Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

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

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.
  Figure/Table
  Supplementary
  Article Metrics

References

1. Bonifazi P, Goldin M, Picardo MA, et al. (2009) GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326: 1419–1424.    

2. Kamimura T, Nakamura K, Yoneda K, et al. (2010) Information communication in brain based on memory loop neural circuit. ICIS2010 & SEDM2010 (appears in IEEE Xplore), Chengdu, June, 23–25.

3. Choe Y (2002) Analogical Cascade: A theory on the role of the thalamo-cortical loop in brain function. Neurocomputing 52: 713–719.

4. Tamura S, Mizuno-Matsumoto Y, Chen YW, et al. (2009) Association and abstraction on neural circuit loop and coding. IIHMSP 2009 10-07: 546–549 (appears in IEEE Xplore).

5. Thorpre S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381: 520.    

6. Cessac B, Paugam-Moisy H, Viéville T (2010) Overview of facts and issues about neural coding by spike. J Physiol-Paris 104: 5–18.    

7. Kliper O, Horn D, Quenet B, et al. (2004) Analysis of spatiotemporal patterns in a model of olfaction. Neurocomputing 58: 1027–1032.

8. Fujita K, Kashimori Y, Kambara T (2007) Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli. Biol Cybern 97: 293–305.    

9. Tyukin I, Tyukina T, Van LC (2009) Invariant template matching in systems with spatiotemporal coding: A matter of instability. Neural Networks 22: 425–449.    

10. Mohemmed A, Schliebs S, Matsuda S, et al. (2013) Training spiking neural networks to associate spatio-temporal input–output spike patterns. Neurocomputing 107: 3–10.    

11. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607–609.    

12. Bell AJ, Sejnowski TJ (1997) The "independent components" of natural scenes are edge filters. Vision Res 37: 3327–3338.    

13. Aviel Y, Horn D, Abeles M (2004) Synfire waves in small balanced networks. Neurocomputing 58–60: 123–127.

14. Abeles M (1982) Local Cortical Circuits: An Electrophysiological study, Berlin: Springer.

15. Shinozaki T, Câteau H, Urakubo H, et al. (2007) Controlling synfire chain by inhibitory synaptic input.   J Phys Soc Jpn 76:   044806.

16. Izhikevich EM (2014) Polychronization: Computation with spikes. Neural Comput 18: 245–282.

17. Perc M (2007) Fluctuating excitability: A mechanism for self-sustained information flow in excite arrays. Chaos Soliton Fract 32: 1118–1124.    

18. Zhang H, Wang Q, Perc M, et al. (2013) Synaptic plasticity induced transition of spike propagation in neuronal networks. Commun Nonlinear Sci 18: 601–615.    

19. Mizuno-Matsumoto Y, Okazaki K, Kato A, et al. (1999) Visualization of epileptogenic phenomena using crosscorrelation analysis: Localization of epileptic foci and propagation of epileptiform discharges. IEEE Trans Biomed Eng 46: 271–279.    

20. Mizuno-Matsumoto Y, Ishijima M, Shinosaki K, et al. (2001) Transient global amnesia (TGA) in a MEG study. Brain Topogr 13: 269–274.    

21. Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2012) Detection of M-sequences from spike sequence in neuronal networks. Comput Intell Neurosci 2012: 167–185.

22. Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2014) Synchronized code sequences from spike trains in cultured neuronal networks. Int J Eng Ind 5: 13–24.

23. Tamura S, Nishitani Y, Hosokawa C, et al. (2016) Simulation of code spectrum and code flow of cultured neuronal networks. Comput Intell Neurosci 2016: 1–12.

24. Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2016) Classification of spike wave propagations in a cultured neuronal network: Investigating a brain communication mechanism. AIMS Neurosci 4: 1–13.    

25. Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2017) Effect of correlating adjacent neurons for identifying communications: Feasibility experiment in a cultured neuronal network AIMS Neurosci 5: 18–31.

26. Ando H (2002) Human Brain Regions Involved in Visual Motion Prediction. Neuro Image Human Brain Mapping 2002 Meeting, 599.

27. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323: 533–536.    

© 2019 the Author(s), 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)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved