Cognitive Behavior of Humans, Animals, and Machines:

Situation Model Perspectives

Oktober 2019 – Juli 2020

Leitung: Werner Schneider (Bielefeld, GER), Helge Ritter (Bielefeld, GER)

Thomas Martinetz

Associate Fellow

Foto Institute for Neuro- and Biocomputing,
University of Lübeck, Germany
E-Mail: martinet@inb.uni-luebeck.de


Thomas Martinetz is full professor of computer science and the director of the Institute for Neuro- and Biocomputing at the University of Lübeck. He studied Physics at the TU München and obtained his doctoral degree in Theoretical Biophysics at the Beckman Institute for Advanced Science and Technology of the University of Illinois at Urbana-Champaign. From 1991 to 1996 he led the project "Neural Networks for automation control" at the Corporate Research Laboratories of the Siemens AG in Munich. From 1996 to 1999 he was Professor for Neural Computation at the Ruhr-University of Bochum and head of the Center for Neuroinformatics.

Current Main Research Interests

Thomas Martinetz' research aims at applying biological information processing principles to create machine learning systems in the domain of image processing and pattern recognition. This also involves modelling neural networks and the immune system in the context of memory formation, e.g., during sleep.

Five selected publications with particular relevance to the Research Group
  • Ngo, H. V. V., Miedema, A., Faude, I., Martinetz, T., Mölle, M., & Born, J. (2015). Driving sleep slow oscillations by auditory closed-loop stimulation—a self-limiting process. Journal of Neuroscience, 35, 6630-6638.
  • Weigenand, A., Costa, M. S., Ngo, H. V. V., Claussen, J. C., & Martinetz, T. (2014). Characterization of K-complexes and slow wave activity in a neural mass model. PLoS computational biology, 10, e1003923.
  • Ngo, H. V. V., Martinetz, T., Born, J., & Mölle, M. (2013). Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron, 78, 545-553.
  • Labusch, K., Barth, E., & Martinetz, T. (2009). Sparse coding neural gas: Learning of overcomplete data representations. Neurocomputing, 72, 1547-1555.
  • K Labusch, E Barth, T Martinetz (2008) Simple method for high-performance digit recognition based on sparse coding Labusch, K., Barth, E., & Martinetz, T. (2008). Simple method for high-performance digit recognition based on sparse coding. Neural Networks, IEEE Transactions on 19 (11), 1985-1989.