Robert Legenstein

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

19992022
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Forschungsoutput 1999 2019

2019

A solution to the learning dilemma for recurrent networks of spiking neurons

Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R. & Maass, W., 9 Dez 2019, in : bioRxiv - the Preprint Server for Biology. 2019, 31 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

Recurrent neural networks
Reinforcement learning
Complex networks
Backpropagation
Neurons

Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets

Bellec, G., Scherr, F., Hajek, E., Salaj, D., Legenstein, R. & Maass, W., 25 Jan 2019, in : arXiv.org e-Print archive. 2019, S. 1-37 37 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

Datei
Backpropagation
Neurons
Neural networks
Learning algorithms
Brain

Brain Computation: A Computer Science Perspective

Maass, W., Papadimitriou, C. H., Vempala, S. & Legenstein, R., 5 Okt 2019, Computing and Software Science.: Lecture Notes in Computer Science. Springer, Cham, Band 10000. S. 184-199 16 S.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtForschungBegutachtung

Brain
Neurosciences
Research

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Yan, Y., Kappel, D., Neumärker, F., Partzsch, J., Partzsch, J., Vogginger, B., Höppner, S., Furber, S., Maass, W., Legenstein, R. & Mayr, C., 27 Mär 2019, in : IEEE transactions on biomedical circuits and systems. 2019, 13:3, S. 579-591 13 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Open Access
Plasticity
Brain
Particle accelerators
Hardware
Data storage equipment

Embodies Synaptic plasticity with online reinforcement learning

Kaiser, J., Hoff, M., Konle, A., Tieck, J. C. V., Kappel, D., Reichard, D., Subramoney, A., Legenstein, R., Roennau, A., Maass, W. & Dillmann, R., 3 Okt 2019, in : Frontiers in Neurorobotics. 2019, S. 1-11 11 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Reinforcement learning
Plasticity
Brain
Robotics
Random processes

STDP forms associations between memory traces in networks of spiking neurons

Pokorny, C., Ison, M. J., Rao, A., Legenstein, R., Maass, W. & Papadimitriou, C., 12 Aug 2019, in : Cerebral Cortex. 2019, 00, S. 1-17 17 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Neurons
Brain
Statistical Models
Cognition
Rodentia
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 Mär 2018, in : eNeuro. 5, 2, 27 S., e0301-17.2018.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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, Band 94. S. 57:1–57:15 57. (LIPIcs-Leibniz International Proceedings in Informatics ).

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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 S., 840.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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.

Publikation: KonferenzbeitragPosterForschung

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. S. 1-27 24 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

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. S. 1-36 36 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

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.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

Datei
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, S. 8511– 8523 24 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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 mehrScholze, S., Thanasoulis, V., Vogginger, B., Legenstein, R., Maass, W., Mayr, C., Schüffny, R., Schemmel, J. & Meier, K., 17 Mär 2017, in : arXiv.org e-Print archive. arXiv:1703.01909, 8 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschung

Datei
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 mehrMauch, 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 Mär 2017, in : arXiv.org e-Print archive. preprint arXiv:1703.06043, 4 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Open Access
Datei
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 S.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Datei
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, S. 449-453.

Publikation: KonferenzbeitragPaperForschungBegutachtung

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.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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, S. e0134356-e0134356

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Neurons
Plasticity
learning
Probability Theory
neurons

Nanoscale connections for brain-like circuits

Legenstein, R., 2015, in : Nature (London). 521, S. 37-38

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Network plasticity as Bayesian inference

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

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Neuronal Plasticity
Bayesian inference
Plasticity
plasticity
Brain

Neural Computation with Assemblies and Assembly Sequences

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

Publikation: KonferenzbeitragPosterForschung

Reward-based network plasticity as Bayesian inference

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

Publikation: KonferenzbeitragPosterForschung

Stochastic network plasticity as Bayesian inference

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

Publikation: KonferenzbeitragPosterForschung

Synaptic Plasticity as Bayesian Inference

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

Publikation: KonferenzbeitragPosterForschung

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

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Plasticity
Sampling
Neural networks
Maximum likelihood
Brain
2014

A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling

Legenstein, R., 2014, in : Frontiers in Neuroanatomy . 8, S. 65-65

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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, S. 1-18

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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, S. 677-690

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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, S. 1-27

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Recurrent network models, reservoir computing

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtForschung

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, S. 384010-384010

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Local inhibition facilitates synaptic learning in spatially extended Bayesian spiking networks

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

Publikation: KonferenzbeitragPosterForschung

2012

Improved feature extraction by pyramidal cells through relaxed lateral inhibition

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

Publikation: KonferenzbeitragPosterForschung

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, S. 10787-10802

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Dendritic computation could support probabilistic inference in networks of spiking neurons

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

Publikation: KonferenzbeitragPosterForschung

Dendritic computation could support probabilistic inference in networks of spiking neurons

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschung

Eliminating the teacher in reservoir computing

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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, S. 8400-8410

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Combining predictions for accurate recommender systems

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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, S. 1272-1311

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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, S. 1105-1113

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Reinforcement learning on slow features of high-dimensional input streams

Legenstein, R., 2010, in : PLoS computational biology. 6, 8, S. e1000894-e1000894

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

2009

An integrated learning rule for branch strength potentiation and STDP

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

Publikation: KonferenzbeitragPosterForschung