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Verstraeten, D., B. Schrauwen, S. Dieleman, P. Brakel, P. Buteneers, and D. Pecevski, "Oger: Modular Learning Architectures For Large-Scale Sequential Processing", Journal of Machine Learning Research, Submitted.  Download: Verstraeten11.pdf (186.23 KB)
Schrauwen, B., L. Buesing, and R. Legenstein, "On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing", NIPS: Vancouver, 2008. Abstract
Vreeken, J., On real-world temporal pattern recognition using Liquid State Machines, : Institute for Information and Computing Sciences, Utrecht University, Submitted.
Lukoševičius, M., On self-organizing reservoirs and their hierarchies, , no. 25: Jacobs University Bremen, October, 2010.
Maass, W., "On the computational power of neural microcircuit models: Pointers to the literature", International Conference on Artificial Neural Networks - ICANN 2002, vol. 2415: Springer, pp. 254-256, 2002.
Maass, W., and H. Markram, "On the computational power of recurrent circuits of spiking neurons", Journal of Computer and System Sciences, vol. 69, no. 4, pp. 593-616, 2004. Abstract
Hermans, M., and B. Schrauwen, "One Step Backpropagation through time for learning input mapping in reservoir computing applied to speech recognition", ISCAS 2010, Paris, 2010.
Steil, J. J., "Online Reservoir Adaptation by Intrinsic Plasticity for Backpropagation-Decorrelation and Echo State Learning", Neural Networks, vol. 20, no. 3, pp. 353 - 364, 2007.
Jaeger, H., M. Lukoševičius, D. Popovici, and U. Siewert, "Optimization and applications of echo state networks with leaky-integrator neurons", Neural Networks, vol. 20, no. 3, pp. 335-352, 2007.