Nilton Liuji Kamiji

Identification of Retinal Ganglion Cells and Their Projections Involved in Central Transmission of Information about Upward and Downward Image Motion

The direction of image motion is coded by direction-selective (DS) ganglion cells in the retina. Particularly, the ON DS ganglion cells project their axons specifically to terminal nuclei of the accessory optic system (AOS) responsible for optokinetic reflex (OKR). We recently generated a knock-in mouse in which SPIG1 (SPARC-related protein containing immunoglobulin domains 1)-expressing cells are visualized with GFP, and found that retinal ganglion cells projecting to the medial terminal nucleus (MTN), the principal nucleus of the AOS, are comprised of SPIG1+ and SPIG1− ganglion cells distributed in distinct mosaic patterns in the retina. Here we examined light responses of these two subtypes of MTN-projecting cells by targeted electrophysiological recordings. SPIG1+ and SPIG1− ganglion cells respond preferentially to upward motion and downward motion, respectively, in the visual field. The direction selectivity of SPIG1+ ganglion cells develops normally in dark-reared mice. The MTN neurons are activated by optokinetic stimuli only of the vertical motion as shown by Fos expression analysis. Combination of genetic labeling and conventional retrograde labeling revealed that axons of SPIG1+ and SPIG1− ganglion cells project to the MTN via different pathways. The axon terminals of the two subtypes are organized into discrete clusters in the MTN. These results suggest that information about upward and downward image motion transmitted by distinct ON DS cells is separately processed in the MTN, if not independently. Our findings provide insights into the neural mechanisms of OKR, how information about the direction of image motion is deciphered by the AOS.

2011 Special Issue: PLATO: Data-oriented approach to collaborative large-scale brain system modeling

The brain is a complex information processing system, which can be divided into sub-systems, such as the sensory organs, functional areas in the cortex, and motor control systems. In this sense, most of the mathematical models developed in the field of neuroscience have mainly targeted a specific sub-system. In order to understand the details of the brain as a whole, such sub-system models need to be integrated toward the development of a neurophysiologically plausible large-scale system model. In the present work, we propose a model integration library where models can be connected by means of a common data format. Here, the common data format should be portable so that models written in any programming language, computer architecture, and operating system can be connected. Moreover, the library should be simple so that models can be adapted to use the common data format without requiring any detailed knowledge on its use. Using this library, we have successfully connected existing models reproducing certain features of the visual system, toward the development of a large-scale visual system model. This library will enable users to reuse and integrate existing and newly developed models toward the development and simulation of a large-scale brain system model. The resulting model can also be executed on high performance computers using Message Passing Interface (MPI).

A Next Generation Modeling Environment PLATO: Platform for Collaborative Brain System Modeling

To understand the details of brain function, a large scale system model that reflects anatomical and neurophysiological characteristics needs to be implemented. Though numerous computational models of different brain areas have been proposed, these integration for the development of a large scale model have not yet been accomplished because these models were described by different programming languages, and mostly because they used different data formats. This paper introduces a platform for a collaborative brain system modeling (PLATO) where one can construct computational models using several programming languages and connect them at the I/O level with a common data format. As an example, a whole visual system model including eye movement, eye optics, retinal network and visual cortex is being developed. Preliminary results demonstrate that the integrated model successfully simulates the signal processing flow at the different stages of visual system.

RAST: finding related documents based on triplet similarity

With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only searches abstracts by the given keyword(s) but also displays abstracts related to a single or multiple selection. It also displays highly relevant abstracts together with possible keywords to help users refine their search. Analysis of the conditional similarity algorithm proposed here has shown that it does provide better output compared to ordinary cosine similarity, as well as the list of possible keywords reflects results of latent topic analysis. An interface for storing and sorting selected abstracts for future review and/or printing is also provided.

RAST: A Related Abstract Search Tool

With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only search abstracts by the given keyword(s), but also displays abstracts related to a single or multiple selection. It also displays information on the similarity between all abstracts on a list, providing users with more information to take into account to support finding relevant abstracts. It also suggests possible keywords to help refine their search, and an interface for storing and sorting selected abstracts for future review and/or printing.