Wolfgang Maass

Summary

Affiliation: Graz University of Technology
Country: Austria

Publications

  1. pmc A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
    Robert Legenstein
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 4:e1000180. 2008
  2. pmc Computational aspects of feedback in neural circuits
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria
    PLoS Comput Biol 3:e165. 2007
  3. ncbi request reprint Fading memory and kernel properties of generic cortical microcircuit models
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria
    J Physiol Paris 98:315-30. 2004
  4. ncbi request reprint A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models
    Alexander Kaske
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Netw 19:600-9. 2006
  5. pmc Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
    Bernhard Nessler
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 9:e1003037. 2013
  6. pmc A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
    Robert Legenstein
    Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
    J Neurosci 30:8400-10. 2010
  7. ncbi request reprint What can a neuron learn with spike-timing-dependent plasticity?
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Comput 17:2337-82. 2005
  8. pmc Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
    Dejan Pecevski
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 7:e1002294. 2011
  9. ncbi request reprint On the classification capability of sign-constrained perceptrons
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Comput 20:288-309. 2008
  10. pmc Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1
    Malte J Rasch
    1Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    J Neurophysiol 105:757-78. 2011

Detail Information

Publications37

  1. pmc A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
    Robert Legenstein
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 4:e1000180. 2008
    ..In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics...
  2. pmc Computational aspects of feedback in neural circuits
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria
    PLoS Comput Biol 3:e165. 2007
    ..Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks...
  3. ncbi request reprint Fading memory and kernel properties of generic cortical microcircuit models
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria
    J Physiol Paris 98:315-30. 2004
    ..This article also provides detailed data on the fading memory property of generic neural microcircuit models, and a quick review of other new results on the computational power of such circuits of spiking neurons...
  4. ncbi request reprint A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models
    Alexander Kaske
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Netw 19:600-9. 2006
    ....
  5. pmc Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
    Bernhard Nessler
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 9:e1003037. 2013
    ..Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex...
  6. pmc A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
    Robert Legenstein
    Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
    J Neurosci 30:8400-10. 2010
    ..Furthermore, when the model parameters are matched to data recorded during the brain-computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments...
  7. ncbi request reprint What can a neuron learn with spike-timing-dependent plasticity?
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Comput 17:2337-82. 2005
    ....
  8. pmc Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
    Dejan Pecevski
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 7:e1002294. 2011
    ....
  9. ncbi request reprint On the classification capability of sign-constrained perceptrons
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Comput 20:288-309. 2008
    ..Our theoretical analysis is complemented by computer simulations, which demonstrate in particular that sparse input patterns improve the classification capability of sign-constrained perceptrons...
  10. pmc Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1
    Malte J Rasch
    1Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    J Neurophysiol 105:757-78. 2011
    ....
  11. doi request reprint Emergence of optimal decoding of population codes through STDP
    Stefan Habenschuss
    Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
    Neural Comput 25:1371-407. 2013
    ..Furthermore, we show that this learning process is very stable and automatically adjusts weights to changes in the number of readout neurons, the tuning functions of sensory neurons, and the statistics of external stimuli...
  12. doi request reprint Reward-modulated Hebbian learning of decision making
    Michael Pfeiffer
    Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
    Neural Comput 22:1399-444. 2010
    ..Hence our proposed framework for fast learning of decisions also provides interesting new hypotheses regarding neural nodes and computational goals of cortical areas that provide input to the final decision stage...
  13. ncbi request reprint Edge of chaos and prediction of computational performance for neural circuit models
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Neural Netw 20:323-34. 2007
    ....
  14. ncbi request reprint Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons
    Robert Legenstein
    Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria
    Rev Neurosci 14:5-19. 2003
    ....
  15. pmc A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons
    Stefan Klampfl
    Institute for Theoretical Computer Science, Graz Univ of Technology, Austria
    J Neurophysiol 108:1366-80. 2012
    ..Furthermore, our analysis of these experimental data shows that methods from information theory and the application of standard machine learning methods for extracting specific information yield quite similar results...
  16. pmc Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons
    Lars Buesing
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 7:e1002211. 2011
    ..This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons...
  17. ncbi request reprint A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models
    Stefan Haeusler
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
    Cereb Cortex 17:149-62. 2007
    ..We conclude that computer simulations of detailed lamina-specific cortical microcircuit models provide new insight into computational consequences of anatomical and physiological data...
  18. ncbi request reprint Movement generation with circuits of spiking neurons
    Prashant Joshi
    Institute for Theoretical Computer Science, Technische Universitat Graz, A 8010 Graz, Austria
    Neural Comput 17:1715-38. 2005
    ....
  19. ncbi request reprint Real-time computing without stable states: a new framework for neural computation based on perturbations
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitat Graz, A 8010 Graz, Austria
    Neural Comput 14:2531-60. 2002
    ..Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology...
  20. doi request reprint Branch-specific plasticity enables self-organization of nonlinear computation in single neurons
    Robert Legenstein
    Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
    J Neurosci 31:10787-802. 2011
    ..Hence, our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms...
  21. doi request reprint Spiking neurons can learn to solve information bottleneck problems and extract independent components
    Stefan Klampfl
    Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
    Neural Comput 21:911-59. 2009
    ..We derive suitable learning rules, which extend the well-known BCM rule, from abstract information optimization principles. These rules will simultaneously keep the firing rate of the neuron within a biologically realistic range...
  22. doi request reprint Emergence of dynamic memory traces in cortical microcircuit models through STDP
    Stefan Klampfl
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    J Neurosci 33:11515-29. 2013
    ....
  23. doi request reprint Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates
    Stefan Haeusler
    Institute for Theoretical Computer Science, Graz University of Technology, Austria
    J Physiol Paris 103:73-87. 2009
    ..We also show that their computational performance is correlated with specific statistical properties of the circuit dynamics that is induced by a particular distribution of degrees of nodes...
  24. pmc Probing real sensory worlds of receivers with unsupervised clustering
    Michael Pfeiffer
    Institute for Theoretical Computer Science, TU Graz, Graz, Austria
    PLoS ONE 7:e37354. 2012
    ..This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands...
  25. doi request reprint A spiking neuron as information bottleneck
    Lars Buesing
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    Neural Comput 22:1961-92. 2010
    ..Furthermore, we show that the proposed IB learning rule allows spiking neurons to learn a predictive code, that is, to extract those parts of their input that are predictive for future input...
  26. ncbi request reprint Dynamics of information and emergent computation in generic neural microcircuit models
    Thomas Natschl├Ąger
    Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010, Austria
    Neural Netw 18:1301-8. 2005
    ....
  27. doi request reprint Biologically inspired kinematic synergies enable linear balance control of a humanoid robot
    Helmut Hauser
    Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
    Biol Cybern 104:235-49. 2011
    ....
  28. doi request reprint A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction
    Stefan Klampfl
    Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
    Neural Comput 22:2979-3035. 2010
    ..It also provides a theoretical basis for understanding recent experimental results on the emergence of view- and position-invariant classification of visual objects in inferior temporal cortex...
  29. ncbi request reprint Inferring spike trains from local field potentials
    Malte J Rasch
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    J Neurophysiol 99:1461-76. 2008
    ..In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity...
  30. ncbi request reprint Synapses as dynamic memory buffers
    Wolfgang Maass
    Institute for Theoretical Computer Science, Technische Universitat Graz, Austria
    Neural Netw 15:155-61. 2002
    ..We show that the relatively large number of synaptic release sites that make up a GABAergic synaptic connection makes these connections suitable for such complex information transmission processes...
  31. doi request reprint Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning
    Gregor M Hoerzer
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    Cereb Cortex 24:677-90. 2014
    ..We show that generic networks of neurons can learn numerous biologically relevant computations through trial and error. ..
  32. doi request reprint STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning
    David Kappel
    Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
    PLoS Comput Biol 10:e1003511. 2014
    ..We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. ..
  33. pmc Stochastic computations in cortical microcircuit models
    Stefan Habenschuss
    Graz University of Technology, Institute for Theoretical Computer Science, Graz, Austria
    PLoS Comput Biol 9:e1003311. 2013
    ....
  34. doi request reprint A learning rule for very simple universal approximators consisting of a single layer of perceptrons
    Peter Auer
    Chair for Information Technology, University of Leoben, Franz Josef Strasse 18, A 8700 Leoben, Austria
    Neural Netw 21:786-95. 2008
    ..In Proceedings of the 2004 IEEE international joint conference on neural networks (pp. 967-972): Vol. 2] that one can also prove quite satisfactory bounds for the generalization error of this new learning rule...
  35. ncbi request reprint Self-tuning of neural circuits through short-term synaptic plasticity
    David Sussillo
    Center for Theoretical Neuroscience in the Center for Neurobiology and Behavior, Columbia University, New York, New York, USA
    J Neurophysiol 97:4079-95. 2007
    ..The contribution of synaptic dynamics to this stability can be predicted on the basis of general principles from control theory...
  36. ncbi request reprint On the computational power of threshold circuits with sparse activity
    Kei Uchizawa
    Graduate School of Information Sciences, Tohoku University, Sendai 980 8579, Japan
    Neural Comput 18:2994-3008. 2006
    ..In particular, different pathways are activated in these circuits for different classes of inputs. This letter shows that such circuits with sparse activity have a surprisingly large computational power...
  37. ncbi request reprint Coding and learning of behavioral sequences
    Ofer Melamed
    Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
    Trends Neurosci 27:11-4; discussion 14-5. 2004