Abstract
Reservoir computing is a computational framework suited for sequential data processing, consisting of a reservoir part and a readout part. Not only theoretical and numerical studies on reservoir computing but also its implementation with physical devices have attracted much attention. In most studies, the reservoir part is constructed with identical units. However, a variability of physical units is inevitable, particularly when implemented with nano/micro devices. Here we numerically examine the effect of variability of reservoir units on computational performance. We show that the heterogeneity in reservoir units can be beneficial in reducing the prediction error in the reservoir computing system with a simple cycle reservoir.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach. GMD-Forschungszentrum Informationstechnik (2002)
Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European Symposium on Artificial Neural Networks, pp. 471–482 (2007)
LukošEvičIus, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. German National Research Center for Information Technology GMD Technical Report 148, Bonn, Germany 34 (2001)
Jaeger, H.: Short term memory in echo state networks. GMD-Forschungszentrum Informationstechnik (2001)
Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Advances in neural information processing systems, pp. 593–600 (2002)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)
Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., Massar, S.: Optoelectronic reservoir computing. Scientific Reports 2, 287 (2012)
Kulkarni, M.S., Teuscher, C.: Memristor-based reservoir computing. In: 2012 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp. 226–232 (2012)
Katayama, Y., Yamane, T., Nakano, D., Nakane, R., Tanaka, G.: Wave-based neuromorphic computing framework for brain-like energy efficiency and integration. IEEE Trans. Nanotechnology (Accepted)
Yamane, T., Katayama, Y., Nakane, R., Tanaka, G., Nakano, D.: Wave-based reservoir computing by synchronization of coupled oscillators. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) Neural Information Processing, pp. 198–205. Springer, Switzerland (2015)
Rodan, A., Tiňo, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131–144 (2011)
Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)
Wyffels, F., Schrauwen, B., Verstraeten, D., Stroobandt, D.: Band-pass reservoir computing. In: IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 3204–3209. IEEE (2008)
Weigend, A., Gershenfeld, N.: Time series prediction: forecasting the future and understanding the past. In: Proceedings of a NATO Advanced Research Workshop on Comparative Time Series Analysis, held in Santa Fe, New Mexico (1994)
Acknowledgments
This work was partially supported by JSPS KAKENHI Grant Number 16K00326 (GT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Tanaka, G. et al. (2016). Exploiting Heterogeneous Units for Reservoir Computing with Simple Architecture. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-46687-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46686-6
Online ISBN: 978-3-319-46687-3
eBook Packages: Computer ScienceComputer Science (R0)