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Journal Article
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:
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:
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:
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:
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:
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:
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 .
Habenschuss S, Jonke Z, Maass W. Stochastic Computations in Cortical Microcircuit Models. PLoS Computational Biology [Internet]. 2013 ;9:e1003311. Available from:
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:

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