Learning process for identifying different types of communication via repetitive stimulation: feasibility study 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.2019.4.240

AIMS Neuroscience

2019, Volume 6, Issue 4: 240-249. doi: 10.3934/Neuroscience.2019.4.240

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Research article

Learning process for identifying different types of communication via repetitive stimulation: feasibility study in a cultured neuronal network

1.
Department of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
2.
Graduate School of Science Osaka City University, Osaka, 558-8585, 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., 631 Shindachimakino, Sennan 590-0522, Japan

Received: 04 July 2019 Accepted: 23 September 2019 Published: 16 October 2019

It is well known that various types of information can be learned and memorized via repetitive training. In brain information science, it is very important to determine how neuronal networks comprising neurons with fluctuating characteristics reliably learn and memorize information. The aim of this study is to investigate the learning process in cultured neuronal networks and to address the question described above. Previously, we reported that the spikes resulting from stimulation at a specific neuron propagate as a cluster of excitation waves called spike wave propagation in cultured neuronal networks. We also reported that these waves have an individual spatiotemporal pattern that varies according to the type of neuron that is stimulated. Therefore, different spike wave propagations can be identified via pattern analysis of spike trains at particular neurons. Here, we assessed repetitive stimulation using intervals of 0.5 and 1.5 ms. Subsequently, we analyzed the relationship between the repetition of the stimulation and the identification of the different spike wave propagations. We showed that the various spike wave propagations were identified more precisely after stimulation was repeated several times using an interval of 1.5 ms. These results suggest the existence of a learning process in neuronal networks that occurs via repetitive training using a suitable interval.
- cultured neuronal network,
- spike wave propagation,
- identifying communications,
- repeated stimulations,
- learning process
Citation: Yoshi Nishitani, Chie Hosokawa, Yuko Mizuno-Matsumoto, Tomomitsu Miyoshi, Shinichi Tamura. Learning process for identifying different types of communication via repetitive stimulation: feasibility study in a cultured neuronal network[J]. AIMS Neuroscience, 2019, 6(4): 240-249. doi: 10.3934/Neuroscience.2019.4.240

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Abstract

It is well known that various types of information can be learned and memorized via repetitive training. In brain information science, it is very important to determine how neuronal networks comprising neurons with fluctuating characteristics reliably learn and memorize information. The aim of this study is to investigate the learning process in cultured neuronal networks and to address the question described above. Previously, we reported that the spikes resulting from stimulation at a specific neuron propagate as a cluster of excitation waves called spike wave propagation in cultured neuronal networks. We also reported that these waves have an individual spatiotemporal pattern that varies according to the type of neuron that is stimulated. Therefore, different spike wave propagations can be identified via pattern analysis of spike trains at particular neurons. Here, we assessed repetitive stimulation using intervals of 0.5 and 1.5 ms. Subsequently, we analyzed the relationship between the repetition of the stimulation and the identification of the different spike wave propagations. We showed that the various spike wave propagations were identified more precisely after stimulation was repeated several times using an interval of 1.5 ms. These results suggest the existence of a learning process in neuronal networks that occurs via repetitive training using a suitable interval.

Acknowledgments

This study was partly supported by the Grants-in-Aid for Scientific Research of Exploratory Research JP21656100, JP25630176, JP17K20029, JP16K12524, and Scientific Research (A) JP22246054 of the Japan Society for the Promotion of Science. We would like to thank Editage (www.editage.com) for English language editing.

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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