Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

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.2018.1.18

AIMS Neuroscience

2018, Volume 5, Issue 1: 18-31. doi: 10.3934/Neuroscience.2018.1.18

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Research article

Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network

1.
Department of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
2.
Biomedical Research Institute and Advanced Photonics and Biosensing Open Innovation Laboratory, AIST, Ikeda, Osaka 563-8577, Japan
3.
Graduate School of Applied Informatics, University of Hyogo, Kobe 650-0044, Japan
4.
Department of Integrative Physiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
5.
NBL Technovator Co., Ltd., Sennan 590-0522, Japan

Received: 26 June 2017 Accepted: 15 October 2017 Published: 25 December 2017

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested.
- cultured neuronal network,
- spike wave propagation,
- adjacent neurons,
- identifying communications,
- microelectrode array
Citation: Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Shinichi Tamura. Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network[J]. AIMS Neuroscience, 2018, 5(1): 18-31. doi: 10.3934/Neuroscience.2018.1.18

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Abstract

Neuronal networks have fluctuating characteristics, unlike the stable characteristics seen in computers. The underlying mechanisms that drive reliable communication among neuronal networks and their ability to perform intelligible tasks remain unknown. Recently, in an attempt to resolve this issue, we showed that stimulated neurons communicate via spikes that propagate temporally, in the form of spike trains. We named this phenomenon “spike wave propagation”. In these previous studies, using neural networks cultured from rat hippocampal neurons, we found that multiple neurons, e.g., 3 neurons, correlate to identify various spike wave propagations in a cultured neuronal network. Specifically, the number of classifiable neurons in the neuronal network increased through correlation of spike trains between current and adjacent neurons. Although we previously obtained similar findings through stimulation, here we report these observations on a physiological level. Considering that individual spike wave propagation corresponds to individual communication, a correlation between some adjacent neurons to improve the quality of communication classification in a neuronal network, similar to a diversity antenna, which is used to improve the quality of communication in artificial data communication systems, is suggested.

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