Classification of Spike Wave Propagations in a Cultured Neuronal Network: Investigating a Brain Communication Mechanism

Yoshi Nishitani; Chie Hosokawa; Yuko Mizuno-Matsumoto; Tomomitsu Miyoshi; Shinichi Tamura; Yoshi Nishitani; Chie Hosokawa; Yuko Mizuno-Matsumoto; Tomomitsu Miyoshi; Shinichi Tamura

doi:10.3934/Neuroscience.2017.1.1

AIMS Neuroscience

2017, Volume 4, Issue 1: 1-13. doi: 10.3934/Neuroscience.2017.1.1

Previous Article Next Article

Research article Recurring Topics

Classification of Spike Wave Propagations in a Cultured Neuronal Network: Investigating a Brain Communication Mechanism

1.
Dept. of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
2.
Biomedical Research Institute, AIST, Ikeda, Osaka 563-8577, Japan;
3.
Graduate School of Applied Informatics, University of Hyogo, Kobe 650-0044, Japan;
4.
Dept. of Integrative Physiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan;
5.
NBL Technovator Co., Ltd., 631 Shindachimakino, Sennan 590-0522, Japan

Received: 22 September 2016 Accepted: 01 December 2016 Published: 26 December 2016

In brain information science, it is still unclear how multiple data can be stored and transmitted in ambiguously behaving neuronal networks. In the present study, we analyze the spatiotemporal propagation of spike trains in neuronal networks. Recently, spike propagation was observed functioning as a cluster of excitation waves (spike wave propagation) in cultured neuronal networks. We now assume that spike wave propagations are just events of communications in the brain. However, in reality, various spike wave propagations are generated in neuronal networks. Thus, there should be some mechanism to classify these spike wave propagations so that multiple communications in brain can be distinguished. To prove this assumption, we attempt to classify various spike wave propagations generated from different stimulated neurons using our original spatiotemporal pattern matching method for spike temporal patterns at each neuron in spike wave propagation in the cultured neuronal network. Based on the experimental results, it became clear that spike wave propagations have various temporal patterns from stimulated neurons. Therefore these stimulated neurons could be classified at several neurons away from the stimulated neurons. These are the classifiable neurons. Moreover, distribution of classifiable neurons in a network is also different when stimulated neurons generating spike wave propagations are different. These results suggest that distinct communications occur via multiple communication links and that classifiable neurons serve this function.
- cultured neuronal network,
- spike wave propagation,
- spatiotemporal form,
- classifying,
- multiple communications
Citation: Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Shinichi Tamura. Classification of Spike Wave Propagations in a Cultured Neuronal Network: Investigating a Brain Communication Mechanism[J]. AIMS Neuroscience, 2017, 4(1): 1-13. doi: 10.3934/Neuroscience.2017.1.1

Related Papers:

Abstract

In brain information science, it is still unclear how multiple data can be stored and transmitted in ambiguously behaving neuronal networks. In the present study, we analyze the spatiotemporal propagation of spike trains in neuronal networks. Recently, spike propagation was observed functioning as a cluster of excitation waves (spike wave propagation) in cultured neuronal networks. We now assume that spike wave propagations are just events of communications in the brain. However, in reality, various spike wave propagations are generated in neuronal networks. Thus, there should be some mechanism to classify these spike wave propagations so that multiple communications in brain can be distinguished. To prove this assumption, we attempt to classify various spike wave propagations generated from different stimulated neurons using our original spatiotemporal pattern matching method for spike temporal patterns at each neuron in spike wave propagation in the cultured neuronal network. Based on the experimental results, it became clear that spike wave propagations have various temporal patterns from stimulated neurons. Therefore these stimulated neurons could be classified at several neurons away from the stimulated neurons. These are the classifiable neurons. Moreover, distribution of classifiable neurons in a network is also different when stimulated neurons generating spike wave propagations are different. These results suggest that distinct communications occur via multiple communication links and that classifiable neurons serve this function.

References

[1]	Bonifazi P, Goldin M, Picardo MA, et al. (2009) GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks. Science 326: 1419-1424. doi: 10.1126/science.1175509
[2]	Lecerf C (1998) The double loop as a model of a learning neural system. Proceedings World Multiconference on Systemics. Cybernetics Informatics 1: 587-594.
[3]	Choe Y (2003) Analogical Cascade: A Theory on the Role of the Thalamo-Cortical Loop in Brain Function. Neurocomputing 1: 52-54.
[4]	Tamura S, Mizuno-Matsumoto Y, Chen YW, et al. (2009) Association and abstraction on neural circuit loop and coding. 5th Int’l Conf. Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP2009) 10-07.546 (appears in IEEE Xplore).
[5]	Thorpre S, Fize D, Marlot C, et al. (1996) Speed of processing in the human visual system. Nature 381: 520-522. doi: 10.1038/381520a0
[6]	Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity computation and information coding. J Neurosci 18: 3870-3896.
[7]	Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Letters Nat 381: 607-609. doi: 10.1038/381607a0
[8]	Bell, Sejnowski T (1997) The independent components of natural scenes are edge filters. Vision Res 37: 3327-3338. doi: 10.1016/S0042-6989(97)00121-1
[9]	Klipera, Hornb D, Quene B (2005) The inertial-DNF model: spatiotemporal coding on two time scales. Neurocomputing 65-66: 543-548. doi: 10.1016/j.neucom.2004.10.046
[10]	Takahashi K, Kim S, Coleman TP, et al. (2015) Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex. Nat Commu 6: 7169. DOI:10.1038/ncomms8169. Available from: http://www.nature.com/ncomms/2015/150521/ncomms8169/full/ncomms8169.html doi: 10.1038/ncomms8169
[11]	Aviel Y, Horn D, Abeles M (2004) Synfire waves in small balanced networks. Neural Computation 58-60: 123-127.
[12]	Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2012) Detection of M-sequences from spike sequence in neuronal networks. Comput Intell Neurosci. Article ID, 862579: 1-9
[13]	Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2014) Synchronized Code Sequences from Spike Trains in Cultured Neuronal Networks. Int J Engineer Industries 5: 13-24.
[14]	Tamura S, Nishitani Y, Kamimura T, et al. (2013) Multiplexed spatiotemporal communication model in artificial neural networks. Auto Control Intell Systems 1: 121-130. DOI: 10.11648/j.acis.20130106.11
[15]	Tamura S, Nishitani Y, Hosokawa C, et al. (2016) Simulation of code spectrum and code flow of cultured neuronal networks. Compu Intell Neurosci 7186092: 1-12.
[16]	Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2016) Variance of spatiotemporal spiking patterns by different stimulated neurons in cultured neuronal networks. Int J Acade Res Reflect 4: 11-19.
[17]	Shinichi Tamura Yoshi Nishitani, Chie Hosokawa (2016) Feasibility of multiplex communication in 2D mesh asynchronous neural network with fluctuations. AIMS Neurosci 3: 385-397. doi: 10.3934/Neuroscience.2016.4.385
[18]	Wagenaar DA, Pine J, Potter SM (2004) Effective parameters for stimulation of dissociated cultures using multi-electrode arrays. J Neurosci Method 138: 27-37. doi: 10.1016/j.jneumeth.2004.03.005
[19]	Muller M (2007) Dynamic Time Warping. In: Information Retrieval for Music and Motion, Springer.
[20]	Mei J, Liu M, Wang YF, et al. (2015) Learning a Mahalanobis Distance based Dynamic Time Warping Measure for Multivariate Time Series Classification. IEEE_cybernetics Available from: https://www.google.co.jp/?gws_rd=ssl#q=dtw+weakpoint
[21]	Rivlin-Etzion M, Ritov Y, Heimer G, et al. (2006) Local shuffling of spike trains boosts the accuracy of spike train spectral analysis. J Neurophysiol 95: 3245-3256. doi: 10.1152/jn.00055.2005

Reader Comments

Your name:*

Email:*
© 2017 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)