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

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@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.