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 [ à ] fias.uni-frankfurt.de
References:
SORN: a Self-organizing Recurrent Neural Network. A. Lazar, G. Pipa, and J. Triesch. Frontiers in Computational Neuroscience,
Independent Component Analysis in Spiking Neurons. C. Savin, P. Joshi,
and J. Triesch. PLoS Computational Biology,
Reward Dependent Learning in Recurrent Neural Networks - Emergence of
Working Memory. C. Savin and J. Triesch. Submitted.
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 [ à ] fias.uni-frankfurt.de
References:
SORN: a Self-organizing Recurrent Neural Network. A. Lazar, G. Pipa, and J. Triesch. Frontiers in Computational Neuroscience,
Independent Component Analysis in Spiking Neurons. C. Savin, P. Joshi,
and J. Triesch. PLoS Computational Biology,
Reward Dependent Learning in Recurrent Neural Networks - Emergence of
Working Memory. C. Savin and J. Triesch. Submitted.