Self-organized Reservoirs and Their Hierarchies

1. Chappell, G.J., Taylor, J.G.: The temporal Kohonen map. Neural Networks 6(3), 441–445 (1993)

   Article  Google Scholar 

2. Erhan, D., Bengio, Y., Courville, A.C., Manzagol, P.-A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11, 625–660 (2010)

   MathSciNet  MATH  Google Scholar 

3. Farkaš, I., Crocker, M.W.: Systematicity in sentence processing with a recursive self-organizing neural network. In: Proceedings of ESANN 2007, pp. 49–54 (2007)

   Google Scholar 

4. Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17(8-9), 1061–1085 (2004)

   Article  MATH  Google Scholar 

5. Jaeger, H.: Echo state network. Scholarpedia 2(9), 2330 (2007)

   Article  Google Scholar 

6. Jaeger, H., Lukoševičius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Networks 20(3), 335–352 (2007)

   Article  MATH  Google Scholar 

7. Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: Proceedings of ICCV 2009. IEEE (2009)

   Google Scholar 

8. Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia 2(1), 1568 (2007)

   Article  Google Scholar 

9. Koutník, J.: Inductive modelling of temporal sequences by means of self-organization. In: Proceeding of IWIM 2007, pp. 269–277. CTU in Prague (2007)

   Google Scholar 

10. Lazar, A., Pipa, G., Triesch, J.: SORN: a self-organizing recurrent neural network. Frontiers in Computational Neuroscience 4, 12 (2010)

    Google Scholar 

11. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4), 541–551 (1989)

    Article  Google Scholar 

12. Lukoševičius, M.: Reservoir Computing and Self-Organized Neural Hierarchies. PhD thesis, Jacobs University Bremen, Bremen, Germany (2011)

    Google Scholar 

13. Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)

    Article  Google Scholar 

14. Mak, M.W.: A learning algorithm for recurrent radial basis function networks. Neural Processing Letters 2(1), 27–31 (1995)

    Article  MathSciNet  Google Scholar 

15. Martinetz, T., Schulten, K.: A “neural-gas” network learns topologies. Artificial Neural Networks 1, 397–402 (1991)

    Google Scholar 

16. Rahimi, A., Recht, B.: Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. In: Proceedings of NIPS 2008, pp. 1313–1320. MIT Press, Cambridge (2009)

    Google Scholar 

17. Saxe, A., Koh, P., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: Proceedings of ICML 2011 (2011)

    Google Scholar 

18. Tiňo, P., Farkaš, I., van Mourik, J.: Dynamics and topographic organization of recursive self-organizing maps. Neural Computation 18(10), 2529–2567 (2006)

    Article  MathSciNet  MATH  Google Scholar 

19. Varsta, M., Millán, J.D.R., Heikkonen, J.: A Recurrent Self-Organizing Map for Temporal Sequence Processing. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 421–426. Springer, Heidelberg (1997)

    Google Scholar 

20. Voegtlin, T.: Recursive self-organizing maps. Neural Networks 15(8-9), 979–991 (2002)

    Article  Google Scholar 

Download references