Abstract
In this paper, we address the issues in representation of continuous valued variables by firing times of neurons in the spiking neural network used for clustering multi-variate data. The existing range-based encoding method encodes each dimension separately. This method does not make use of the correlation among the different variables, and the knowledge of the distribution of data. We propose a region-based encoding method that places multi-dimensional Gaussian receptive fields in the data-inhabited regions, and captures the correlation among the variables. Effectiveness of the proposed encoding method in clustering the complex 2-dimensional and 3-dimensional data sets is demonstrated.
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
Preview
Unable to display preview. Download preview PDF.
References
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Englewood Cliffs (1998)
Kumar, S.: Neural Networks: A Classroom Approach. Tata McGraw-Hill, New Delhi (2004)
Maass, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Trans. Soc. Comput. Simul. Int. 14(4), 1659–1671 (1997)
Bi, Q., Poo, M.: Precise Spike Timing Determines the Direction and Extent of Synaptic Modifications in Cultured Hippocampal Neurons. Neuroscience 18, 10464–10472 (1998)
Maass, W., Bishop, C.M.: Pulsed Neural Networks. MIT-Press, London (1999)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)
Maass, W.: Fast Sigmoidal Networks via Spiking Neurons. Neural Computation 9, 279–304 (1997)
Verstraeten, D., Schrauwen, B., Stroobandt, D., Campenhout, J.V.: Isolated Word Recognition with the Liquid State Machine: A Case Study. Information Processing Letters 95(6), 521–528 (2005)
Bohte, S.M., Kok, J.N., Poutre, H.L.: Spike-Prop: Error-backpropagation in Temporally Encoded Networks of Spiking Neurons. Neural Computation 48, 17–37 (2002)
Natschlager, T., Ruf, B.: Spatial and Temporal Pattern Analysis via Spiking Neurons. Network: Comp. Neural Systems 9, 319–332 (1998)
Ruf, B., Schmitt, M.: Unsupervised Learning in Networks of Spiking Neurons using Temporal Coding. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 361–366. Springer, Heidelberg (1997)
Hopfield, J.J.: Pattern Recognition Computation using Action Potential Timing for Stimulus Representations. Nature 376, 33–36 (1995)
Gerstner, W., Kempter, R., Van Hemmen, J.L., Wagner, H.: A Neuronal Learning Rule for Sub-millisecond Temporal Coding. Nature 383, 76–78 (1996)
Bohte, S.M., Poutre, H.L., Kok, J.N.: Unsupervised Clustering with Spiking Neurons by Sparse Temporal Coding and Multilayer RBF Networks. IEEE Transactions on Neural Networks 13, 426–435 (2002)
Panuku, L.N., Sekhar, C.C.: Clustering of Nonlinearly Separable Data using Spiking Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4668, Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Panuku, L.N., Sekhar, C.C. (2008). Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-69158-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
Online ISBN: 978-3-540-69158-7
eBook Packages: Computer ScienceComputer Science (R0)