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Robert Legenstein
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- affiliation: Graz University of Technology, Austria
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2020 – today
- 2024
- [j22]Ozan Özdenizci, Robert Legenstein:
Adversarially Robust Spiking Neural Networks Through Conversion. Trans. Mach. Learn. Res. 2024 (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 - [i24]Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Spela Brglez
, Thomas Limbacher, Enrique Piñero, Alejandro Linares Barranco, Angeliki Pantazi, Robert Legenstein:
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
- [j21]Horst Petschenig
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) - [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 - [i21]Ozan Özdenizci, Robert Legenstein:
Adversarially Robust Spiking Neural Networks Through Conversion. CoRR abs/2311.09266 (2023) - 2022
- [j18]Agnes Korcsak-Gorzo
Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Comput. Biol. 18(3) (2022) - [c23]Ozan Özdenizci, Robert Legenstein:
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching. CVPR 2022: 13378-13387 - [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
- [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 - [p2]Ceca Kraisnikovic, Wolfgang Maass, Robert Legenstein:
Spike-Based Symbolic Computations on Bit Strings and Numbers. Neuro-Symbolic Artificial Intelligence 2021: 214-234 - [i18]Manuel Traub, Robert Legenstein, Sebastian Otte:
Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks. CoRR abs/2104.04064 (2021) - 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) - [c19]Thomas Limbacher, Robert Legenstein:
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks. NeurIPS 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)
2010 – 2019
- 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) - [p1]Wolfgang Maass, Christos H. Papadimitriou, Santosh S. Vempala, Robert Legenstein:
Brain Computation: A Computer Science Perspective. Computing and Software Science 2019: 184-199 - [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
- [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 - [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
- [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 - [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
- [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 - [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
- [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) - [c11]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring. NIPS 2015: 370-378 - [i6]David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass:
Network Plasticity as Bayesian Inference. CoRR abs/1504.05143 (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) - [r1]Robert Legenstein:
Recurrent Network Models, Reservoir Computing. Encyclopedia of Computational Neuroscience 2014 - 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) - 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) - [c10]Michael Jahrer, Andreas Töscher, Robert Legenstein:
Combining predictions for accurate recommender systems. KDD 2010: 693-702
2000 – 2009
- 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) - [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
- [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) - [c8]Benjamin Schrauwen, Lars Buesing, Robert Legenstein:
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. NIPS 2008: 1425-1432 - 2007
- [j4]Robert Legenstein
Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20(3): 323-334 (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
- [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) - [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
- [j1]Robert Legenstein, Wolfgang Maass:
Neural circuits for pattern recognition with small total wire length. Theor. Comput. Sci. 287(1): 239-249 (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 - 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) - 2000
- [c1]Robert Legenstein, Wolfgang Maass:
Foundations for a Circuit Complexity Theory of Sensory Processing. NIPS 2000: 259-265
Coauthor Index
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