Robert Legenstein

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

19992022
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Research Output 1999 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

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
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

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
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

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

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

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

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

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

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

2008

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback

Legenstein, R., Pecevski, D. & Maass, W., 2008, In : PLoS computational biology. 4, 10, p. 1-27

Research output: Contribution to journalArticleResearchpeer-review

On the classification capability of sign-constrained perceptrons

Legenstein, R. A. & Maass, W., 2008, In : Neural computation. 20, 1, p. 208-309

Research output: Contribution to journalArticleResearchpeer-review

2007

Edge of Chaos and Prediction of Computational Performance for Neural Circuit Models

Legenstein, R. A. & Maass, W., 2007, In : Neural networks. 20, 3, p. 323-333

Research output: Contribution to journalArticleResearchpeer-review

2005

What can a neuron learn with spike-timing-dependent plasticity?

Legenstein, R. A., Näger, C. & Maass, W., 2005, In : Neural computation. 17, p. 2337-2382

Research output: Contribution to journalArticleResearchpeer-review

Wire length as a circuit complexity measure

Legenstein, R. A. & Maass, W., 2005, In : Journal of computer and system sciences. 70, p. 53-72

Research output: Contribution to journalArticleResearchpeer-review

2003

Input prediction and autonomous movement analysis in recurrent circuit of spiking neurons

Legenstein, R. A., Markram, H. & Maass, W., 2003, In : Reviews in the neurosciences. 14, 1-2, p. 5-19

Research output: Contribution to journalArticleResearchpeer-review