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Neuroinformatics is a new discipline which combines neuroscience with information technology. The Japan-Node of INCF was established at NIJC of RIKEN Brain Science Institute to address the task of integrating outstanding neuroscience researches in Japan. Each platform subcommittee from selected research areas develops a platform on the base-platform XooNIps. NIJC operates the J-Node portal to make platform resources open accessible in public. We introduce our concepts and the scheme of J-Node including nine platforms. [ABSTRACT FROM AUTHOR]

Various kinds of analysis and mathematical models based on neuroscience are developing in the neural network study. In the research, experimental data and knowledge so far obtained are essential resources to deepen their consideration of neuronal systems and functions. In order to utilize the accumulation of expertise and research effectively, it is important to integrate various resources, such as bibliography and experimental data. The managing and sharing of research resources are absolutely imperative for future development in both experimental and analytical studies. Various scientific fields need a new method to obtain precise information, because the amount of experimental data and publications has increased rapidly due to innovations in measurement, computers and network technologies. Under this situation, an effective resource-managing based on CMS (Content Management System) is introduced here for the laboratory use. We are providing functional modules to manage research resources for neuroinfomatics. As a practical use of these modules, a database system for managing image data of invertebrate neurons measured by Confocal Laser Scanning Microscope (CLSM) is developed. Furthermore, these modules are implemented for management of contents in the Invertebrate Brain Platform. [ABSTRACT FROM AUTHOR]

In this paper, systematic three-dimensional classification is presented for sets of interneuron slice images of silkworm moths, using self-organizing maps. Fractal dimension values are calculated for target sets to quantify denseness of their branching structures, and are employed as element values in training data for constructing a map. The other element values are calculated from the sets to which labeling and erosion are applied, and they quantifies whether the sets include thick main dendrites. The classification result is obtained as clusters with units in the map. The proposed classification employing only two elements in training data achieves as high accuracy as the manual classification made by neuroscientists. [ABSTRACT FROM AUTHOR]