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Feasibility of Multiplex Communication in a 2D Mesh Asynchronous Neural Network with Fluctuations

1 NBL Technovator Co., Ltd., Sennan, Osaka, Japan;
2 Dept. of Radiology, Graduate School of Medicine, Osaka University, Suita, Japan;
3 Biomedical Research Institute, AIST, Ikeda, Osaka, Japan

Topical Section: Communication and Language: Theoretical Advances in Explaining Brain Mechanisms

It remains a mystery how neural networks composed of neurons with fluctuating characteristics can reliably transmit information. In this study, we simulated a 9 × 9 2D mesh neural network consisting of an integrate-and-fire model without leak, and connection weights that were randomly generated. The characteristics of the refractory period and output delay of the neurons were fluctuated time to time. Spikes from transmitting neuron groups spread (propagated as spike waves) to receiving neurons. For 9 to 1 multiplex communication with a back propagation neural network (BPN), the receiving neurons successfully classified which neuron group transmitted the spike at a rate of 99%. In other words, the activity of the neuron group is propagated in the neural network as spike waves in a broadcasting manner and the wave fragment is received by receiving neurons. Next, point-to-point signal transmission in the neural network is carried out by multi-path, multiplex communication, and diversity reception. Each neuron can function in 3 ways of transmit, relay (transfer), and receive; however, most neurons act as a local relaying media. This type of mechanism is similar to sound propagation through air. Our research group studies the functions of neural networks by combining experiments with cultured neuronal networks with artificial neural network simulations. This current study corresponding to our previous work on the ability of remote receiving neurons to identify two transmitting neuron groups stimulated in a cultured neuronal network, i.e., 2 to 1 communication. These mechanisms may be the basis of higher cortical functions.
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References

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

2. Kliper O, Horn D, Quenet B, et al. (2004) Analysis of spatiotemporal patterns in a model of olfaction. Neurocomputing 58-60: 1027-1032.

3. Fujita K, Kashimori Y, Kambara T (2007) Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli. Biol Cybern 97: 293-305. doi: 10.1007/s00422-007-0175-z.    

4. Tyukin I, Tyukina T, Leeuwen C (2009) Invariant template matching in systems with spatiotemporal coding: A matter of instability. Neural Networks 22: 425-449.    

5. 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.

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

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

8. Tamura S, Mizuno-Matsumoto Y, Chen YW, et al. (2009) Association and abstraction on neural circuit loop and coding. The Fifth Int’l Conf. Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP2009) A10-07(No.546)

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

10. Abeles M (2009) Synfire chains. Scholarpedia 4: 1441. http://www.scholarpedia.org/article/Synfire_chains.

11. Izhikevich EM (2006) Polychronization: Computation with spikes. Neural Comput 18: 245-282.    

12. Perc M (2007) Fluctuating excitability: A mechanism for self-sustained information flow in excite arrays. Chaos Solitons Fractals 32: 1118-1124. doi:10.1016/j.chaos.2005.11.035    

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

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. Kamimura T, Yagi Y, Tamura S, et al. (2015) Multiplex Communication with Synchronous Shift and Weight Learning in 2D Mesh Neural Network. Auto Control Intell Systems 3: 63-70. doi: 10.11648/j.acis.20150305.11.    

16. Yuko Mizuno-Matsumoto, Kozo Okazaki, Amami Kato, 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.    

17. Mizuno-Matsumoto Y, Ishijima M, Shinosaki K, et al. (2001) Transient Global Amnesia (TGA) in an MEG Study. Brain Topography 13: 269-274.    

18. Nishitani Y, Hosokawa C, Mizuno-Matsumoto Y, et al. (2012) Detection of M-sequences from spike sequence in neuronal networks. Comput Intell Neurosci 2012. doi:10.1155/2012/862579.

19. Tamura S, Nishitani Y, Hosokawa C, et al. (2016) Spike code flow in cultured neuronal networks. Comput Intell Neurosci 2016. doi:10.1155/2016/7267691.

20. Tamura S, Nishitani Y, Hosokawa C, et al. (2016) Simulation of code spectrum and code flow of cultured neuronal networks. Comput Intell Neurosci 2016. doi:10.1155/2016/7186092.

21. 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 Academ Res Reflect 4: 11-19.

22. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323: 533-536. doi:10.1038/323533a0.    

23. Perc M (2005) Spatial coherence resonance in excitable media. Physical Rev E 72: 1-6. doi: 10.1103/PhysRevE.72.016207

24. Martha N. Havenith, Shan Yu, Julia Biederlack, et al. (2011) Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead. J Neurosci 31: 8570-8584.    

25. Sakurai Y, Takahashi S (2013) Conditioned enhancement of firing rates and synchrony of hippocampal neurons and firing rates of motor cortical neurons in rats. Europ J Neurosci 37: 623-639. doi: 10.1111/ejn.12070.    

26. Vaughan RG, Andersen JB (1987) Antenna diversity in mobile communications. IEEE Transact Vehicul Technol 36: 149-172. doi:10.1109/T-VT.1987.24115    

27. Silver D, Huang A, Maddison CJ, et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529: 484-489. doi:10.1038/nature16961.    

Copyright Info: © 2016, Shinichi Tamura, et al., 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)

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