It is well known that there is intercommunication among the different areas of the brain. However, till date, the rules of communication have not been successfully analyzed. The spike trains from neuronal cells have been simply treated as density-modulated waves with an activation level of the corresponding neuronal cells, or, at most, they have been analyzed using traditional metrics between sequences. The spike trains from neuronal cells have a random-like pattern that provides few clues regarding a coding rule. Here in a randomly generated artificial 3 × 3 multiplexed spatiotemporal communication neural network composed of threshold elements, we showed that pseudorandom sequences were generated during the simulation, similar to the random sequences generated by the cultured neural network of the rat brain. The transiently generated sequence patterns in the simulation were regarded as reflecting the circuit structure. These randomly shaped circuits generated pseudorandom sequences that functioned as codes for multiplexing communication. Although the circuit weights are randomly generated at present, it will be possible to extend this approach to determine the network weights by learning. This paper provides simulation results that support findings on cultured neural network.
Published in | Automation, Control and Intelligent Systems (Volume 1, Issue 6) |
DOI | 10.11648/j.acis.20130106.11 |
Page(s) | 121-130 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
M-Sequence, Neural Network, Pseudo Random Sequence, Spatiotemporal Communication, Spike Train
[1] | Y. Mizuno-Matsumoto, K. Okazaki, A. Kato, T. Yoshimine, Y. Sato, S. Tamura, and T. Hayakawa., "Visualization of epileptogenic phenomena using cross-correlation analysis: Localization of epileptic foci and propagation of epileptiform discharges," IEEE Trans. Biomed. Eng. 46, pp.271-279, 1999. |
[2] | Y. Mizuno-Matsumoto, M. Ishijima, K. Shinosaki, T. Nishikawa, S. Ukai, Y. Ikejiri, Y. Nakagawa, R. Ishii, H. Tokunaga, S. Tamura, S. Date, T. Inouye, S. Shimojo, and M. Takeda, "Transient Global Amnesia (TGA) in an MEG Study," Brain Topography 13, pp.269-274, 2001. |
[3] | B. Cessac, H. Paugam-Moisy, T. Viéville, "Overview of facts and issues about neural coding by spikes," J. Physiol.-Paris 104, (1-2), pp.5-18, 2010. |
[4] | O. Kliper, D. Horn, B. Quenet, and G. Dror, "Analysis of spatiotemporal patterns in a model of olfaction," Neurocomputing 58-60, pp.1027-1032, 2004. |
[5] | K. Fujita, Y. Kashimori, and T. Kambara, "Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli," Biol. Cybern., 97, pp.293-305, 2007. DOI 10.1007/s00422-007-0175-z |
[6] | I. Tyukin, T. Tyukina, and C. van Leeuwen, "Invariant template matching in systems with spatiotemporal coding: A matter of instability," Neural Networks 22, pp.425-449, 2009. |
[7] | A. Mohemmed, S. Schliebs, S. Matsuda, and N. Kasabov, "Training spiking neural networks to associate spatio-temporal input–output spike patterns," Neurocomputing 107, pp.3-10, 2013. |
[8] | B.A. Olshausen and D.J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature 381, pp.607-609, 1996. |
[9] | A. Bell and T. Sejnowski, "The independent components of natural scenes are edge filters," Vision Research 37, pp.3327-3338, 1997. |
[10] | S. Tamura, Y. Mizuno-Matsumoto, Y-W. Chen, and K. Nakamura, "Association and abstraction on neural circuit loop and coding," The Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP2009) A10-07(No.546),Kyoto, September 12-14, 2009. Available: IEEE XPlore |
[11] | Y. Nishitani, C. Hosokawa, Y. Mizuno-Matsumoto, T. Miyoshi, H. Sawai, and S. Tamura "Detection of M-Sequences from Spike Sequence in Neuronal Networks," Computational Intelligence and Neuroscience 2012, Article ID 862579, 9 pages, 2012. doi:10.1155/2012/862579. |
[12] | S. Tamura, Y. Nishitani, C. Hosokawa, Y. Mizuno-Matsumoto, T. Kamimura, Y-W. Chen, T. Miyoshi, and H. Sawai, "M-sequence family from cultured neural circuits," The 3rd Int'l Workshop on Computational Intelligence for Bio-Medical Science and Engineering (CIMSE-2012), Taipei, October 23-25, 2012. |
[13] | S. Tamura, Y. Nishitani, C. Hosokawa, Y. Mizuno-Matsumoto, T. Kamimura, Y-W. Chen, T. Miyoshi, and H. Sawai, "Pseudo random sequences from neural circuits," IFMIA 2012, Daejeon, November 16-17, 2012. |
[14] | R.C. Dixon, Spread Spectrum Systems, John Wiley & Sons Inc., 1976. |
[15] | D.V. Sarwate and M. B. Pursley, "Crosscorrelation properties of pseudorandom and related sequences," Proc. IEEE 68, pp.593-619, 1980. |
[16] | S. Tamura, S. Nakano, and K. Okazaki, "Optical code-multiplex transmission by Gold-sequences," IEEE/OSA J. Lightwave Tech. 1, No.3, pp.121-127, 1985. |
[17] | S.W. Golomb and G. Gong, Signal Design for Good Correlation: For Wireless Communication, Cryptography, and Rader, Cambridge University Press, 2005. |
[18] | P. Fromherz and V. Gaede, "Exclusive-OR function of single arborized neuron," Biol Cybern 69, pp.337-344, 1993. |
[19] | C. Lecerf, "The double loop as a model of a learning neural system," Cybernetics and Informatics 1, pp.587-594, 1998. |
[20] | Y. Choe, "Analogical cascade: A theory on the role of the thalamo-cortical loop in brain function," Neurocomputing 52-54, pp.713-719, 2003. |
[21] | T. Kamimura, K. Nakamura, K. Yoneda, Y-W. Chen, Y. Mizuno-Matsumoto, T. Miyoshi, H. Sawai, and S. Tamura, "Information communication in brain based on memory loop neural circuit," ICIS2010 & SEDM2010, Chengdu, June 23-25, 2010. Available: IEEE Xplore |
[22] | T. Kamimura, Y-W. Chen, Y. Yagi, and S. Tamura, "Learning of Loop Neural Circuit for Memory," CIMSE2011, ICCIT2011, Jeju, November 29-December 1, 2011. Available: IEEE Xplore |
[23] | M. Abeles, Local Cortical Circuits: An Electrophysiological Study, Springer, Berlin, 1982. |
[24] | M. Abeles, "Synfire chains," Scholarpedia, 4(7), pp.1441, 2009. |
APA Style
Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. (2014). Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Automation, Control and Intelligent Systems, 1(6), 121-130. https://doi.org/10.11648/j.acis.20130106.11
ACS Style
Shinichi Tamura; Yoshi Nishitani; Takuya Kamimura; Yasushi Yagi; Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom. Control Intell. Syst. 2014, 1(6), 121-130. doi: 10.11648/j.acis.20130106.11
AMA Style
Shinichi Tamura, Yoshi Nishitani, Takuya Kamimura, Yasushi Yagi, Chie Hosokawa, et al. Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Autom Control Intell Syst. 2014;1(6):121-130. doi: 10.11648/j.acis.20130106.11
@article{10.11648/j.acis.20130106.11, author = {Shinichi Tamura and Yoshi Nishitani and Takuya Kamimura and Yasushi Yagi and Chie Hosokawa and Tomomitsu Miyoshi and Hajime Sawai and Yuko Mizuno-Matsumoto and Yen-Wei Chen}, title = {Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks}, journal = {Automation, Control and Intelligent Systems}, volume = {1}, number = {6}, pages = {121-130}, doi = {10.11648/j.acis.20130106.11}, url = {https://doi.org/10.11648/j.acis.20130106.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130106.11}, abstract = {It is well known that there is intercommunication among the different areas of the brain. However, till date, the rules of communication have not been successfully analyzed. The spike trains from neuronal cells have been simply treated as density-modulated waves with an activation level of the corresponding neuronal cells, or, at most, they have been analyzed using traditional metrics between sequences. The spike trains from neuronal cells have a random-like pattern that provides few clues regarding a coding rule. Here in a randomly generated artificial 3 × 3 multiplexed spatiotemporal communication neural network composed of threshold elements, we showed that pseudorandom sequences were generated during the simulation, similar to the random sequences generated by the cultured neural network of the rat brain. The transiently generated sequence patterns in the simulation were regarded as reflecting the circuit structure. These randomly shaped circuits generated pseudorandom sequences that functioned as codes for multiplexing communication. Although the circuit weights are randomly generated at present, it will be possible to extend this approach to determine the network weights by learning. This paper provides simulation results that support findings on cultured neural network.}, year = {2014} }
TY - JOUR T1 - Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks AU - Shinichi Tamura AU - Yoshi Nishitani AU - Takuya Kamimura AU - Yasushi Yagi AU - Chie Hosokawa AU - Tomomitsu Miyoshi AU - Hajime Sawai AU - Yuko Mizuno-Matsumoto AU - Yen-Wei Chen Y1 - 2014/01/30 PY - 2014 N1 - https://doi.org/10.11648/j.acis.20130106.11 DO - 10.11648/j.acis.20130106.11 T2 - Automation, Control and Intelligent Systems JF - Automation, Control and Intelligent Systems JO - Automation, Control and Intelligent Systems SP - 121 EP - 130 PB - Science Publishing Group SN - 2328-5591 UR - https://doi.org/10.11648/j.acis.20130106.11 AB - It is well known that there is intercommunication among the different areas of the brain. However, till date, the rules of communication have not been successfully analyzed. The spike trains from neuronal cells have been simply treated as density-modulated waves with an activation level of the corresponding neuronal cells, or, at most, they have been analyzed using traditional metrics between sequences. The spike trains from neuronal cells have a random-like pattern that provides few clues regarding a coding rule. Here in a randomly generated artificial 3 × 3 multiplexed spatiotemporal communication neural network composed of threshold elements, we showed that pseudorandom sequences were generated during the simulation, similar to the random sequences generated by the cultured neural network of the rat brain. The transiently generated sequence patterns in the simulation were regarded as reflecting the circuit structure. These randomly shaped circuits generated pseudorandom sequences that functioned as codes for multiplexing communication. Although the circuit weights are randomly generated at present, it will be possible to extend this approach to determine the network weights by learning. This paper provides simulation results that support findings on cultured neural network. VL - 1 IS - 6 ER -