Optimizing recurrent reservoirs with neuro-evolution

Author:

Otte Sebastian,Butz Martin V.,Koryakin Danil,Becker Fabian,Liwicki Marcus,Zell Andreas

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference21 articles.

1. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots;Dasgupta;Front. Neurorobot.,2015

2. B.G. Woolley, K.O. Stanley, Evolving a single scalable controller for an octopus arm with a variable number of segments, In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part II, PPSN׳10, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 270–279.

3. V. Mnih, N. Heess, A. Graves, K. Kavukcuoglu, Recurrent models of visual attention, In: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, Curran Associates, Inc., Montreal, Canada, 2014, pp. 2204–2212.

4. A. Graves, Generating sequences with recurrent neural networks, arXiv:1308.0850 [cs]

5. S. Otte, F. Becker, M.V. Butz, M. Liwicki, A. Zell, Learning recurrent dynamics with differential evolution, In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2015.

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