Shinichi Tamura

Random bin for analyzing neuron spike trains

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.

Association and Abstraction on Neural Circuit Loop and Coding

We show in this paper a feasibility of self organization of loop neural circuit for memory, association, and abstraction, which is algorithmically realizable. First we assume the memory is composed of loop neural circuit in the cerebral cortex. Then, we give how a memory content expressed by loop circuit shape is copied to blank area of cerebral cortex as well as how a path between memory loops with the same shape is found by broadcasting based on the classical Hebbian law. The generated sequence from the transmitting loop corresponding to the memory is a code by pseudo random sequence which is now popular in spread spectrum communication or mobile CDMA. Further, we show how the association and abstraction of the memory can be realized by the neural circuit. The loop circuit self-organizing model seems to be reasonable in the sense of hardware realizable, having large number of codes corresponding to various memory contents, and able to access to other memories associatively. Thus, the model seems able to account well unifiedly the information processing function of the brain.

Mental Rotation by Neural Network

This paper proposes a new and compact method for object image retrieval fusing Dominant Colors (DCs) and embedded Markov chain concepts. This proposed method uses combined color-texture features which are characterized in terms of their spatial interaction or interrelationship properties, modeled by means of a set of embedded Markov chains, each associated with a major spatial direction. Specifically, DCs are extracted from the object image, which are encountered pixel-wise along a given direction to form an embedded Markov chain. Normalizing the resultant Markov chains over all specified directions, the corresponding stationary distribution is derived and served as Markov Feature-Vector (MFV). We then employ the chi square distance between the feature vectors in comparing similarity of images. The MFV involves spatial structure information of both within and between dominant color regions. Moreover, it keeps simplicity, compactness, efficiency, and robustness. We conduct experiments using a comprehensive set of images including deformable shapes. Experimental results show that the proposed method can retrieve an important number of correct images with very high accuracy while the mismatch ratio remains constant.

Why people play: artificial lives acquiring play instinct to stabilize productivity

We propose a model to generate a group of artificial lives capable of coping with various environments which is equivalent to a set of requested task, and likely to show that the plays or hobbies are necessary for the group of individuals to maintain the coping capability with various changes of the environment as a whole. This may be an another side of saying that the wide variety of the abilities in the group is necessary, and if the variety in a species decreased, its species will be extinguished. Thus, we show some simulation results, for example, in the world where more variety of abilities are requested in the plays, performance of the whole world becomes stable and improved in spite of being calculated only from job tasks, and can avoid the risk of extinction of the species. This is the good effect of the play.

Computational intelligence in biomedical science and engineering

Biomedical science and engineering is an interdisciplinary research field, which combines the advanced technologies and problem solving skills with medical and biological science. Since the biomedical solutions generally have large variations and complexity, it is difficult to use a simple way or a classical approach to find the solutions. Computation intelligence techniques such as neural networks and evolutionary algorithms are nature-inspired computational approaches to address complex problems of the real world. Recently, computational intelligence is playing an important role in biomedical research fields, such as computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy, and bioinformatics. Approaches based on computational intelligence have been shown to be advantageous compared to classical approaches. n nThis special issue focuses on major trend and new techniques in computational intelligence and their use in biomedical science and engineering. We received 15 submissions. Each paper was reviewed by two external referees. We finally accepted 8 papers for our special issue. The area of interest of the accepted papers covers a broad spectrum of computational intelligence techniques with application to biomedical science and engineering. n nH. Jiang at al. proposed an optimized medical image compression algorithm based on wavelet transform and improved vector quantization, which can maintain the diagnostic-related information of the medical image at a high compression ratio. n nP. Zhang et al. proposed a composite match index (CMI) method for the integration of different feature-point matching approaches in order to improve the robustness of the matching result. The proposed method has also been applied to interior deformation field measurement of complex human tissues from three-dimensional magnetic resonance (MR) volumetric images. n nC.-L. Lin et al. proposed a hybrid particle swarm optimization (HPSO) for robust medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover. n nH. Ikeno et al. developed a scheme and tools to construct a standard moth brain for neural network simulations. Morphological models of neurons are reconstructed from confocal image data of neurons. n nY. Nishitani et al. detected a significantly greater number of Rev. M3 patterns from the time series stimulated spike response than from the random series (interval shuffle) data in neuronal networks formed on MEAs. These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. n nS. M. Rabiee and H. Baseri developed three different adaptive neurofuzzy inference systems (ANFISs) for estimation of the setting properties of calcium phosphate bone cement. Despite the relatively small amount of data (25 conditions), the proposed method gave satisfying results. n nS. Tamura et al. proposed automutual information-(AM-I) based randomizing method of bin width and location instead of conventional fixed bin setting for analyzing neuron spike trains. In his second paper, they also proposed a model of human society where roles are divided to each person to obtain high performance as a whole, and as a result people play to train their hidden abilities. n nAlthough the above papers do not make a complete coverage of the computational intelligence in biomedical science and engineering, it provides a flavor of what are the important issues and the benefits of applying computational intelligence to biomedical science and engineering. We would like to thank the authors for submitting their papers to the special issue as well as the reviewers for providing their expertise and making valuable comments.