Publications

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Bertschinger, N., and T. Natschläger, "Real-time computation at the edge of chaos in recurrent neural networks", Neural Computation, vol. 16, no. 7, Cambridge, MA, USA, MIT Press, pp. 1413–1436, 2004.
Maass, W., T. Natschlaeger, and H. Markram, "Real-time Computing without stable states: A New Framework for Neural Computation Based on Perturbations", Neural Computation, vol. 14, no. 11, pp. 2531–2560, 2002. Abstract
Hinaut, X., and P. F. Dominey, "Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing", PLoS ONE, vol. 8, no. 2, pp. e52946, 02/2013. Abstract
Verstraeten, D., B. Schrauwen, and J V. Campenhout, "Recognition of Isolated Digits using a Liquid State Machine", Proceedings of SPS-DARTS 2005, pp. 135–138, April, 2005.
Reinhart, F. R., and J. J. Steil, "Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot", LAB-RS, 2008. Abstract
Enel, P., M. Khamassi, E. Procyk, and P. F. Dominey, "REINFORCEMENT LEARNING MODEL IN PROBABILISTICALLY REWARDED TASK", Neurocomp, Lyon, France, 2010. Abstract  Download: Enel2010_NEUROCOMP2010.pdf (403.22 KB)
Klampfl, S., and W. Maass, "Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks", NIPS 2009: Advances in Neural Information Processing Systems, vol. 22: MIT Press, 2010, 2009. Abstract
Lukoševičius, M., "Reservoir Computing and Self-Organized Neural Hierarchies", School of Engineering and Science, vol. PhD: Jacobs University Bremen, 2012. Abstract  Download: Mantas_Lukosevicius_PhD_thesis.pdf (3.13 MB)
Lukoševičius, M., and H. Jaeger, "Reservoir computing approaches to recurrent neural network training", Computer Science Review , vol. 3, no. 3, pp. 127-149, August, 2009.
Antonelo, E A., "Reservoir Computing Architectures for Modeling Robot Navigation Systems", Department of Electronic and Information Systems , vol. PhD, Gent, Universiteit Gent, 2011. Abstract  Download: PhD_Eric_final.pdf (21.33 MB)
Lukoševičius, M., H. Jaeger, and B. Schrauwen, "Reservoir Computing Trends", KI - Künstliche Intelligenz, pp. 1-7, 05/2012. Abstract  Download: KI_reservoir_computing.pdf (574.71 KB)
Verstraeten, D., "Reservoir Computing: computation with dynamical systems", Electronics and Information Systems, Gent, Ghent University, pp. 190, 2009.  Download: dvrstrae_organic.pdf (7.23 MB)
Bernacchia, A., H. Seo, D. Lee, and X-J. Wang, "A reservoir of time constants for memory traces in cortical neurons", Nature Neuroscience, vol. 14, no. 3, pp. 366-372, 02/2011.
Jaeger, H., "Reservoir Riddles: Sugggestions for Echo State Network Research (Extended Abstract of Invited Talk)", Proceedings of the International Joint Conference on Neural Networks, pp. 1460–1462, August, 2005.
Verstraeten, D., B. Schrauwen, and D. Stroobandt, "Reservoir-based techniques for speech recognition", Proceedings of the World Conference on Computational Intelligence, pp. 1050-1053, 2006.
Pfeiffer, M., B. Nessler, R. Douglas, and W. Maass, "Reward-modulated Hebbian Learning of Decision Making", Neural Computation, In Press. Abstract
Legenstein, R., 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", Journal of Neuroscience, vol. 30, no. 25, 2010. Abstract
Khamassi, M., S. Lallée, P. Enel, E. Procyk, and P. F. Dominey, "Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model", Frontiers in NeuroRobotics, 2011.  Download: FrontiersNeuroboticsPaper_v11_MK.pdf (1.89 MB)