Abstract
The digital spiking neuron consists of shift registers and behaves like a simplified neuron model. By adjusting wirings among the registers, the neuron can generate spike-trains with various characteristics. In this paper some analysis results on spike-train properties are outlined. Also a learning algorithm for the neuron is introduced and its application potentials are discussed.
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
H. Torikai and T. Saito, Synchronization phenomena in pulse-coupled networks driven by spike-train inputs, IEEE Trans. Neural Networks, vol. 15, no. 2, pp. 337–347, 2004.
G. Lee and N. H. Farhat, The bifurcating neuron network 2, Neural networks, vol.15, pp. 69–84, 2002.
R. Perez and L .Glass, Bistability, period doubling bifurcations and chaos in a periodically forced oscillator, Phys. Lett. vol.90A, no.9, pp. 441–443, 1982.
E. M. Izhikevich, Dynamical systems in neuroscience, MIT Press,2006.
J. J. Hopfield and A. V. M. Herz, Rapid local synchronization of action potentials: Toward computation with coupled integrate-and-fire neurons, Proc. Natl. Acad. Sci. USA, vol. 92, pp. 6655–6662, 1995.
S. R. Campbell, D. Wang and C. Jayaprakash, Synchrony and desynchrony in integrate-and-fire oscillators, Neural computation, vol. 11, pp. 1595–1619, 1999.
R. Eckhorn, Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models, IEEE Trans. Neural Networks, vol.10, no.3, pp. 464–479, 1999.
S. Wolfram, Universality and complexity in cellular automata, Pysica D, vol. 10, pp. 1–35, 1984.
L. O. Chua, S. Yoon and R. Dogaru, A nonlinear dynamics perspective of Wolfram’s new kind of science. Part I: threshold of complexity, Int. J. Bif. and Chaos, vol. 12, no.12, pp. 2655–2766, 2002.
T. Tamura, J. Kuroiwa and S. Nara, Errorless reproduction of given pattern dynamics by means of cellular automata, Physical Review E 68, 036707, 2003.
S. C. Kim and B. G Lee, A theory on sequence spaces and shift register generators, IEEE Trans. Comm. vol.44, no.5, pp. 609–618, 1996.
D. E. Knuth, The art of computer programming, vol. 2, 3rd edn., Addison Wesley, 1998.
H. Torikai, H. Hamanaka and T. Saito, Reconfigurable Digital Spiking Neuron and its Pulse-Coupled Network: Basic Characteristics and Potential Applications, IEEE Trans. CAS-II, vol. 53, no.8, pp. 734–738, 2006.
H. Torikai, Basic Characteristics and Learning Potential of a Digital Spiking Neuron, IEICE Trans. Fundamentals, 2007 (to appear).
H. Torikai, Y. Shimizu and T. Saito, Various spike-trains from a digital spiking neuron: analysis of inter-spike intervals and their modulation, Proc. of IJCNN, 2006, pp. 7591–7598.
H. Torikai, Y. Shimizu and T. Saito, A digital spiking neuron and its simple learning, Proc. of NOLTA pp. 263–266, 2006.
T. Kabe, H. Torikai and T. Saito, Synchronization via multiplex spike-trains in digital pulse-coupled networks, Lecture Note on Computational Science, 4234, Springer, Neural Information Processing, III, pp. 1141–1149 (2006). {\em The Best Student Paper Award of ICONIP2006.}
H. Torikai, A. Funew and T. Saito, Approximation of Spike-trains by Digital Spiking Neuron, Proc. of IJCNN, paper #1708, 2007.
G. M. Maggio, N. Rulkov and L. Reggiani, Pseudo-chaotic time hopping for UWB impulse radio, IEEE Trans. CAS-I, vol. 48, no. 12, pp. 1424–1435, 2001.
I. Oppermann, L. Stoica, A. Rabbachin, Z. Shelby and J. Haapola, UWB wireless sensor networks: UWEN - a practical example, IEEE Communications Magazine, vol. 42, no.12, pp. S27-S32, 2004.
M. G. Benedetto and G. Giancola, Understanding Ultra Wide Band Radio Fundamentals, Prentice Hall, 2004.
W. Chu and C. J. Colbourn, Sequence designs for ultra-wideband impulse radio with optimal correlation properties, IEEE Trans. Information Theory, vol. 50, no.10, pp. 2402–2407, 2004.
G. Bi and M. Poo, Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type, J. Neurosci., vol. 18, pp. 10464–10472, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Torikai, H. (2009). Learning of Digital Spiking Neuron and its Application Potentials. In: In, V., Longhini, P., Palacios, A. (eds) Applications of Nonlinear Dynamics. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85632-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-85632-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85631-3
Online ISBN: 978-3-540-85632-0
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)