• 8010

    Inffeldgasse 16b Graz

    Austria

Research Output 1983 2018

2018

A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning

Kappel, D., Legenstein, R., Habenschuss, S., Hsieh, M. & Maass, W., 1 Mar 2018, In : eNeuro. 5, 2, 27 p., e0301-17.2018.

Research output: Contribution to journalArticleResearchpeer-review

Connectome
Reward
Spine
Learning
Synapses

Bioinspired approach to modeling retinal ganglion cells using system identification techniques

Vance, P. J., Das, G. P., Kerr, D., Coleman, S. A., McGinnity, T. M., Gollisch, T. & Liu, J. K., 1 May 2018, In : IEEE Transactions on Neural Networks and Learning Systems. 29, 5, p. 1796-1808 13 p.

Research output: Contribution to journalArticleResearchpeer-review

Identification (control systems)
Computer vision
Biophysics
Biological systems
Processing

Camera-based vehicle velocity estimation from monocular video

Kampelmühler, M. D., Müller, M. G. & Feichtenhofer, C., 5 Feb 2018, Computer Vision Winter Workshop (CVWW), 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All

Yu, Z., Guo, S., Deng, F., Yan, Q., Huang, K., Liu, J. K. & Chen, F., 3 Oct 2018, In : IEEE Transactions on Cybernetics.

Research output: Contribution to journalArticleResearchpeer-review

Hidden Markov models
Neural networks
Neurons
Networks (circuits)
Brain

Implementation of Bayesian Inference in Distributed Neural Networks

Yu, Z., Huang, T. & Liu, J. K., 6 Jun 2018, Proceedings - 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2018. Institute of Electrical and Electronics Engineers, p. 666-673 8 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Neural networks
Brain
Sampling
Importance sampling
Bayesian networks

Long short-term memory and learning-to-learn in networks of spiking neurons

Bellec, G. E. F., Salaj, D., Subramoney, A., Legenstein, R. & Maass, W., 2018, Advances in Neural Information Processing Systems: NeurIPS.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Long Term Memory and the Densest K-Subgraph Problem

Legenstein, R., Maass, W., Papapdimitriou, C. H. & Vempala, S. S., 2018, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, Vol. 94. p. 57:1–57:15 57. (LIPIcs-Leibniz International Proceedings in Informatics ).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Data storage equipment
Graph theory
Plasticity
Experiments

Maximal Two-Guard Walks in Polygons

Aurenhammer, F., Steinkogler, M. & Klein, R., 2018.

Research output: Contribution to conferencePaperResearch

Walk
Polygon
Vertex of a graph

Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

Liu, C., Bellec, G. E. F., Vogginger, B., Kappel, D., Partzsch, J., Neumärker, F., Höppner, S., Maass, W., Furber, S. B., Legenstein, R. & Mayr, C. G., 19 Nov 2018, In : Frontiers in neuroscience. 12, 840, 15 p., 840.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
Learning
Datasets

Mitered offsets and straight skeletons for circular arc polygons

Weiß, B., Jüttler, B. & Aurenhammer, F., 2018.

Research output: Contribution to conferencePaperResearch

Decomposition

On merging straight skeletons

Aurenhammer, F. & Steinkogler, M., 2018.

Research output: Contribution to conferencePaperResearch

Motorcycles
Merging
Decomposition

Partially walking a polygon

Aurenhammer, F., Steinkogler, M. & Klein, R., 2018.

Research output: Contribution to conferencePaperResearch

Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons

Ananri, N., Daskalakis, C., Maass, W., Papadimitriou, C. H., Saberi, A. & Vempala, S., 2018, Advances in Neural Information Processing Systems 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2017

A model for the formation of associations between memory items in the brain

Pokorny, C., Ison, M., Legenstein, R. & Maass, W., 2017.

Research output: Contribution to conferencePosterResearch

A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition

Legenstein, R., Jonke, Z., Habenschuss, S. & Maass, W., 17 Jul 2017, In : arXiv.org e-Print archive. p. 1-27 24 p.

Research output: Contribution to journalArticleResearch

Plasticity
Blind source separation
Electric power distribution
Neurons
Statistical Models

Associations between memory traces emerge in a generic neural circuit model through STDP

Pokorny, C., Ison, M., Rao, A., Legenstein, R., Papadimitriou, C. H. & Maass, W., 14 Sep 2017, In : bioRxiv - the Preprint Server for Biology. p. 1-36 36 p.

Research output: Contribution to journalArticleResearch

Neurons
Data storage equipment
Networks (circuits)
Plasticity
Brain

Computing straight skeletons for arc polygons

Aurenhammer, F., Jüttler, B. & Weiß, B., 2017.

Research output: Contribution to conferenceAbstractResearchpeer-review

Decomposition
Splines

Deep Rewiring: Training very sparse deep networks

Bellec, G., Kappel, D., Maass, W. & Legenstein, R., 14 Nov 2017, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearch

File
Neural networks
Hardware
Recurrent neural networks
Feedforward neural networks
Sampling

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs

Jonke, Z., Legenstein, R., Habenschuss, S. & Maass, W., 30 Aug 2017, In : The journal of neuroscience. 37, 35, p. 8511– 8523 24 p.

Research output: Contribution to journalArticleResearchpeer-review

Pyramidal Cells
Parvalbumins
Neurons
Population

Long-term evolution of neural responses in the barrel cortex during learning of a bilateral vibrotactile two-alternative forced choice task

Škreb, V. & Pokorny, C., 2017, The Impact of Sensory-guided Behavior of Neuronal Processing in the Mouse Barrel Cortex and a Novel Two-alternative Forced Choice Paradigm in Head-fixed Mice and Rats: Doctoral Dissertation. Škreb, V. (ed.). p. 54-87

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

Schmitt, S., Klähn, J., Bellec, G. E. F., Grübl, A., Maurice, G., Hartl, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S., Karasenko, V., Kleider, M., Koke, C., Kononov, A., Mauch, C., Müller, E., Müller, P., Partzsch, J., Petrovici, M., Schiefer, S. & 9 othersScholze, S., Thanasoulis, V., Vogginger, B., Legenstein, R., Maass, W., Mayr, C., Schüffny, R., Schemmel, J. & Meier, K., 17 Mar 2017, In : arXiv.org e-Print archive. arXiv:1703.01909, 8 p.

Research output: Contribution to journalArticleResearch

File
Hardware
Substrates
Backpropagation
Energy utilization
Neural networks

Pattern representation and recognition with accelerated analog neuromorphic systems

Petrovici, M. A., Schmitt, S., Klähn, J., Stöckel, D., Schroeder, A., Bellec, G., Bill, J., Breitwieser, O., Bytschok, I., Grübl, A., Güttler, M., Hartel, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S., Karasenko, V., Kleider, M., Koke, C., Kononov, A. & 15 othersMauch, C., Müller, P., Partzsch, J., Pfeil, T., Schiefer, S., Scholze, S., Subramoney, A., Thanasoulis, V., Vogginger, B., Legenstein, R., Maass, W., Schüffny, R., Mayr, C., Schemmel, J. & Meier, K., 17 Mar 2017, In : arXiv.org e-Print archive. preprint arXiv:1703.06043, 4 p.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Hardware
Network components
Neurology
Dynamical systems
Neural networks

Reward-based stochastic self-configuration of neural circuits

Kappel, D., Legenstein, R., Habenschuss, S., Hsieh, M. & Maass, W., 2017, In : arXiv.org e-Print archive. arXiv preprint arXiv:1704.04238, 32 p.

Research output: Contribution to journalArticleResearchpeer-review

Plasticity
Networks (circuits)
Fokker Planck equation
Random processes
Sampling

Scaling up liquid state machines to predict over address events from dynamic vision sensors

Kaiser, J., Stal, R., Subramoney, A., Roennau, A. & Dillmann, R., 1 Sep 2017, In : Bioinspiration & Biomimetics. 12, 5

Research output: Contribution to journalArticleResearchpeer-review

Robotics
Neurosciences
Sensors
Liquids
Silicon

Supervised Learning Algorithms for Spiking Neuromorphic Hardware

Limbacher, T., 22 Feb 2017, 75 p.

Research output: ThesisMaster's ThesisResearch

Supervised learning
Learning algorithms
Neural networks
Hardware
Feedforward neural networks

Variable Binding Through Assemblies in Spiking Neural Networks

Müller, M. G., 2017

Research output: ThesisMaster's ThesisResearch

Voronoi diagrams for parallel halflines and line segments in space

Aurenhammer, F., Jüttler, B. & Paulini, G., 2017, LIPIcs-Leibniz International Proceedings in Informatics. Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH, Vol. 92.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2016

Assembly pointers for variable binding in networks of spiking neurons

Legenstein, R., Papadimitriou, C. H., Vempala, S. & Maass, W., 11 Nov 2016, In : arXiv.org e-Print archive. preprint arXiv:1611.03698

Research output: Contribution to journalArticleResearchpeer-review

File
Neurons
Brain
Information retrieval
Copying
Fillers

Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming

Taraghi, B., Saranti, A., Legenstein, R. & Ebner, M., 26 Apr 2016, p. 449-453.

Research output: Contribution to conferencePaperResearchpeer-review

CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling

Yu, Z., Kappel, D., Legenstein, R., Song, S., Chen, F. & Maass, W., 1 Jun 2016, In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearchpeer-review

Network Optimization
Reward
Activation
Neural Networks
Plasticity

Contributing to the early identification of neurodevelopmental disorders: The retrospective analysis of pre-linguistic vocalisations in home video material

Pokorny, F. B., Schuller, B. W., Peharz, R., Pernkopf, F., Bartl-Pokorny, K. D., Einspieler, C. & Marschik, P. B., 2016, Congreso Internacional Psicologia Clinica.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Energy-efficient neural network chips approach human recognition capabilities

Maass, W., 2016, In : Proceedings of the National Academy of Sciences of the United States of America. 113, 40, p. doi/10.1073/pnas.1614109113

Research output: Contribution to journalArticleResearchpeer-review

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity

Pecevski, D. & Maass, W., 16 Jul 2016, In : eNeuro. 3, 2

Research output: Contribution to journalArticleResearchpeer-review

Decision Making
Learning
Neurons
Pyramidal Cells
Brain

Searching for principles of brain computation

Maass, W., 2016, In : Current Opinion in Behavioral Sciences . 11, Special Iss. on Computational Modelling, p. 81-92

Research output: Contribution to journalArticleResearchpeer-review

Solving constraint satisfaction problems with networks of spiking neurons

Jonke, Z., Habenschuss, S. & Maass, W., 2016, In : Frontiers in neuroscience.

Research output: Contribution to journalArticleResearchpeer-review

Neurons
Noise
Brain

Straight skeletons and mitered offsets of nonconvex polytopes

Aurenhammer, F. & Walzl, G. C., 8 Aug 2016, In : Discrete & computational geometry. 56, 3, p. 743-801

Research output: Contribution to journalArticleResearchpeer-review

File
Polytopes
Skeleton
Straight
Decomposition
Polytope

The role of transient target stimuli in a steady-state somatosensory evoked potential-based brain-computer interface setup

Pokorny, C., Breitwieser, C. & Müller-Putz, G., 7 Apr 2016, In : Frontiers in neuroscience. 10, 152

Research output: Contribution to journalArticleResearchpeer-review

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

Serb, A., Bill, J., Khiat, A., Berdan, R., Legenstein, R. & Prodromakis, T., 2016, In : Nature Communications . 7, 12611.

Research output: Contribution to journalArticleResearchpeer-review

synapses
Unsupervised learning
Synapses
Oxides
learning

Voronoi diagrams for parallel halflines in 3D

Aurenhammer, F., Paulini, G. & Jüttler, B., 30 Mar 2016, Proceedings of the 32nd European Workshop on Computational Geometry EuroCG'2016.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Costs
2015

3-Colorability of Pseudo-Triangulations

Aichholzer, O., Aurenhammer, F., Hackl, T., Pilz, A., Vogtenhuber, B. & Huemer, C., 2015, In : International Journal of Computational Geometry and Applications. p. 283-298

Research output: Contribution to journalArticleResearchpeer-review

Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition

Bill, J., Büsing, L. H., Habenschuss, S., Nessler, B., Maass, W. & Legenstein, R., 2015, In : PLoS ONE. 10, 8, p. e0134356-e0134356

Research output: Contribution to journalArticleResearchpeer-review

Neurons
Plasticity
learning
Probability Theory
neurons

Evaluation of Healthy EEG Responses for Spelling through Listener-Assisted Scanning

Horki, P., Klobassa, D. S., Pokorny, C. & Müller-Putz, G., 2015, In : IEEE Journal of Biomedical and Health Informatics. 19, 1, p. 29-36

Research output: Contribution to journalArticleResearchpeer-review

Nanoscale connections for brain-like circuits

Legenstein, R., 2015, In : Nature (London). 521, p. 37-38

Research output: Contribution to journalArticleResearchpeer-review

Network plasticity as Bayesian inference

Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W., 2015, In : PLoS computational biology. 11, 11, p. e1004485-e1004485

Research output: Contribution to journalArticleResearchpeer-review

Neuronal Plasticity
Bayesian inference
Plasticity
plasticity
Brain

Neural Computation with Assemblies and Assembly Sequences

Pokorny, C., Griesbacher, G., Jonke, Z. & Legenstein, R., 2015.

Research output: Contribution to conferencePosterResearch

On Triangulation Axes of Polygons

Aigner, W., Aurenhammer, F. & Jüttler, B., 2015, In : Information Processing Letters. 115, p. 45-51

Research output: Contribution to journalArticleResearchpeer-review

Open Access

Reward-based network plasticity as Bayesian inference

Maass, W., Kappel, D., Habenschuss, S. & Legenstein, R., 2015.

Research output: Contribution to conferencePosterResearch

Stochastic network plasticity as Bayesian inference

Legenstein, R., Kappel, D., Habenschuss, S. & Maass, W., 2015.

Research output: Contribution to conferencePosterResearch

Synaptic Plasticity as Bayesian Inference

Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W., 2015.

Research output: Contribution to conferencePosterResearch

Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring

Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W., 2015, (Accepted/In press) Proceedings of NIPS. .

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Plasticity
Sampling
Neural networks
Maximum likelihood
Brain