default search action
Robert Legenstein
Person information
- affiliation: Graz University of Technology, Austria
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 2024
- [j22]Ozan Özdenizci, Robert Legenstein:
Adversarially Robust Spiking Neural Networks Through Conversion. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j21]Horst Petschenig
, Robert Legenstein
Quantized rewiring: hardware-aware training of sparse deep neural networks. Neuromorph. Comput. Eng. 3(2): 24006 (2023) - [j20]Robert Legenstein
Editorial: Focus on algorithms for neuromorphic computing. Neuromorph. Comput. Eng. 3(3): 30402 (2023) - [j19]Ozan Özdenizci
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 10346-10357 (2023) - 2022
- [j18]Agnes Korcsak-Gorzo
Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Comput. Biol. 18(3) (2022) - 2020
- [j17]Thomas Limbacher, Robert Legenstein
Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring. Frontiers Comput. Neurosci. 14: 57 (2020) - [j16]Christophe Verbist, Michael G. Müller
The location of the axon initial segment affects the bandwidth of spike initiation dynamics. PLoS Comput. Biol. 16(7) (2020) - 2019
- [j15]Jacques Kaiser, Michael Hoff, Andreas Konle, Juan Camilo Vasquez Tieck
Embodied Synaptic Plasticity With Online Reinforcement Learning. Frontiers Neurorobotics 13: 81 (2019) - [j14]Yexin Yan
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. IEEE Trans. Biomed. Circuits Syst. 13(3): 579-591 (2019) - 2015
- [j13]Robert Legenstein
Computer science: Nanoscale connections for brain-like circuits. Nat. 521(7550): 37-38 (2015) - [j12]David Kappel
Network Plasticity as Bayesian Inference. PLoS Comput. Biol. 11(11) (2015) - 2014
- [j11]Robert Legenstein
Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment. PLoS Comput. Biol. 10(10) (2014) - 2011
- [j10]Marco Baglietto
Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Trans. Neural Networks 22(1): 1-7 (2011) - 2010
- [j9]Lars Büsing, Benjamin Schrauwen, Robert Legenstein
Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons. Neural Comput. 22(5): 1272-1311 (2010) - [j8]Robert Legenstein
Reinforcement Learning on Slow Features of High-Dimensional Input Streams. PLoS Comput. Biol. 6(8) (2010) - 2009
- [j7]Stefan Klampfl, Robert Legenstein
Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components. Neural Comput. 21(4): 911-959 (2009) - 2008
- [j6]Robert Legenstein
On the Classification Capability of Sign-Constrained Perceptrons. Neural Comput. 20(1): 288-309 (2008) - [j5]Robert Legenstein
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback. PLoS Comput. Biol. 4(10) (2008) - 2007
- [j4]Robert Legenstein
Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20(3): 323-334 (2007) - 2005
- [j3]Robert Legenstein, Wolfgang Maass:
Wire length as a circuit complexity measure. J. Comput. Syst. Sci. 70(1): 53-72 (2005) - [j2]Robert Legenstein
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? Neural Comput. 17(11): 2337-2382 (2005) - 2002
- [j1]Robert Legenstein, Wolfgang Maass:
Neural circuits for pattern recognition with small total wire length. Theor. Comput. Sci. 287(1): 239-249 (2002)
Conference and Workshop Papers
- 2024
- [c27]Jasmin Viktoria Gritsch, Robert Legenstein, Ozan Özdenizci:
Preserving Real-World Robustness of Neural Networks Under Sparsity Constraints. ECML/PKDD (5) 2024: 337-354 - 2023
- [c26]Francisco Javier Klaiber Aboitiz, Robert Legenstein
Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks. ICANN (10) 2023: 248-259 - [c25]Romain Ferrand
Context-Dependent Computations in Spiking Neural Networks with Apical Modulation. ICANN (1) 2023: 381-392 - [c24]Ceca Kraisnikovic
Fault Pruning: Robust Training of Neural Networks with Memristive Weights. UCNC 2023: 124-139 - 2022
- [c23]Ozan Özdenizci, Robert Legenstein:
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching. CVPR 2022: 13378-13387 - 2021
- [c22]Manuel Traub
Dynamic Action Inference with Recurrent Spiking Neural Networks. ICANN (5) 2021: 233-244 - [c21]Ozan Özdenizci, Robert Legenstein:
Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling. ICML 2021: 8314-8324 - [c20]Manuel Traub, Robert Legenstein
Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks. IROS 2021: 4918-4925 - 2020
- [c19]Thomas Limbacher, Robert Legenstein:
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks. NeurIPS 2020 - 2018
- [c18]Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein:
Deep Rewiring: Training very sparse deep networks. ICLR (Poster) 2018 - [c17]Robert Legenstein, Wolfgang Maass, Christos H. Papadimitriou, Santosh S. Vempala:
Long Term Memory and the Densest K-Subgraph Problem. ITCS 2018: 57:1-57:15 - [c16]Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass:
Long short-term memory and Learning-to-learn in networks of spiking neurons. NeurIPS 2018: 795-805 - 2017
- [c15]Sebastian Schmitt, Johann Klähn, Guillaume Bellec, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch
Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system. IJCNN 2017: 2227-2234 - [c14]Mihai A. Petrovici
Pattern representation and recognition with accelerated analog neuromorphic systems. ISCAS 2017: 1-4 - 2016
- [c13]Behnam Taraghi
Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. LAK 2016: 449-453 - [c12]Robert Legenstein, Christos H. Papadimitriou, Santosh S. Vempala, Wolfgang Maass:
Variable Binding through Assemblies in Spiking Neural Networks. CoCo@NIPS 2016 - 2015
- [c11]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring. NIPS 2015: 370-378 - 2010
- [c10]Michael Jahrer, Andreas Töscher, Robert Legenstein:
Combining predictions for accurate recommender systems. KDD 2010: 693-702 - 2009
- [c9]Robert Legenstein, Steven M. Chase, Andrew B. Schwartz, Wolfgang Maass:
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning. NIPS 2009: 1105-1113 - 2008
- [c8]Benjamin Schrauwen, Lars Buesing, Robert Legenstein:
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. NIPS 2008: 1425-1432 - 2007
- [c7]Robert Legenstein, Dejan Pecevski, Wolfgang Maass:
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity. NIPS 2007: 881-888 - 2006
- [c6]Stefan Klampfl, Robert Legenstein, Wolfgang Maass:
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons. NIPS 2006: 713-720 - 2005
- [c5]Robert Legenstein, Wolfgang Maass:
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. NIPS 2005: 763-770 - 2004
- [c4]Nils Bertschinger, Thomas Natschläger, Robert Legenstein:
At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks. NIPS 2004: 145-152 - [c3]Wolfgang Maass, Robert Legenstein, Nils Bertschinger:
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits. NIPS 2004: 865-872 - 2002
- [c2]Wolfgang Maass, Robert Legenstein, Henry Markram:
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models. Biologically Motivated Computer Vision 2002: 282-293 - 2000
- [c1]Robert Legenstein, Wolfgang Maass:
Foundations for a Circuit Complexity Theory of Sensory Processing. NIPS 2000: 259-265
Parts in Books or Collections
- 2021
- [p2]Ceca Kraisnikovic, Wolfgang Maass, Robert Legenstein:
Spike-Based Symbolic Computations on Bit Strings and Numbers. Neuro-Symbolic Artificial Intelligence 2021: 214-234 - 2019
- [p1]Wolfgang Maass, Christos H. Papadimitriou, Santosh S. Vempala, Robert Legenstein:
Brain Computation: A Computer Science Perspective. Computing and Software Science 2019: 184-199
Reference Works
- 2014
- [r1]Robert Legenstein:
Recurrent Network Models, Reservoir Computing. Encyclopedia of Computational Neuroscience 2014
Informal and Other Publications
- 2024
- [i24]Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Spela Brglez
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing. CoRR abs/2405.05141 (2024) - [i23]Alejandro Linares-Barranco, Luciano Prono, Robert Legenstein, Giacomo Indiveri, Charlotte Frenkel:
Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator. CoRR abs/2405.12849 (2024) - [i22]Maximilian Baronig, Romain Ferrand, Silvester Sabathiel, Robert Legenstein:
Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation. CoRR abs/2408.07517 (2024) - 2023
- [i21]Ozan Özdenizci, Robert Legenstein:
Adversarially Robust Spiking Neural Networks Through Conversion. CoRR abs/2311.09266 (2023) - 2022
- [i20]Thomas Limbacher, Ozan Özdenizci, Robert Legenstein
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity. CoRR abs/2205.11276 (2022) - [i19]Ozan Özdenizci, Robert Legenstein:
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models. CoRR abs/2207.14626 (2022) - 2021
- [i18]Manuel Traub, Robert Legenstein, Sebastian Otte:
Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks. CoRR abs/2104.04064 (2021) - 2020
- [i17]Jacques Kaiser, Michael Hoff, Andreas Konle, Juan Camilo Vasquez Tieck, David Kappel, Daniel Reichard, Anand Subramoney, Robert Legenstein, Arne Roennau, Wolfgang Maass, Rüdiger Dillmann:
Embodied Synaptic Plasticity with Online Reinforcement learning. CoRR abs/2003.01431 (2020) - [i16]Michael G. Müller
Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks. CoRR abs/2006.11099 (2020) - 2019
- [i15]Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass:
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. CoRR abs/1901.09049 (2019) - [i14]Yexin Yan, David Kappel, Felix Neumärker, Johannes Partzsch, Bernhard Vogginger, Sebastian Höppner, Steve B. Furber, Wolfgang Maass, Robert Legenstein, Christian Mayr:
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype. CoRR abs/1903.08500 (2019) - [i13]Alberto Riminucci
Fast learning synapses with molecular spin valves via selective magnetic potentiation. CoRR abs/1903.08624 (2019) - 2018
- [i12]Guillaume Bellec, Darjan Salaj, Anand Subramoney
Long short-term memory and Learning-to-learn in networks of spiking neurons. CoRR abs/1803.09574 (2018) - 2017
- [i11]Sebastian Schmitt
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System. CoRR abs/1703.01909 (2017) - [i10]Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn, David Stöckel, Anna Schroeder, Guillaume Bellec, Johannes Bill, Oliver Breitwieser, Ilja Bytschok, Andreas Grübl, Maurice Güttler, Andreas Hartel, Stephan Hartmann, Dan Husmann de Oliveira, Kai Husmann, Sebastian Jeltsch, Vitali Karasenko, Mitja Kleider, Christoph Koke, Alexander Kononov, Christian Mauch, Paul Müller, Johannes Partzsch
Pattern representation and recognition with accelerated analog neuromorphic systems. CoRR abs/1703.06043 (2017) - [i9]David Kappel, Robert Legenstein, Stefan Habenschuss, Michael Hsieh, Wolfgang Maass:
Reward-based stochastic self-configuration of neural circuits. CoRR abs/1704.04238 (2017) - [i8]Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein:
Deep Rewiring: Training very sparse deep networks. CoRR abs/1711.05136 (2017) - 2016
- [i7]Zhaofei Yu, David Kappel, Robert Legenstein, Sen Song, Feng Chen, Wolfgang Maass:
Hamiltonian synaptic sampling in a model for reward-gated network plasticity. CoRR abs/1606.00157 (2016) - 2015
- [i6]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Network Plasticity as Bayesian Inference. CoRR abs/1504.05143 (2015) - 2013
- [i5]Giacomo Indiveri, Bernabé Linares-Barranco, Robert Legenstein, George Deligeorgis, Themistoklis Prodromakis:
Integration of nanoscale memristor synapses in neuromorphic computing architectures. CoRR abs/1302.7007 (2013) - 2001
- [i4]Robert Legenstein, Wolfgang Maass:
Optimizing the Layout of a Balanced Tree. Electron. Colloquium Comput. Complex. TR01 (2001) - [i3]Robert Legenstein, Wolfgang Maass:
Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing. Electron. Colloquium Comput. Complex. TR01 (2001) - [i2]Robert Legenstein, Wolfgang Maass:
Neural Circuits for Pattern Recognition with Small Total Wire Length. Electron. Colloquium Comput. Complex. TR01 (2001) - [i1]Robert Legenstein:
On the Complexity of Knock-knee Channel-Routing with 3-Terminal Nets. Electron. Colloquium Comput. Complex. TR01 (2001)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-09 12:51 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: