Hidetoshi Ikeno

A new approach for the simultaneous tracking of multiple honeybees for analysis of hive behavior

Social activities are among the most striking of animal behaviors, and the clarification of their mechanisms is a major subject in ethology. Honeybees are a good model for revealing these mechanisms because they display various social behaviors, such as division of labor, in their colonies. Image processing is a precise and convenient tool for obtaining animal behavior data, but even recent methods are inadequate for the identification or description of honeybee behavior. This is because of the difficulty distinguishing between the large number of individuals in a small hive and their multiple movements. The present study developed a new computer-aided system, using a vector quantization method, for the identification and behavioral tracking of individual honeybees. The vector quantization method enabled separation of honeybee bodies in photographs recorded as a movie. This system succeeded in analyzing a huge number of frames quickly and can thus save both time and labor. Moreover, the system identified more than 72% of the bees in a hive and found and determined the active areas in the hive by extracting the trajectories of walking bees. In addition, useful behavioral data on the honeybee waggle dance were obtained using the present system.

Reconstruction and simulation for three-dimensional morphological structure of insect neurons

We present a system for the reconstruction three-dimensional morphological structure of a neuron from a sequence of tomographic images acquired by a confocal laser scanning microscope. In this system, the branching structure and diameter of dendrites are extracted by the Single-Seed Distance Transform method. Compartmental neuron models are reconstructed using the morphological structure detected by our system. In order to analyze electrical properties, a model description for the neuronal simulator, NEURON, is generated automatically. The effectiveness of the proposed system is shown by application to the reconstruction of interneurons in an antennal lobe of silkworm moths.

Reconstruction of virtual neural circuits in an insect brain

The reconstruction of large-scale nervous systems represents a major scientific and engineering challenge in current neuroscience research that needs to be resolved in order to understand the emergent properties of such systems. We focus on insect nervous systems because they represent a good compromise between architectural simplicity and the ability to generate a rich behavioral repertoire. In insects, several sensory maps have been reconstructed so far. We provide an overview over this work including our reconstruction of population activity in the primary olfactory network, the antennal lobe. Our reconstruction approach, that also provides functional connectivity data, will be refined and extended to allow the building of larger scale neural circuits up to entire insect brains, from sensory input to motor output.

Effects of excising and washing treatments on the root respiration rates of Japanese cedar (Cryptomeria japonica) seedlings

Tree root respiration is an important component of the carbon balance in forest ecosystems; however, it is not clear whether root preparation treatments (such as excising and washing) affect root respiration measurements. Here, we aimed to compare the respiration rates of roots subjected to different treatments (i.e., washing with water vs. brushing without water, and excising vs. not excising) for 17-month-old seedlings of Cryptomeria japonica. Immediately after sampling an entire root system, the root respiration rate was measured on a mass basis using a closed static chamber system equipped with an infrared gas analyzer. We found that the respiration rates for roots that were excised 10–20 times were significantly higher than those for roots that were not excised. There was no significant difference in the root respiration rates between washing and brushing treatments. Our results indicate that large numbers of excisions (>10 times) could lead to bias in the measured changes in specific root respiration rates, and imply that differences between washing and brushing treatments do not affect the specific root respiration rate. We conclude that potential variation in recorded root respiration rates could be minimized by standardizing the root preparation technique, which should involve rapidly removing all loose soil and limiting the extent of root excision.

Waggle dance effect: dancing in autumn reduces the mass loss of a honeybee colony

SUMMARY A honeybee informs her nestmates about the location of a profitable food source that she has visited by means of a waggle dance: a round dance and a figure-of-eight dance for a short- and long-distance food source, respectively. Consequently, the colony achieves an effective collection of food. However, it is still not fully understood how much effect the dance behavior has on the food collection, because most of the relevant experiments have been performed only in limited locations under limited experimental conditions. Here, we examined the efficacy of the waggle dances by physically preventing bees from dancing and then analyzing the changes in daily mass of the hive as an index of daily food collection. To eliminate place- and year-specific effects, the experiments were performed under fully natural conditions in three different cities in Japan from mid September to early October in three different years. Because the experiments were performed in autumn, all six of the tested colonies lost mass on most of the experimental days. When the dance was prevented, the daily reduction in mass change was greater than when the dance was allowed, i.e. the dance inhibited the reduction of the hive mass. This indicates that dance is effective for food collection. Furthermore, clear inhibition was observed on the first two days of the experiments; after that, inhibition was no longer evident. This result suggests that the bee colony adapted to the new environment.

Development of a new method to track multiple honey bees with complex behaviors on a flat laboratory arena.

A computer program that tracks animal behavior, thereby revealing various features and mechanisms of social animals, is a powerful tool in ethological research. Because honeybee colonies are populated by thousands of bees, individuals co-exist in high physical densities and are difficult to track unless specifically tagged, which can affect behavior. In addition, honeybees react to light and recordings must be made under special red-light conditions, which the eyes of bees perceive as darkness. The resulting video images are scarcely distinguishable. We have developed a new algorithm, K-Track, for tracking numerous bees in a flat laboratory arena. Our program implements three main processes: (A) The object (bees) region is detected by simple threshold processing on gray scale images, (B) Individuals are identified by size, shape and spatiotemporal positional changes, and (C) Centers of mass of identified individuals are connected through all movie frames to yield individual behavioral trajectories. The tracking performance of our software was evaluated on movies of mobile multi-artificial agents and of 16 bees walking around a circular arena. K-Track accurately traced the trajectories of both artificial agents and bees. In the latter case, K-track outperformed Ctrax, well-known software for tracking multiple animals. To investigate interaction events in detail, we manually identified five interaction categories; ‘crossing’, ‘touching’, ‘passing’, ‘overlapping’ and ‘waiting’, and examined the extent to which the models accurately identified these categories from bees interactions. All 7 identified failures occurred near a wall at the outer edge of the arena. Finally, K-Track and Ctrax successfully tracked 77 and 60 of 84 recorded interactive events, respectively. K-Track identified multiple bees on a flat surface and tracked their speed changes and encounters with other bees, with good performance.

2008 Special Issue: Development and application of a neuroinformatics environment for neuroscience and neuroethology

Insect brains are excellent models for analyzing neuronal function in moderately complex central nervous systems due to the vast potential they offer for revealing the intricate details of the workings of a biological neural network. For a systematic approach to understanding neuronal mechanisms, it is important to integrate research results from various fields, such as morphology, physiology and immunohistochemistry. We are developing a database system, the Bombyx Neuron Database (BoND) for assembling and sharing experimental and analytical data. The system is designed and developed based on experimental data, mostly obtained from intracellular recordings. A new WWW technology, CMS (Content Management System), was implemented in our system. That is, PHP-based CMS, XOOPS, provides several functions for web-based database management, for instance, user accounting, web page designing and data backup. The BoND was developed by our original database module of XOOPS, in order to deal with electrophysiological and anatomical data. Research resources from various fields are combined in the database for realizing a conjunction of experiments and analysis, which will assist progress in understanding neural network mechanisms as a virtual laboratory.

Development and application of CMS-based database modules for neuroinformatics

In order to utilize the accumulation of expertise and research effectively, it is important to integrate various resources, such as bibliography and experimental data, from individual laboratories to international levels. The sharing of research resources and the integration of knowledge are absolutely imperative for future development in both experimental and computational neuroscience fields. On the other hand, content management systems (CMS) have become widespread for constructing and managing WWW portal sites. In this study, in order to construct an effective resource-managing environment in the laboratory, we develop two database modules based on CMS, which can provide data integration and sharing capabilities for bibliographical resources and archived data files. We show the effectiveness of these modules in the biological and neuroscience fields by applying them in our laboratory-based work. Furthermore, similar applications can be made in the construction of Internet portal sites, because of considerations for security.

Japanese Neuroinformatics Node and Platforms

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.

Development of a scheme and tools to construct a standard moth brain for neural network simulations

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies.