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Simulation of Spike Wave Propagation and Two-to-one Communication with Dynamic Time Warping

1 Graduate School of Applied Informatics, University of Hyogo, Kobe 650-0047, Japan;
2 Dept. of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
3 NBL Technovator Co., Ltd., 631 Shindachimakino, Sennan 590-0522, Japan

Topical Section: Communication and Language: Theoretical Advances in Explaining Brain Mechanisms

Although intercommunication among the different areas of the brain is well known, the rules of communication in the brain are not clear. Many previous studies have examined the firing patterns of neural networks in general, while we have examined the involvement of the firing patterns of neural networks in communication. In order to understand information processing in the brain, we simulated the interactions of the firing activities of a large number of neural networks in a 25 × 25 two-dimensional array for analyzing spike behavior. We stimulated the transmitting neurons at 0.1 msec. Then we observed the generated spike propagation for 120 msec. In addition, the positions of the firing neurons were determined with spike waves for different variances in the temporal fluctuations of the neuronal characteristics. These results suggested that for the changes (diversity) in the propagation routes of neuronal transmission resulted from variance in synaptic propagation delays and refractory periods. The simulation was used to examine differences in the percentages of neurons with significantly larger test statistics and the variances in the synaptic delay and refractory period. These results suggested that multiplex communication was more stable if the synaptic delay and refractory period varied.
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Keywords Dynamic Time Warping; refractory period; spike wave; synaptic delay

Citation: Shun Sakuma, Yuko Mizuno-Matsumoto, Yoshi Nishitani, Shinichi Tamura. Simulation of Spike Wave Propagation and Two-to-one Communication with Dynamic Time Warping. AIMS Neuroscience, 2016, 3(4): 474-486. doi: 10.3934/Neuroscience.2016.4.474


  • 1. Eugene M. Izhikevich (2006) Polychronization: Computation with spikes. Neural Compu 18: 245-282.    
  • 2. Hiroyoshi Miyagawa, Masashi Inoue (2013) Biophysics of neurons. Maruzen Publishing.
  • 3. Donald Olding Hebb (1972) Textbook of Psychology, Philadelphia: Saunders, Pa, USA, 3rd edition.
  • 4. Anders Lansner (2009) Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations, Trends Neurosci 32: 178-186.
  • 5. Masato Okada (1996) Notions of Associative Memory and Sparse Coding, Neural Networks 9: 1429-1458.
  • 6. Yuval Aviell, David Horn, Moshe Abeles (2004) Synfire waves in small balanced networks. Neurcomputing 58-60: 123-127.    
  • 7. Takuma Tanaka, Takeshi Kaneko, Toshio Aoyagi (2009) Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity. Neural Computation 21: 1038-1067.    
  • 8. Yuko Mizuno-Matsumoto, Masatsugu Ishijima, Kazuhiro Shinosaki, et al. (2001) Transient Global Amnesia (TGA) in an MEG Study. Brain Topography 13: 269-274.    
  • 9. Yuko Mizuno-Matsumoto, Toshiki Yoshimine, Yasuo Nii, et al. (2001) Landau-Kleffner Syndrome: Localization of Epileptogenic lesion Using Wavelet-Crosscorrelation Analysis, Epilepsy Behavior 2: 288-294.
  • 10. Yuko Mizuno-Matsumoto, Satoshi Ukai, Ryosuke Ishii, et al. (2005) Wavelet-crosscorrelation analysis: Non-stationary analysis of neurophysiological signals. Brain Topogr 17: 237-252.    
  • 11. Shinichi Tamura, Shigenori Nakano, Kozo Okazaki (1985) Optical code-multiplex transmission by Gold-sequences, IEEE/OSA J. Lightwave Tech 1: 21-127.
  • 12. Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, et al. (2012) Detection of M-sequences from spike sequence in neuronal networks. Computational Intelligence and Neuroscience 2012, Article ID 862579, 9 pages. doi:10.1155/2012/862579.
  • 13. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, et al. (2016) Spike code flow in cultured neuronal networks. Computational Intelligence and Neuroscience 2016, Article ID 7267691, 11 pages. http://dx.doi.org/10.1155/2016/7267691.
  • 14. Shinichi Tamura, Yoshi Nishitani, Yakuya Kamimura, et al. (2013) Multiplexed Spatiotemporal Communication Model in Artificial Neural Networks. Auto Control Intelligent System 1: 121-130. doi: 10.11648/j.acis.20130106.11.    
  • 15. Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, et al. (2016) Variance of spatiotemporal spiking patterns by different stimulated neurons in cultured neuronal networks. Int J Academic Res Reflect 4: 11-19.
  • 16. Müller Meinard (2007) Information Retrieval for Music and Motion, Springer 69-84.
  • 17. Wulfram Gerstner, Werner M. Kistler (2002) Spiking Neuron Models. Single Neurons, Populations, Plasticity, Cambridge University Press.
  • 18. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, et al. (2016) Simulation of code spectrum and code flow of cultured neuronal networks. Comput Intell Neurosci 2016, Article ID 7186092, 12 pages.
  • 19. Shinichi Tamura Yoshi Nishitani, Chie Hosokawa (2016) Feasibility of multiplex communication in a 2D mesh asynchronous neural network with fluctuations. AIMS Neurosci 3: 385-397. DOI: 10.3934/Neuroscience.2016.4.385.    


This article has been cited by

  • 1. Shun Sakuma, Yuko Mizuno-Matsumoto, Yoshi Nishitani, Shinichi Tamura, Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network, AIMS Neuroscience, 2017, 4, 4, 238, 10.3934/Neuroscience.2017.4.238

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Copyright Info: 2016, Shun Sakuma, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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