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University of Graz
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
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
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
Hoerzer GM, Legenstein R, Maass W. Emergence of Complex Computational Structures From Chaotic Neural Networks Through Reward-Modulated Hebbian Learning. Cerebral Cortex [Internet]. 2012 . Available from: http://cercor.oxfordjournals.org/content/early/2012/11/09/cercor.bhs348.abstract
Hauser H, Ijspeert A, Füchslin RM, Pfeifer R, Maass W. The role of feedback in morphological computation with compliant bodies. Biological Cybernetics [Internet]. 2012 ;106:595-613. Available from: http://dx.doi.org/10.1007/s00422-012-0516-4
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
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
Kappel D, Nessler B, Maass W. STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. PLoS Computational Biology. 2014 .
WP4-Adaptive-Modules
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

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