Wolfgang Maass

Technische Universität Graz, Graz, Steiermark, Austria 
computation & theory
"Wolfgang Maass"
Mean distance: 14.5 (cluster 17)
Cross-listing: Computational Biology Tree


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Prashant Joshi grad student TU Graz
Stefan Klampfl grad student TU Graz
Malte J. Rasch grad student 2004-2008 TU Graz
Michael Pfeiffer grad student 2003-2010 TU Graz
Bernhard Nessler grad student 2006-2013 TU Graz


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Rui Ponte Costa collaborator 2011-2013 Edinburgh
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Yan Y, Kappel D, Neumaerker F, et al. (2019) Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. Ieee Transactions On Biomedical Circuits and Systems
Liu C, Bellec G, Vogginger B, et al. (2018) Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype. Frontiers in Neuroscience. 12: 840
Kappel D, Legenstein R, Habenschuss S, et al. (2018) A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. Eneuro. 5
Jonke Z, Legenstein R, Habenschuss S, et al. (2017) Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience
Pecevski D, Maass W. (2016) Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity. Eneuro. 3
Jonke Z, Habenschuss S, Maass W. (2016) Solving Constraint Satisfaction Problems with Networks of Spiking Neurons. Frontiers in Neuroscience. 10: 118
Kappel D, Habenschuss S, Legenstein R, et al. (2015) Network Plasticity as Bayesian Inference. Plos Computational Biology. 11: e1004485
Bill J, Buesing L, Habenschuss S, et al. (2015) Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition. Plos One. 10: e0134356
Janzen S, Maass W. (2015) Towards benevolent sales assistants in retailing scenarios Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9103: 180-193
Legenstein R, Maass W. (2014) Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment. Plos Computational Biology. 10: e1003859
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