT is highly
interactive and dynamic, is situated in central Paris, and is embedded
within the strong Parisian theoretical neuroscience community. The ideal
candidate should have *a PhD with a quantitative background* (ideally in
fields such as machine learning and/or computational neuroscience).


We will investigate information coding and learning in spiking neural
networks, combining theoretical approaches, simulations and analysis of
neurophysiological datasets. Possible projects are described in more
details below.


Starting dates are flexible. The positions are for two years, with net
salaries from 2500 to 2800 euro/month depending on prior experience. We
will also provide generous travel funds. Possibilities exists to get
subsidized housing (especially for families).


Candidates should send a letter of motivation (2 pages max), the contact
information of 2 to 3 referees and their CVs to sophie.deneve at ens.fr *BEFORE
OCTOBER 10, 2012*. Interviews of short-listed candidates will be conducted
in the fall either in Paris, at SFN in New Orleans or by video-conferences.


Description of projects:


Dealing with uncertainties is necessary for the survival of any living
organism. Indeed, recent years have seen the growing application of
probabilistic inference models to perception and action. Excitable neural
structures face similar uncertainties: they receive noisy and ambiguous
inputs and must accumulate evidence over time, combine unreliable cues and
decide among alternative interpretations of the sensory input.
Probabilistic model can thus be used to further our understanding not only
of behavior, but also of the function and dynamics of biological neural
networks.


Our working hypotheses are two-fold. First, we suppose that neural networks
are tuned to estimate sensory or motor variables as reliably as possible.
And second, firing dynamics insure self-consistency, i.e. these estimates
can be extracted by postsynaptic integration of output spike trains. These
two principles entirely constrain the structure, dynamics and plasticity of
the corresponding spiking neural network. In particular, this purely
functional approach captures many aspects of cortical dynamics and sensory
responses (Boerlin and Deneve Plos Comp Bio 2011, Lochman, Ernst and Deneve
J Neurosci 2012, Lochman and Deneve, Curr Opin Neurobiol. 2011).


The projects will consist in


1. Developing and generalizing this framework to explore its implications
for neural coding, dynamics and sensory representations

2. Designing new methods of data analysis able to extract a network’s
function from multi-electrode neural recordings.

3. Applying this approach to neural datasets (multielectrode recordings –
optical imaging data) from sensory and motor areas.
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