Memory in reservoirs for high dimensional input

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Hermans, Michiel ^a   Schrauwen, Benjamin ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

MAPPING;

DYNAMICAL PROPERTIES; FADING MEMORY; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; INTERNAL STATE; LINEAR MAPPING; MEMORY PROPERTIES; RANDOM MAPPINGS; RECURRENT NETWORKS; RESERVOIR COMPUTING;

RECURRENT NEURAL NETWORKS;

EID: 79959399630     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596884     Document Type: Conference Paper

References (15)

1. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities." Proc Natl Acad Sci U S A, vol. 79, no. 8, pp. 2554-2558, Apr 1982.
2. D. Rumelhart, G. Hinton, and R. Williams, Learning internal representations by error propagation. MIT Press, Cambridge, MA, 1986.
3. B. Hammer and J. J. Steil, "Perspectives on learning with recurrent neural networks," in Proceedings of the European Symposium on Artificial Neural Networks (ESANN), 2002.
4. H. Jaeger, "The "echo state" approach to analysing and training recurrent neural networks," German National Research Center for Information Technology, Tech. Rep. GMD Report 148, 2001.
5. W. Maass, T. Natschläger, 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.
6. D. Verstraeten, B. Schrauwen, and J. Van Campenhout, "Recognition of isolated digits using a liquid state machine," in Proceedings of SPS-DARTS 2005, April 2005, pp. 135-138.
7. B. Fontaine, H. Peremans, and B. Schrauwen, "Bat echolocation modelling using spike kernels with Support Vector Regression," in Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007, pp. 367-372.
8. A. Graves and J. Schmidhuber, "Offline handwriting recognition with multidimensional recurrent neural networks," in Advances in Neural Information Processing Systems 21, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., 2009, pre-publication.
9. S. Ganguli, D. Huh, and H. Sompolinsky, "Memory traces in dynamical systems." Proceedings of the National Academy of Sciences of the United States of America, November 2008.
10. M. Hermans and B. Schrauwen, "Memory in linear recurrent neural networks in continuous time," Neural Networks, (doi:10.1016/j.neunet.2009. 08.008), 2009.
11. H. Jaeger, "Short term memory in echo state networks," German National Research Center for Information Technology, Tech. Rep. GMD Report 152, 2001.
12. O. L. White, D. D. Lee, and H. Sompolinsky, "Short-term memory in orthogonal neural networks," Physical Review Letters, vol. 92, p. 148102, 2004.
13. M. C. Ozturk, D. Xu, and J. C. Principe, "Analysis and design of echo state networks," Neural Computation, vol. 19, pp. 111-138, 2006.
14. D. Verstraeten and B. Schrauwen, "On the quantification of dynamics in reservoir computing," in Proceedings of the International Conference on Analog Neural Networks (ICANN), 2009, pp. 985-994, (submitted).
15. E. Oja, "Simplified neuron model as a principal component analyzer," Journal of Mathematical Biology, vol. 15, no. 3, pp. 267-273, 1982.