Robert Legenstein

Assoc.Prof. Dipl.-Ing. Dr.techn.

19992022
If you made any changes in Pure these will be visible here soon.

Research Output 1999 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

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

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 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

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
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

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

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

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

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
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

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

Recurrent network models, reservoir computing

Legenstein, R., 2014, Encyclopedia of Computational Neuroscience. 1 ed. New York: Springer, p. 1-5

Research output: Chapter in Book/Report/Conference proceedingChapterResearch

2013

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

Local inhibition facilitates synaptic learning in spatially extended Bayesian spiking networks

Bill, J., Buesing, L., Habenschuss, S., Nessler, B., Legenstein, R. & Maass, W., 2013.

Research output: Contribution to conferencePosterResearch

2012

Improved feature extraction by pyramidal cells through relaxed lateral inhibition

Jonke, Z., Habenschuss, S., Legenstein, R. & Maass, W., 2012.

Research output: Contribution to conferencePosterResearch

2011

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

Legenstein, R. & Maass, W., 2011, In : The journal of neuroscience. 31, 30, p. 10787-10802

Research output: Contribution to journalArticleResearchpeer-review

Dendritic computation could support probabilistic inference in networks of spiking neurons

Legenstein, R., Pecevski, D., Büsing, L. H. & Maass, W., 2011.

Research output: Contribution to conferencePosterResearch

Dendritic computation could support probabilistic inference in networks of spiking neurons

Legenstein, R., 2011, Abstracts Annual Meeting. ., p. 1-1

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

Eliminating the teacher in reservoir computing

Hörzer, G. M., Legenstein, R. & Maass, W., 2011, Proceedings of the 2nd International Conference on Morphological Computation. ., p. 32-32

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

2010

A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task

Legenstein, R., Chase, S., Schwartz, A. B. & Maass, W., 2010, In : The journal of neuroscience. 30, 25, p. 8400-8410

Research output: Contribution to journalArticleResearchpeer-review

Combining predictions for accurate recommender systems

Legenstein, R., 2010, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ., p. 693-702

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

Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons

Büsing, L. H., Schrauwen, B. & Legenstein, R., 2010, In : Neural computation. 22, 5, p. 1272-1311

Research output: Contribution to journalArticleResearchpeer-review

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning

Legenstein, R., Chase, S., Schwartz, A. B. & Maass, W., 2010, Proc. of NIPS 2009, Advances in Neural Information Processing Systems. MIT Press, p. 1105-1113

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

Reinforcement learning on slow features of high-dimensional input streams

Legenstein, R., 2010, In : PLoS computational biology. 6, 8, p. e1000894-e1000894

Research output: Contribution to journalArticleResearchpeer-review

2009

An integrated learning rule for branch strength potentiation and STDP

Legenstein, R. & Maass, W., 2009.

Research output: Contribution to conferencePosterResearch

Computation and Learning in Neural Systems

Legenstein, R., 2009

Research output: ThesisHabilitationResearch

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning

Legenstein, R., Chase, S. M., Schwartz, A. B. & Maass, W., 2009.

Research output: Contribution to conferencePosterResearch

On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing

Schrauwen, B., Büsing, L. H. & Legenstein, R., 2009, (Accepted/In press) Annual Conference on Neural Information Processing Systems. .

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

Reinforcement learning on complex visual stimuli

Wilbert, N., Franzius, M., Legenstein, R. & Wiskott, L., 2009.

Research output: Contribution to conferencePosterResearch

Slowness in hierarchical networks for visual processing

Wilbert, N., Legenstein, R. & Wiskott, L., 2009.

Research output: Contribution to conferencePosterResearch

Spiking neurons can learn to solve information bottleneck problems and to extract independent components

Klampfl, S., Legenstein, R. A. & Maass, W., 2009, In : Neural computation. 21, 4, p. 911-959

Research output: Contribution to journalArticleResearchpeer-review