Publications

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Natschlaeger, T., and W. Maass, "Information dynamics and emergent computation in recurrent circuits of spiking neurons", NIPS 2003: Advances in Neural Information Processing Systems, vol. 16: MIT Press, pp. 1255-1262, 2004, 2003. Abstract
Dambre, J., D. Verstraeten, B. Schrauwen, and S. Massar, "Information Processing Capacity of Dynamical Systems", Scientific Reports, vol. 2, no. 514, pp. 1 -- 7, 07/2012. Abstract  Download: srep00514.pdf (365.56 KB)
Legenstein, R., 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), vol. 14, no. 1-2, pp. 5-19, 2003. Abstract
Jaeger, H., W. Maass, and J. Principe, "Introduction to the Special Issue on Echo State Networks and Liquid State Machines", Neural Networks, vol. 20, no. 3, pp. 287–289, 2007.
Jones, B., D. Stekel, J. Rowe, and C. Fernando, "Is there a Liquid State Machine in the Bacterium Escherichia Coli?", IEEE Symposium on Artificial Life, 2007 (ALIFE '07), pp. 187-191, 2007.
Verstraeten, D., B. Schrauwen, and D. Stroobandt, "Isolated word recognition using a Liquid State Machine", Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN), Evere, D-side Publications, pp. 435–440, May, 2005.
Verstraeten, D., B. Schrauwen, D. Stroobandt, and J. Van Campenhout, "Isolated word recognition with the Liquid State Machine: a case study", Information Processing Letters, vol. 95, no. 6, pp. 521–528, 2005.
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Frank, S. L., "Learn more by training less: systematicity in sentence processing by recurrent networks", Connection Science, vol. 18, no. 3: Taylor & Francis, pp. 287–302, 2006. Abstract
Rao, Y., S. - P. Kim, J. Sanchez, D. Erdogmus, J. Principe, J. Carmena, M. Lebedev, and M. Nicolelis, "Learning Mappings in Brain Machine Interfaces with Echo State Networks", Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 233–236, 2005. Abstract
Antonelo, E A., and B. Schrauwen, "Learning Slow Features with Reservoir Computing for Biologically-inspired Robot Localization", Neural Networks, In Press. Abstract  Download: eric_nn2011.pdf (2.77 MB)
Legenstein, R., D. Pecevski, and W. Maass, "A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback", PLoS Computational Biology, vol. 4, no. 10, pp. 1-27, 2008. Abstract
Lallée, S., F. Warneken, and P. F. Dominey, "Learning to Collaborate by Observation", International Conference on Epigenetic Robotics, 2009. Abstract  Download: LWDepirob092pages.pdf (129.25 KB)
Lallée, S., F. Warneken, and P. F. Dominey, "Learning to Collaborate by Observation ", IEEE Humanoids Workshop on developmental psychology contributions to cooperative human robot interaction, 2009. Abstract  Download: Humanoids2009vLalleWarnekenDominey.pdf (805.03 KB)
Lallée, S., C. Madden, M. Hoen, and P F. Dominey, "Linking language with embodied and teleological representations of action for humanoid cognition", Frontiers in Neurorobotics, vol. 4, 2010.  Download: LAllee2010FN.pdf (1.83 MB)
Maass, W., "Liquid Computing", Computability in Europe 2007 - CiE'07: Springer (Berlin), 2007. Abstract
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Massar, M., and S. Massar, "Mean-field theory of echo state networks", PHYSICAL REVIEW E, vol. 87, 2013.  Download: 2013MassarMassarPhysRevE87_042809.pdf (490.19 KB)
Hermans, M., and B. Schrauwen, "Memory in linear neural networks in continuous time", Neural Networks, 2009.  Download: Paper.pdf (834.16 KB)
Hermans, M., and B. Schrauwen, "Memory in reservoirs for high dimensional input", IJCNN, Barcelona, Spain, 2010.
Kulkarni, M., and C. Teuscher, "Memristor-based Reservoir Computing", Proceedings of the IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH'12), pp. 226–232, 2012.
Maass, W., R. Legenstein, and N. Bertschinger, "Methods for estimating the computational power and generalization capability of neural microcircuits", NIPS 2004: Advances in Neural Information Processing Systems, vol. 17: MIT Press, pp. 865-872, 2005, 2004. Abstract
Li, J., and H. Jaeger, "Minimal Energy Control of an ESN Pattern Generator", Jacobs University Technical Report, no. 26: Jacobs University Bremen, 2011. Abstract
Skowronski, M. D., and J. G. Harris, "Minimum mean squared error time series classification using an echo state network prediction model", IEEE International Symposium on Circuits and Systems, 2006.
Antonelo, E A., B. Schrauwen, and D. Stroobandt, "Mobile Robot Control in the Road Sign Problem using Reservoir Computing Networks", IEEE Int. Conf. on Robotics and Automation (ICRA), 2008.
Maass, W., T. Natschlaeger, and H. Markram, "A model for real-time computation in generic neural microcircuits", NIPS 2002: Advances in Neural Information Processing Systems, vol. 15: MIT Press, pp. 213–220, 2003, 2002. Abstract
Kaske, A., and W. Maass, "A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models", Neural Networks, vol. 19, no. 5, pp. 600-609, 2006. Abstract