Publications

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2013
Klampfl S, Maass W. Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP. The Journal of Neuroscience [Internet]. 2013 ;33:11515-11529. Available from: http://www.jneurosci.org/content/33/28/11515.abstract
Rückert EA, Neumann G, Toussaint M, Maass W. Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers in Computational Neuroscience (Special Issue on Modularity in motor control: from muscle synergies to cognitive action representation) [Internet]. 2013 ;6. Available from: http://www.frontiersin.org/computational_neuroscience/10.3389/fncom.2012.00097/abstract
Rückert EA, d'Avella A. Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems. In: Abstracts of Neural Control of Movement Conference (NCM 2013). Abstracts of Neural Control of Movement Conference (NCM 2013). ; 2013. Available from: http://eprints.pascal-network.org/archive/00009898/
Rückert E, d'Avella A. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers in Computational Neuroscience (Special Issue on Modularity in motor control: from muscle synergies to cognitive action representation) [Internet]. 2013 ;7:138. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797962/
Habenschuss S, Jonke Z, Maass W. Stochastic Computations in Cortical Microcircuit Models. PLoS Computational Biology [Internet]. 2013 ;9:e1003311. Available from: http://dx.doi.org/10.1371%2Fjournal.pcbi.1003311
2011
Hauser H, Neumann G, Ijspeert A, Maass W. Biologically inspired kinematic synergies enable linear balance control of a humanoid robot. Biological Cybernetics [Internet]. 2011 ;104:235–249. Available from: http://www.springerlink.com/content/5217485124776363/
Pecevski D, Buesing L, Maass W. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons. PLoS Computational Biology [Internet]. 2011 ;7:e1002294. Available from: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002294
Rückert EA, Neumann G. A study of Morphological Computation by using Probabilistic Inference for Motor Planning. In: 2nd International Conference on Morphological Computation (ICMC2011). 2nd International Conference on Morphological Computation (ICMC2011). Venice, Italy; 2011. pp. 51–53. Available from: http://eprints.pascal-network.org/archive/00008757/01/AICOMorphComp.pdf
Hauser H, Ijspeert A, Füchslin RM, Pfeifer R, Maass W. Towards a Theoretical Foundation for Morphological Computation with Compliant Bodies. Biological Cybernetics [Internet]. 2011 ;105:355-370. Available from: http://www.igi.tugraz.at/psfiles/209.pdf
Neumann G. Variational Inference for Policy Search in Changing Situations. In: Getoor L, Scheffer T Proceedings of the 28th International Conference on Machine Learning (ICML-11). Proceedings of the 28th International Conference on Machine Learning (ICML-11). New York, NY, USA: ACM; 2011. pp. 817–824. Available from: http://www.igi.tugraz.at/gerhard/research/papers/441_icmlNeumann.pdf

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