Connectionists: Post-doc position in modeling learning in networks of spiking neurons

[Jochen Triesch]

A post-doc position is available in our lab at the Frankfurt Institute  
for Advanced Studies (http://fias.uni-frankfurt.de/). Recent research  
has shown how networks of spiking neurons can solve challenging  
learning problems if endowed with multiple forms of plasticity (see  
references below). Building on this work, we will develop models of  
spiking neuron networks that combine different forms of learning  
including reward-modulated spike-timing-dependent plasticity to solve  
a range of tasks. Of particular interest are the questions how such  
networks can learn to selectively route information (attention,  
communication through coherence) and to temporarily store information  
(working memory).
The project is part of a new, large multi-lab effort to understand  
neuronal coordination, i.e. the spatio-temporal interactions of  
populations of neurons, in the healthy and diseased brain. There will  
be many opportunities to collaborate with leading experimental groups.  
See http://www.neff-ffm.de/de/forschung/ for details (so far only in  
German). Frankfurt has a vibrant neuroscience community with over 50  
experimental and theoretical research groups. Our lab has close ties  
with the Max-Planck Institute for Brain Research (http://www.mpih-frankfurt.mpg.de/ 
) and several collaborations with labs in Europe and the US.
We are looking for a highly qualified individual who has graduated in  
computational neuroscience and has experience with modeling networks  
of spiking neurons and corresponding simulators. Familiarity with high- 
performance-computing environments is a plus. Candidates are required  
to have a strong analytical background and excellent programming  
skills. Good communication skills in English (oral and written) are  
essential.

Application materials should include:
- C.V. (including date of birth, degrees, awards, publications, ...)
- statement of research interests (1-2 pages)
- contact information for 2-3 references

Applications should be sent to:
Ms Gaby Schmitz
Ruth-Moufang-Str. 1
60438 Frankfurt am Main, Germany
Phone: +49 69 798-47614
Fax: +49 69 798-47615
Email: schmitz at fias.uni-frankfurt.de

References:

SORN: a Self-organizing Recurrent Neural Network. A. Lazar, G. Pipa,  
and J. Triesch. Frontiers in Computational Neuroscience, 3(23), doi: 
10.3389/neuro.10.023.2009.
http://www.frontiersin.org/computational_neuroscience/10.3389/neuro.10/023.2009/abstract

Independent Component Analysis in Spiking Neurons. C. Savin, P. Joshi,  
and J. Triesch. PLoS Computational Biology, 6(4), doi:10.1371/  
journal.pcbi.1000757, 2010.
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000757

Reward Dependent Learning in Recurrent Neural Networks - Emergence of  
Working Memory. C. Savin and J. Triesch. Submitted.