Source: https://www.tugraz.at/institute/igi/people/prof-legenstein/legenstein-publications/
Timestamp: 2019-04-23 23:50:55+00:00

Document:
G. Bellec, F. Scherr, E. Hajek, D. Salaj, R. Legenstein, and W. Maass.
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.
arxiv.org/abs/1901.09049 , January 2019. (PDF).
 C. Liu, G. Bellec, B. Vogginger, D. Kappel, J. Partzsch, F. Neumärker, S. Höppner, W. Maass, S. B. Furber, R. Legenstein, and C. G. Mayr.
Memory-efficient deep learning on a spinnaker 2 prototype.
 G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass.
Long short-term memory and learning-to-learn in networks of spiking neurons.
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montreal, Canada; arXiv:1803.09574 , 2018. (PDF).
 R. Legenstein, W. Maass, C. H. Papadimitriou, and S. S. Vempala.
Long term memory and the densest K-subgraph problem.
In Proc. of Innovations in Theoretical Computer Science (ITCS) , 2018. (PDF).
 C. Pokorny, M. J. Ison, A. Rao, R. Legenstein, C. Papadimitriou, and W. Maass.
Associations between memory traces emerge in a generic neural circuit model through STDP.
 G. Bellec, D. Kappel, W. Maass, and R. Legenstein.
Deep rewiring: training very sparse deep networks.
International Conference on Learning Representations (ICLR) , 2018. (PDF).
 R. Legenstein, Z. Jonke, S. Habenschuss, and W. Maass.
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition.
 Z. Jonke, R. Legenstein, S. Habenschuss, and W. Maass.
Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs.
Journal of Neuroscience , 37(35):8511-8523, 2017. (PDF).
 D. Kappel, R. Legenstein, S. Habenschuss, M. Hsieh, and W. Maass.
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning.
eNeuro, 2 April , 2018. DOI: doi.org/10.1523/ENEURO.0301-17.2018. (PDF).
 M. A. Petrovici, S. Schmitt, J. Klähn, D. Stöckel, A. Schroeder, G. Bellec, J. Bill, O. Breitwieser, I. Bytschok, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, , S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, P. Müller, J. Partzsch, T. Pfeil, S. Schiefer, S. Scholze, A. Subramoney, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, R. Schüffny, C. Mayr, J. Schemmel, and K. Meier.
Pattern representation and recognition with accelerated analog neuromorphic systems.
 S. Schmitt, J. Klähn, G. Bellec, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, E. Müller, P. Müller, J. Partzsch, M. A. Petroviciy, S. Schiefer, S. Scholze, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, C. Mayr, R. Schüffny, J. Schemmel, and K. Meier.
IEEE International Joint Conference on Neural Networks (IJCNN) 2017 , pages 2227-2234, 2017. (PDF).
 W. Maass, C. H. Papadimitriou, S. Vempala, and R. Legenstein.
Brain computation: A computer science perspective.
Draft of an invited contribution to Springer Lecture Notes in Computer Science , vol. 10000, 2017. (PDF).
 R. Legenstein, C. H. Papadimitriou, S. Vempala, and W. Maass.
Assembly pointers for variable binding in networks of spiking neurons.
arXiv preprint arXiv:1611.03698 , 2016. (PDF).
 A. Serb, J. Bill, A. Khiat, R. Berdan, R. Legenstein, and T. Prodromakis.
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.
 Z. Yu, D. Kappel, R. Legenstein, S. Song, F. Chen, and W. Maass.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling.
 D. Kappel, S. Habenschuss, R. Legenstein, and W. Maass.
Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, pages 370-378. Curran Associates, Inc., 2015. (PDF).
 J. Bill, L. Buesing, S. Habenschuss, B. Nessler, W. Maass, and R. Legenstein.
Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition.
Nanoscale connections for brain-like circuits.
Nature , 521:37-38, 2015. (PDF).
Network plasticity as Bayesian inference.
Encyclopedia of Computational Neuroscience , pages 1-5. Springer New York, 2014.
 J. Bill and R. Legenstein.
A compound memristive synapse model for statistical learning through STDP in spiking neural networks.
 R. Legenstein and W. Maass.
Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.
 A. V. Blackman, S. Grabuschnig, R. Legenstein, and P. J. Sjöström.
A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling.
 G. Indiveri, B. Linares-Barranco, R. Legenstein, G. Deligeorgis, and T. Prodromakis.
Integration of nanoscale memristor synapses in neuromorphic computing architectures.
Nanotechnology , 24:384010, 2014. (PDF).
 G. M. Hoerzer, R. Legenstein, and Wolfgang Maass.
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning.
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons.
 R. Legenstein, N. Wilbert, and L. Wiskott.
Reinforcement learning on slow features of high-dimensional input streams.
PLoS Computational Biology , 6(8):e1000894, 2010. (PDF).
 M. Jahrer, A. Töscher, and R. Legenstein.
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining , pages 693-702, New York, NY, USA, 2010. ACM. (PDF).
 R. Legenstein, S. M. Chase, A. B. Schwartz, and W. Maass.
A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task.
The Journal of Neuroscience , 30(25):8400-8410, 2010. (PDF).
 R. Legenstein, S. A. Chase, A. B. Schwartz, and W. Maass.
Proc. of NIPS 2009: Advances in Neural Information Processing Systems , D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, volume 22, pages 1105-1113. MIT Press, 2010. (PDF).
 L. Buesing, B. Schrauwen, and R. Legenstein.
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons.
Neural Computation , 22(5):1272-1311, 2010. (PDF).
 B. Schrauwen, L. Buesing, and R. Legenstein.
Proc. of NIPS 2008, Advances in Neural Information Processing Systems , volume 21, pages 1425-1432. MIT Press, 2009. (PDF).
[22b] B. Schrauwen, L. Buesing, and R. Legenstein.
Proc. of NIPS 2008, Advances in Neural Information Processing Systems , volume 21. MIT Press, 2009. in press. (PDF).
 Andreas Toescher, Michael Jahrer, and Robert Legenstein.
KDD-Cup and Workshop . ACM, 2008. (PDF).
 R. Legenstein, D. Pecevski, and W. Maass.
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.
Proc. of NIPS 2007, Advances in Neural Information Processing Systems , volume 20, pages 881-888. MIT Press, 2008. (PDF).
 S. Klampfl, R. Legenstein, and W. Maass.
Spiking neurons can learn to solve information bottleneck problems and extract independent components.
Neural Computation , 21(4):911-959, 2009. (PDF).
On the classification capability of sign-constrained perceptrons.
Neural Computation , 20(1):288-309, 2008. (PDF).
Proc. of NIPS 2006, Advances in Neural Information Processing Systems , volume 19, pages 713-720. MIT Press, 2007. (PDF).
Edge of chaos and prediction of computational performance for neural circuit models.
Neural Networks , 20(3):323-334, 2007. (PDF).
New Directions in Statistical Signal Processing: From Systems to Brains , S. Haykin, J. C. Principe, T.J. Sejnowski, and J.G. McWhirter, editors, pages 127-154. MIT Press, 2007. (PDF).
 R. Legenstein, C. Naeger, and W. Maass.
What can a neuron learn with spike-timing-dependent plasticity?.
Neural Computation , 17(11):2337-2382, 2005. (PDF).
[13a] R. Legenstein and W. Maass.
Advances in Neural Information Processing Systems , Y. Weiss, B. Schoelkopf, and J. Platt, editors, volume 18, pages 763-770. MIT Press, 2006. (PDF).
 T. Natschlaeger, N. Bertschinger, and R. Legenstein.
Advances in Neural Information Processing Systems 17 , Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, pages 145-152. MIT Press, Cambridge, MA, 2005. (PDF).
 W. Maass, R. Legenstein, and N. Bertschinger.
Advances in Neural Information Processing Systems , L. K. Saul, Y. Weiss, and L. Bottou, editors, volume 17, pages 865-872. MIT Press, 2005. (PDF).
 R. A. Legenstein and W. Maass.
Wire length as a circuit complexity measure.
Journal of Computer and System Sciences , 70:53-72, 2005. (PDF).
 R. Legenstein, H. Markram, and W. Maass.
Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons.
Reviews in the Neurosciences (Special Issue on Neuroinformatics of Neural and Artificial Computation) , 14(1-2):5-19, 2003. (PDF).
 W. Maass, R. Legenstein, and H. Markram.
Lecture Notes in Computer Science , pages 282-293. Springer (Berlin), 2002. (PDF).
The Wire-Length Complexity of Neural Networks . PhD thesis, Graz University of Technology, 2002. (PDF).
Neural circuits for pattern recognition with small total wire length.
Theoretical Computer Science , 287:239-249, 2002. (PDF).
On the complexity of knock-knee channel routing with 3-terminal nets.
Technical Report , 2002. (PDF).
Optimizing the layout of a balanced tree.
Technical Report , 2001. (PDF).
Proc. of NIPS 2000, Advances in Neural Information Processing Systems , T. K. Leen, T. G. Dietterich, and V. Tresp, editors, volume 13, pages 259-265, Cambridge, 2001. MIT Press. (PDF).
Effizientes Layout von Neuronalen Netzen. Master's thesis, Technische Universitaet Graz, September 1999.
[-] R. Legenstein, S. A. Chase, A. B. Schwartz, and W. Maass.
A model for learning effects in motor cortex that may facilitate the brain control of neuroprosthetic devices.
38th Annual Conference of the Society for Neuroscience, Program 517.6 , 2008.
[-] R. Legenstein and W. Maass.
An integrated learning rule for branch strength potentiation and STDP.
39th Annual Conference of the Society for Neuroscience, Program 895.20, Poster HH36 , 2009.

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