Wolfgang Maass

Position:

Company: TUG

Research interest:


External Link:
E-mail: maass@igi.tugraz.at
Phone:
Fax:

Mailing Address:


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Workpackages :

Self-construction theory

Publications :

Büsing L, Bill J, Nessler B, Maass W - Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. (2011), PLoS Computational Biology

Legenstein R, Maass W - Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. (2011), J Neurosci 31(30): 10787-10802

Büsing L, Bill J, Nessler B, Maass W - Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. (2011), PLoS Computational Biology, 7(11): e1002211

Hauser H, Ijspert AJ, Füchslin RM, Pfeifer R, Maass W - Towards a theoretical foundation for morphological computation with compliant bodies (2011), Biological cybernetics, 105(5-6): 355-70

Pecevski D, Büsing L, Maass W - Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons (2011), PLoS Computational Biology 7(12): e1002294

Büsing L, Maass W - A spiking neuron as information bottleneck (2010), Neural Computation 22(8): 1-32 1961-1992

Klampfl S, Maass W - A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction (2010), Neural Computation 22(12): 2979 - 3035

Pfeiffer M, Nessler B, Douglas R, Maass W - Reward-modulated Hebbian learning of decision making (2010), Neural Computation 22: 1399-1444

Klampfl S, Maass W - Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks (2010), NIPS 2009: Advances in Neural Information Processing Systems 22, 2010. MIT Press

Legenstein R, Chase SA, Schwartz AB, Maass W - Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning (2010), NIPS 2009: Advances in Neural Information Processing Systems 22, 2010. MIT Press.

Nessler B, Pfeiffer M, Maass W - STDP enables spiking neurons to detect hidden causes of their inputs (2010), NIPS 2009: Advances in Neural Information Processing Systems 22, 2010. MIT Press.

Häusler S, Schuch K, Maass W - Motif distribution and computational performance of two data-based cortical microcircuit templates. (2009), J Phys

Maass W - Motivation, theory, and applications of Liquid State Machines. (2009), In: B. Cooper, A. Sorbi (eds.): Computability in Context: Computation and Logic in the Real World, Imperial College Press

Buonomano D, Maass W - State-dependent computations: spatiotemporal processing in cortical networks. (2009), Nature Reviews in Neuroscience, 10(2): 113-125

Nessler B, Pfeiffer M, Maass W - Hebbian learning of Bayes optimal decisions (2009), In Proc. NIPS 2008: Advances in Neural Information Processing Systems 22. MIT Press

Legenstein R, Chase SA, Schwartz AB, Maass W - A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task (2009), J Neurosci 30(25): 8400 – 8410

Nikolic D, Haeusler S, Singer W, Maass W - Distributed fading memory for stimulus properties in the primary visual cortex (2009), PLoS Biology, 7(12):1-19

Legenstein R, Pecevski D, Maass W - A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. (2008), PLoS Comput Biol 4(10):1-27

Maass W, Joshi P, Sontag ED - Computational Aspects of Feedback in Neural Circuits (2007), PLOS Comput Biol 3:165

Sussillo D, Toyoizumi T, Maass W - Self-tuning of Neural Circuits through Short-term Synaptic Plasticity (2007), J Neurophysiol in press

Maass W, Natschläger T, Makram H - Real-Time Computing Without Stable States : A New Framework for Neural Computation Based on Perturbations (2002), Neural Comput 14:2531-2560

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