Robert Legenstein
Robert Legenstein
Institute for Theoretical Computer Science, Graz University of Technology
Verified email at igi.tugraz.at - Homepage
Title
Cited by
Cited by
Year
Integration of nanoscale memristor synapses in neuromorphic computing architectures
G Indiveri, B Linares-Barranco, R Legenstein, G Deligeorgis, ...
Nanotechnology 24 (38), 384010, 2013
4452013
Edge of chaos and prediction of computational performance for neural circuit models
R Legenstein, W Maass
Neural networks 20 (3), 323-334, 2007
3742007
Combining predictions for accurate recommender systems
M Jahrer, A Töscher, R Legenstein
Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010
3132010
What can a neuron learn with spike-timing-dependent plasticity?
R Legenstein, C Naeger, W Maass
Neural computation 17 (11), 2337-2382, 2005
2502005
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
R Legenstein, D Pecevski, W Maass
PLoS Comput Biol 4 (10), e1000180, 2008
2472008
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
A Serb, J Bill, A Khiat, R Berdan, R Legenstein, T Prodromakis
Nature communications 7 (1), 1-9, 2016
2252016
Long short-term memory and learning-to-learn in networks of spiking neurons
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass
arXiv preprint arXiv:1803.09574, 2018
1582018
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
L Büsing, B Schrauwen, R Legenstein
Neural computation 22 (5), 1272-1311, 2010
1382010
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
L Büsing, B Schrauwen, R Legenstein
Neural computation 22 (5), 1272-1311, 2010
1382010
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning
GM Hoerzer, R Legenstein, W Maass
Cerebral cortex 24 (3), 677-690, 2014
1292014
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons
R Legenstein, W Maass
Journal of Neuroscience 31 (30), 10787-10802, 2011
1262011
What makes a dynamical system computationally powerful
R Legenstein, W Maass
New directions in statistical signal processing: From systems to brain, 127-154, 2007
1232007
What makes a dynamical system computationally powerful
R Legenstein, W Maass
New directions in statistical signal processing: From systems to brain, 127-154, 2007
1232007
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
R Legenstein, SM Chase, AB Schwartz, W Maass
Journal of Neuroscience 30 (25), 8400-8410, 2010
1172010
Reinforcement learning on slow features of high-dimensional input streams
R Legenstein, N Wilbert, L Wiskott
PLoS Comput Biol 6 (8), e1000894, 2010
1012010
A compound memristive synapse model for statistical learning through STDP in spiking neural networks
J Bill, R Legenstein
Frontiers in neuroscience 8, 412, 2014
952014
Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system
S Schmitt, J Klähn, G Bellec, A Grübl, M Guettler, A Hartel, S Hartmann, ...
2017 international joint conference on neural networks (IJCNN), 2227-2234, 2017
842017
Deep rewiring: Training very sparse deep networks
G Bellec, D Kappel, W Maass, R Legenstein
arXiv preprint arXiv:1711.05136, 2017
832017
Network plasticity as Bayesian inference
D Kappel, S Habenschuss, R Legenstein, W Maass
PLoS Comput Biol 11 (11), e1004485, 2015
832015
At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
T Natschläger, N Bertschinger, R Legenstein
Advances in neural information processing systems 17, 145-152, 2005
782005
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