Research article Recurring Topics

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

  • Received: 21 September 2016 Accepted: 30 November 2016 Published: 07 December 2016
  • 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.

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

    Related Papers:

  • 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.


    加载中
    [1] Eugene M. Izhikevich (2006) Polychronization: Computation with spikes. Neural Compu 18: 245-282. doi: 10.1162/089976606775093882
    [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. doi: 10.1016/j.neucom.2004.01.032
    [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. doi: 10.1162/neco.2008.03-08-727
    [8] Yuko Mizuno-Matsumoto, Masatsugu Ishijima, Kazuhiro Shinosaki, et al. (2001) Transient Global Amnesia (TGA) in an MEG Study. Brain Topography 13: 269-274. doi: 10.1023/A:1011176612377
    [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. doi: 10.1007/s10548-005-6032-2
    [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. 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. doi: 10.3934/Neuroscience.2016.4.385
  • Reader Comments
  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3837) PDF downloads(1254) Cited by(2)

Article outline

Figures and Tables

Figures(11)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog