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Research Output 1983 2018

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Article
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

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

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
2017

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

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

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
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

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

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
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

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

To spike or not to spike: That is the question.

Maass, W., 2015, In : Proceedings of the IEEE. 103, 12, p. 2219-2224

Research output: Contribution to journalArticleResearchpeer-review

Triangulations with circular arcs

Aichholzer, O., Aurenhammer, F., Aigner, W., Jüttler, B., Dobiásová, K. & Rote, G., 2015, In : Journal of Graph Algorithms and Applications . p. 43-65

Research output: Contribution to journalArticleResearchpeer-review

2014
Photons
Optical Imaging
Histology
Synaptic Potentials
Pyramidal Cells

A compound memristive synapse model for statistical learning through STDP in spiking neural networks

Bill, J. & Legenstein, R., 2014, In : Frontiers in neuroscience. 8, 214, p. 1-18

Research output: Contribution to journalArticleResearchpeer-review

Statistical Models
Synapses
Learning
Neuronal Plasticity
Computer Simulation

An independent SSVEP-based brain-computer interface in locked-in syndrome

Lesenfants, D., Habbal, D., Lugo, Z., Lebeau, M., Horki, P., Amico, E., Pokorny, C., Gomez, F., Soddu, A., Müller-Putz, G., Laureys, S. & Noirhomme, Q., 2014, In : Journal of neural engineering. 11, 3, 035002.

Research output: Contribution to journalArticleResearchpeer-review

A note on visibility-constrained Voronoi diagrams

Aurenhammer, F., Su, B., Xu, Y. & Zhu, B., 2014, In : Discrete applied mathematics. 174, p. 52-56

Research output: Contribution to journalArticleResearchpeer-review

A Tactile Stimulation Device for EEG Measurements in Clinical Use

Pokorny, C., Breitwieser, C. & Müller-Putz, G., 2014, In : IEEE transactions on biomedical circuits and systems. 8, 3, p. 305-312

Research output: Contribution to journalArticleResearchpeer-review

A theoretical basis for efficient computations with noisy spiking neurons

Jonke, Z., Habenschuss, S. & Maass, W., 2014, (Submitted) In : arXiv.org e-Print archive.

Research output: Contribution to journalArticleResearch

Detection of mental imagery and attempted movements in patients with disorders of consciousness using EEG

Horki, P., Bauernfeind, G., Klobassa, D. S., Pokorny, C., Pichler, G., Schippinger, W. & Müller-Putz, G., 2014, In : Frontiers in Human Neuroscience. 8, 1009, p. 1-9

Research output: Contribution to journalArticleResearchpeer-review

Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning

Hörzer, G. M., Legenstein, R. & Maass, W., 2014, In : Cerebral Cortex. 24, 3, p. 677-690

Research output: Contribution to journalArticleResearchpeer-review

Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment

Legenstein, R. & Maass, W., 2014, In : PLoS computational biology. 10, e1003859;10, p. 1-27

Research output: Contribution to journalArticleResearchpeer-review

NEVESIM: event-driven neural simulation framework with a Python interface

Pecevski, D., Kappel, D. & Jonke, Z., 2014, In : Frontiers in neuroinformatics. 8, 70, p. 1-20

Research output: Contribution to journalArticleResearchpeer-review

Noise as a resource for computation and learning in networks of spiking neurons

Maass, W., 2014, In : Proceedings of the IEEE. 102, 5, p. 860-880

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

On k-Convex Point Sets

Aichholzer, O., Aurenhammer, F., Hackl, T., Hurtado, F., Pilz, A., Ramos, P., Urrutia, J., Valtr, P. & Vogtenhuber, B., 2014, In : Computational geometry. 47, 8, p. 809-832

Research output: Contribution to journalArticleResearchpeer-review

On shape Delaunay tessellations

Aurenhammer, F. & Paulini, G., 2014, In : Information Processing Letters. 114, 10, p. 535-541

Research output: Contribution to journalArticleResearchpeer-review

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning

Kappel, D., Nessler, B. & Maass, W., 2014, In : PLoS computational biology. 10, e1003511;3, p. 1-22

Research output: Contribution to journalArticleResearchpeer-review

2013

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity

Nessler, B., Pfeiffer, M., Buesing, L. & Maass, W., 2013, In : PLoS computational biology. 9, 4, p. e1003037-e1003037

Research output: Contribution to journalArticleResearchpeer-review

Emergence of dynamic memory traces in cortical microcircuit models through STDP

Klampfl, S. & Maass, W., 2013, In : The journal of neuroscience. 33, 28, p. 11515-11529

Research output: Contribution to journalArticleResearchpeer-review

Emergence of Optimal Decoding of Population Codes through STDP

Habenschuss, S., Puhr, H. & Maass, W., 2013, In : Neural computation. 25, 6, p. 1371-1407

Research output: Contribution to journalArticleResearchpeer-review

Integration of nanoscale memristor synapses in neuromorphic computing architectures

Indiveri, G., Linares-Barranco, B., Legenstein, R., Deligeorgis, G. & Prodromakis, T., 2013, In : Nanotechnology. 24, p. 384010-384010

Research output: Contribution to journalArticleResearchpeer-review

Learned graphical models for probabilistic planning provide a new class of movement primitives

Rückert, E., Neumann, G., Toussaint, M. & Maass, W., 2013, In : Frontiers in Computational Neuroscience . 6, p. 1-20

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems.

Rückert, E., 2013, In : Frontiers in Computational Neuroscience . 7, 138, p. 1-18

Research output: Contribution to journalArticleResearchpeer-review

Stochastic Computations in Cortical Microcircuit Models

Habenschuss, S., Jonke, Z. & Maass, W., 2013, In : PLoS computational biology. 9, 11, p. e1003311-e1003311

Research output: Contribution to journalArticleResearchpeer-review