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Kanzaki, Ryohei; Ando, Noriyasu; Sakurai, Takeshi; Kazawa, Tomoki
doi: 10.1163/156855308X368949pmid: N/A
Adaptability, i.e., the ability to behave in accordance with a ceaselessly changing environment, is a defining feature of animals, including insects, and is a necessary attribute in robotics. Insects display a range of sophisticated behaviors in response to their environments based on the processing of a simple nervous system. Insects are uniquely suited for multidisciplinary studies of the brain involving a combined approach at several levels, from molecules and single neurons to neural networks and behavior. Furthermore, insects can be adapted for use with a wide variety of methodological approaches, from molecular genetics, electrophysiology and imaging to computational tools and robotics. Thus, insects are an excellent model taxon for understanding adaptive control in biological systems. In this review, the general features of the insect brain and multiscale approaches for understanding the neural basis of their behavior are introduced. As an example of adaptive behavior in insects, odor–source orientation behavior in silkmoths and the feasibility of a behavioral strategy based on their neural system, with implementation in robots, is described. Finally, we present novel approaches using an insect–machine hybrid, which will enhance our ability to evaluate and understand adaptive behavior.
Takakusaki, Kaoru; Okumura, Toshikatsu
doi: 10.1163/156855308X368958pmid: N/A
Posture and movements are our only physical means of interacting with the environment. As we express our thoughts and emotions through posture and movements, they indicate our will or intentions. Locomotion is representative of purposeful goal-directed behaviors that are initiated by signals arising from either volitional processing in the cerebral cortex or emotional processing in the limbic system. Regardless of whether the locomotion is volitional or emotional, it is accompanied by automatically controlled movement processes such as the adjustment of postural muscle tone and rhythmic limb movements that are unconsciously executed. Sensori-motor integration at the level of the brainstem and spinal cord plays major roles in this automatic control. Signals processed in the basal ganglia and the cerebellum act on the cerebral cortex, the limbic system and the brainstem so that locomotor behaviors are appropriately and precisely regulated depending on the behavioral context. The purpose of this review is to describe how purposeful locomotor behaviors are initiated, executed and regulated so as to enable locomotive subjects to interact with and adapt to the environment.
Okada, Ryuichi; Ikeno, Hidetoshi; Aonuma, Hitoshi; Ito, Etsuro
doi: 10.1163/156855308X368967pmid: N/A
A honeybee informs her nestmates of flower locations by a unique behavior called a 'waggle dance'. We regard this behavior as a good model of the 'propagation and sharing of knowledge' to maintain a society. We have attempted to reveal how this dance benefits the colony using mathematical models and computer simulation based on parameters obtained from observations of bee behavior. Our simulation indicated that the most successful forages were made by a putative bee colony that used the dance to communicate. Video analysis of worker honeybee behavior in the field showed that a bee does not dance in a single, random place in the hive, but waggles several times in one place and several times in another. The orientation and duration of waggle runs varied from run to run, within ranges of ±15° and ±15%, respectively. We also found that most of the bees that listened to the waggle dance turned away from the dancer after listening to one or two runs. These data suggest that honeybees use the waggle dance as a method of communication, but that they must base their forages on ambiguous information about the location of a food source.
Watanabe, Wataru; Kawakatsu, Toshihiro; Ishiguro, Akio
doi: 10.1163/156855308X368976pmid: N/A
This study is intended to deal with the interplay between control and mechanical systems, and to discuss the 'brain–body interaction as it should be', particularly from the viewpoint of learning. To this end, we have employed a decentralized control of a two-dimensional serpentine robot consisting of several identical body segments as a practical example. The preliminary simulation results derived indicate that the convergence of decentralized learning of locomotion control can be significantly improved, even with an extremely simple learning algorithm, i.e., a gradient method, by introducing biarticular muscles which induce long-distant physical interaction between the body segments compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be offloaded from the brain into its body, which allows robots to emerge various with interesting functionalities.
Aoi, Shinya; Ogihara, Naomichi; Sugimoto, Yasuhiro; Tsuchiya, Kazuo
doi: 10.1163/156855308X3689785pmid: N/A
Humans generate bipedal walking by cooperatively manipulating their complicated and redundant musculoskeletal systems to produce adaptive behaviors in diverse environments. To elucidate the mechanisms that generate adaptive human bipedal locomotion, we conduct numerical simulations based on a musculoskeletal model and a locomotor controller constructed from anatomical and physiological findings. In particular, we focus on the adaptive mechanism using phase resetting based on the foot-contact information that modulates the walking behavior. For that purpose, we first reconstruct walking behavior from the measured kinematic data. Next, we examine the roles of phase resetting on the generation of stable locomotion by disturbing the walking model. Our results indicate that phase resetting increases the robustness of the walking behavior against perturbations, suggesting that this mechanism contributes to the generation of adaptive human bipedal locomotion.
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