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Tamura S.,TechNovator | Nishitani Y.,Osaka University | Hosokawa C.,Japan National Institute of Advanced Industrial Science and Technology | Miyoshi T.,Osaka University | Sawai H.,Osaka Prefecture University
Computational Intelligence and Neuroscience | Year: 2016

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. © 2016 Shinichi Tamura et al. Source


Tamura S.,TechNovator | Tamura S.,Osaka University | Nishitani Y.,Osaka University | Hosokawa C.,Japan National Institute of Advanced Industrial Science and Technology | Mizuno-Matsumoto Y.,University of Hyogo
Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems, CIS 2015 and Robotics, Automation and Mechatronics, RAM 2015 | Year: 2015

It's important to investigate natural intelligence how the signal/ information is flown in neuronal network. We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multi-electrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We then constructed code flow maps as movies of the electrode array to observe the code flow especially of '1101' and '1011.' To quantify the flow, we calculated the cross-correlations of the maximum direction of the codes with lengths less than 8. Normalized cross-correlations in the maximum direction were almost constant irrespective of code. Thus, the analysis suggested that the local codes for electrode flow maintained the code shape to some extent and conveyed information in the neural network. Then we made simulation of such code flow. © 2015 IEEE. Source


Tamura S.,TechNovator | Tamura S.,Osaka University | Nishitani Y.,Osaka University | Hosokawa C.,Japan National Institute of Advanced Industrial Science and Technology | And 2 more authors.
2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015 | Year: 2015

It's important for understanding brain intelligence to investigate how the signal/information is flown in neuronal network. We observed spike trains obtained by one-shot electrical stimulation with 8 × 8 multi-electrodes in cultured neuronal networks. Each electrode is considered to collect spikes from several neurons. We then constructed code flow maps as movies of the electrode array to observe the code flow especially of 1101 and 1011. To quantify the flow, we calculated the cross-correlations of the maximum direction of the codes with lengths less than 8. Normalized cross-correlations in the maximum direction were almost constant irrespective of code. Thus, the analysis suggested that the local codes for electrode flow maintained the code shape to some extent and conveyed information in the neural network. Then we made simulation of such code flow, and could estimate rough characteristics of neurons including refractory period and distribution of connection weights between neurons. © 2015 IEEE. Source


Tamura S.,TechNovator | Miyoshi T.,Osaka University | Sawai H.,Osaka University | Mizuno-Matsumoto Y.,University of Hyogo
Computational Intelligence and Neuroscience | Year: 2012

When analyzing neuron spike trains, it is always the problem of how to set the time bin. Bin width affects much to analyzed results of such as periodicity of the spike trains. Many approaches have been proposed to determine the bin setting. However, these bins are fixed through the analysis. In this paper, we propose a randomizing method of bin width and location instead of conventional fixed bin setting. This technique is applied to analyzing periodicity of interspike interval train. Also the sensitivity of the method is presented. © Copyright 2012 Shinichi Tamura et al. Source


Nishitani Y.,Osaka University | Hosokawa C.,Osaka National Research Institute | Mizuno-Matsumoto Y.,University of Hyogo | Miyoshi T.,Osaka University | And 3 more authors.
Computational Intelligence and Neuroscience | Year: 2012

In circuit theory, it is well known that a linear feedback shift register (LFSR) circuit generates pseudorandom bit sequences (PRBS), including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3). These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of 01 reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks. © Copyright 2012 Yoshi Nishitani et al. Source

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