Research Topics
 Wolfgang MaassSummaryAffiliation: Graz University of Technology Country: Austria Publications
 Collaborators

Detail Information
Publications
 Computational aspects of feedback in neural circuitsWolfgang 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...  Fading memory and kernel properties of generic cortical microcircuit modelsWolfgang Maass
Institute for Theoretical Computer Science, Technische Universitaet Graz, Austria
J Physiol Paris 98:31530. 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...  A model for the interaction of oscillations and pattern generation with realtime computing in generic neural microcircuit modelsAlexander Kaske
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
Neural Netw 19:6009. 2006....  Bayesian computation emerges in generic cortical microcircuits through spiketimingdependent plasticityBernhard 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...  A rewardmodulated hebbian learning rule can explain experimentally observed network reorganization in a brain control taskRobert Legenstein
Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
J Neurosci 30:840010. 2010..Furthermore, when the model parameters are matched to data recorded during the braincomputer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments...  Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neuronsDejan Pecevski
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
PLoS Comput Biol 7:e1002294. 2011....  On the classification capability of signconstrained perceptronsRobert Legenstein
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
Neural Comput 20:288309. 2008..Our theoretical analysis is complemented by computer simulations, which demonstrate in particular that sparse input patterns improve the classification capability of signconstrained perceptrons...  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 V1Malte J Rasch
1Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
J Neurophysiol 105:75778. 2011....  Emergence of optimal decoding of population codes through STDPStefan Habenschuss
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
Neural Comput 25:1371407. 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...  Edge of chaos and prediction of computational performance for neural circuit modelsRobert Legenstein
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
Neural Netw 20:32334. 2007....  Rewardmodulated Hebbian learning of decision makingMichael Pfeiffer
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
Neural Comput 22:1399444. 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...  What can a neuron learn with spiketimingdependent plasticity?Robert Legenstein
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
Neural Comput 17:233782. 2005....  A learning theory for rewardmodulated spiketimingdependent plasticity with application to biofeedbackRobert Legenstein
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
PLoS Comput Biol 4:e1000180. 2008..In addition our model demonstrates that rewardmodulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics...  A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neuronsStefan Klampfl
Institute for Theoretical Computer Science, Graz Univ of Technology, Austria
J Neurophysiol 108:136680. 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...  Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neuronsLars 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...  Movement generation with circuits of spiking neuronsPrashant Joshi
Institute for Theoretical Computer Science, Technische Universitat Graz, A 8010 Graz, Austria
Neural Comput 17:171538. 2005....  A statistical analysis of informationprocessing properties of laminaspecific cortical microcircuit modelsStefan Haeusler
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010 Graz, Austria
Cereb Cortex 17:14962. 2007..We conclude that computer simulations of detailed laminaspecific cortical microcircuit models provide new insight into computational consequences of anatomical and physiological data...  Learning Probabilistic Inference through SpikeTimingDependent PlasticityDejan Pecevski
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
Eneuro 3:. 2016..Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. ..  Input prediction and autonomous movement analysis in recurrent circuits of spiking neuronsRobert Legenstein
Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria
Rev Neurosci 14:519. 2003....  Stochastic computations in cortical microcircuit modelsStefan Habenschuss
Graz University of Technology, Institute for Theoretical Computer Science, Graz, Austria
PLoS Comput Biol 9:e1003311. 2013....  Branchspecific plasticity enables selforganization of nonlinear computation in single neuronsRobert Legenstein
Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
J Neurosci 31:10787802. 2011..Hence, our results suggest that nonlinear neural computation may selforganize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms...  Realtime computing without stable states: a new framework for neural computation based on perturbationsWolfgang Maass
Institute for Theoretical Computer Science, Technische Universitat Graz, A 8010 Graz, Austria
Neural Comput 14:253160. 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...  Spiking neurons can learn to solve information bottleneck problems and extract independent componentsStefan Klampfl
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
Neural Comput 21:91159. 2009..We derive suitable learning rules, which extend the wellknown BCM rule, from abstract information optimization principles. These rules will simultaneously keep the firing rate of the neuron within a biologically realistic range...  Distributed Bayesian Computation and SelfOrganized Learning in Sheets of Spiking Neurons with Local Lateral InhibitionJohannes Bill
Institute for Theoretical Computer Science, TU Graz, Graz, Austria
PLoS ONE 10:e0134356. 2015..Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. ..  Emergence of dynamic memory traces in cortical microcircuit models through STDPStefan Klampfl
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
J Neurosci 33:1151529. 2013....  Motif distribution, dynamical properties, and computational performance of two databased cortical microcircuit templatesStefan Haeusler
Institute for Theoretical Computer Science, Graz University of Technology, Austria
J Physiol Paris 103:7387. 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...  Probing real sensory worlds of receivers with unsupervised clusteringMichael 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...  A spiking neuron as information bottleneckLars Buesing
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
Neural Comput 22:196192. 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...  Dynamics of information and emergent computation in generic neural microcircuit modelsThomas Natschläger
Institute for Theoretical Computer Science, Technische Universitaet Graz, A 8010, Austria
Neural Netw 18:13018. 2005....  Biologically inspired kinematic synergies enable linear balance control of a humanoid robotHelmut Hauser
Institute for Theoretical Computer Science, Graz University of Technology, 8010 Graz, Austria
Biol Cybern 104:23549. 2011....  Network Plasticity as Bayesian InferenceDavid Kappel
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
PLoS Comput Biol 11:e1004485. 2015..The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling. ..  A theoretical basis for emergent pattern discrimination in neural systems through slow feature extractionStefan Klampfl
Institute for Theoretical Computer Science, Graz University of Technology, A 8010 Graz, Austria
Neural Comput 22:29793035. 2010..It also provides a theoretical basis for understanding recent experimental results on the emergence of view and positioninvariant classification of visual objects in inferior temporal cortex...  Inferring spike trains from local field potentialsMalte J Rasch
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
J Neurophysiol 99:146176. 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...  Solving Constraint Satisfaction Problems with Networks of Spiking NeuronsZeno Jonke
Faculty of Computer Science and Biomedical Engineering, Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria
Front Neurosci 10:118. 2016..Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. ..  Synapses as dynamic memory buffersWolfgang Maass
Institute for Theoretical Computer Science, Technische Universitat Graz, Austria
Neural Netw 15:15561. 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...  Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environmentRobert Legenstein
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
PLoS Comput Biol 10:e1003859. 2014....  STDP installs in WinnerTakeAll circuits an online approximation to hidden Markov model learningDavid 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. ..  Emergence of complex computational structures from chaotic neural networks through rewardmodulated Hebbian learningGregor M Hoerzer
Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
Cereb Cortex 24:67790. 2014..We show that generic networks of neurons can learn numerous biologically relevant computations through trial and error. ..  On the computational power of threshold circuits with sparse activityKei Uchizawa
Graduate School of Information Sciences, Tohoku University, Sendai 980 8579, Japan
Neural Comput 18:29943008. 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...  Selftuning of neural circuits through shortterm synaptic plasticityDavid Sussillo
Center for Theoretical Neuroscience in the Center for Neurobiology and Behavior, Columbia University, New York, New York, USA
J Neurophysiol 97:407995. 2007..The contribution of synaptic dynamics to this stability can be predicted on the basis of general principles from control theory...  A learning rule for very simple universal approximators consisting of a single layer of perceptronsPeter Auer
Chair for Information Technology, University of Leoben, Franz Josef Strasse 18, A 8700 Leoben, Austria
Neural Netw 21:78695. 2008..In Proceedings of the 2004 IEEE international joint conference on neural networks (pp. 967972): Vol. 2] that one can also prove quite satisfactory bounds for the generalization error of this new learning rule...  Coding and learning of behavioral sequencesOfer Melamed
Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
Trends Neurosci 27:114; discussion 145. 2004