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Wang J.,Harbin University of Science and Technology | Guo H.,Harbin Institute of Technology | Tamura S.,TechNovator
Journal of Medical Imaging and Health Informatics | Year: 2017

With the rapid development of computer technology, computer-aided diagnosis has gradually become one of the focuses of current research. In particular, tumor detection is a significant task for reducing the human mortality. This paper is based on our previous study of automatic liver segmentation, and proposes an automatic approach for the detection of near-spherical liver tumors. Firstly, an improved variable quoit filter model is presented, which utilizes the characteristics of the ring-shaped filter to detect the rotationally symmetric structure. Secondly, in order to improve the detection performance of quoit filter, the sensitivity of multi-size tumor is enhanced by a specially designed grey-scale weight-based distance transformation function. Finally, support vector machine classification algorithm is adopted to reduce the false positive of classification. Experiment on 45 sets of abdominal CT images shows that the modified filter can achieve a better performance than the conventional filter, which indicates a promising prospect for spherical liver tumors detection. © Copyright 2017 American Scientific Publishers All rights reserved.


Sakuma S.,University of Hyogo | Mizuno-Matsumoto Y.,University of Hyogo | Nishitani Y.,Osaka University | Tamura S.,TechNovator
AIMS Neuroscience | Year: 2016

Although intercommunication among the different areas of the brain is well known, the rules of communication in the brain are not clear. Many previous studies have examined the firing patterns of neural networks in general, while we have examined the involvement of the firing patterns of neural networks in communication. In order to understand information processing in the brain, we simulated the interactions of the firing activities of a large number of neural networks in a 25 × 25 two-dimensional array for analyzing spike behavior. We stimulated the transmitting neurons at 0.1 msec. Then we observed the generated spike propagation for 120 msec. In addition, the positions of the firing neurons were determined with spike waves for different variances in the temporal fluctuations of the neuronal characteristics. These results suggested that for the changes (diversity) in the propagation routes of neuronal transmission resulted from variance in synaptic propagation delays and refractory periods. The simulation was used to examine differences in the percentages of neurons with significantly larger test statistics and the variances in the synaptic delay and refractory period. These results suggested that multiplex communication was more stable if the synaptic delay and refractory period varied. © 2016 Shun Sakuma et al.


Tamura S.,TechNovator | Nishitani Y.,Osaka University | Hosokawa C.,Japan National Institute of Advanced Industrial Science and Technology
AIMS Neuroscience | Year: 2016

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. © 2016 Shinichi Tamura et al.


Nishitani Y.,Osaka University | Hosokawa C.,Japan National Institute of Advanced Industrial Science and Technology | Mizuno-Matsumoto Y.,University of Hyogo | Miyoshi T.,Osaka University | Tamura S.,TechNovator
AIMS Neuroscience | Year: 2017

In brain information science, it is still unclear how multiple data can be stored and transmitted in ambiguously behaving neuronal networks. In the present study, we analyze the spatiotemporal propagation of spike trains in neuronal networks. Recently, spike propagation was observed functioning as a cluster of excitation waves (spike wave propagation) in cultured neuronal networks. We now assume that spike wave propagations are just events of communications in the brain. However, in reality, various spike wave propagations are generated in neuronal networks. Thus, there should be some mechanism to classify these spike wave propagations so that multiple communications in brain can be distinguished. To prove this assumption, we attempt to classify various spike wave propagations generated from different stimulated neurons using our original spatiotemporal pattern matching method for spike temporal patterns at each neuron in spike wave propagation in the cultured neuronal network. Based on the experimental results, it became clear that spike wave propagations have various temporal patterns from stimulated neurons. Therefore these stimulated neurons could be classified at several neurons away from the stimulated neurons. These are the classifiable neurons. Moreover, distribution of classifiable neurons in a network is also different when stimulated neurons generating spike wave propagations are different. These results suggest that distinct communications occur via multiple communication links and that classifiable neurons serve this function. © 2017 Yoshi Nishitani et al.


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.


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.


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.


Trademark
TechNovator | Date: 2015-11-09

Carrying cases, holders, protective cases and stands featuring power supply connectors, adaptors, speakers and battery charging devices, specially adapted for use with handheld digital electronic devices, namely, cell phones, tablet computers, MP3 players and personal digital assistants.


PubMed | Japan National Institute of Advanced Industrial Science and Technology, Osaka University, TechNovator and Osaka Prefecture University
Type: | Journal: 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.


PubMed | Osaka University, University of Hyogo, Osaka Prefecture University, Japan National Institute of Advanced Industrial Science and Technology and 2 more.
Type: | Journal: Computational intelligence and neuroscience | Year: 2016

We observed spike trains produced by one-shot electrical stimulation with 8 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of 1101 and 1011, which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the maximum cross-correlations among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

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