Neuronal regeneration in the adult brain, which is restricted compared to that in the embryonic brain, is a long-standing topic in neuroscience and medical research. Based on studies in mammals, a small number of newly generated immature neurons (neuroblasts) migrate toward damaged sites and contribute to functional recovery. During migration, neuroblasts form chain-like collectives and modify the morphology of glial cells (astrocytes), which are the main components of the surrounding environment. However, it remains unclear how neuroblasts form collectives and how efficient migration is achieved through collective formation in a pool of astrocytes. The main difficulty lies in tracking individual neuroblasts within the collective, both in vitro and in vivo, over a period. To address this impasse, we built a mathematical model of the neuroblast-astrocyte system to assess its long-term performance in silico. Our simulations showed that individual neuroblasts gathered into chain-like collectives through occasional contact, astrocyte confinement, and moderate adhesion between the neuroblasts. The forward movement of neuroblasts in an astrocyte-dense environment was accelerated if we assumed a simple interaction: the higher the number of neuroblasts near an astrocyte, the stronger the shrinkage of astrocytic protrusions. Furthermore, temporal changes in neuroblast behavior, as indicated by our observation of living neuroblasts in culture, reinforce the advantages of simulated collectives. A collective of neuroblasts with constant behavior sometimes repeated non-migratory movements, whereas those with inconstant behavior were easily untangled, resulting in a rapid migration. These results highlight the potential for neuroblast collectivity and inconstancy in enhancing neuronal regeneration in the adult brain.

Collective cell behavior is fundamental to structure multicellular organisms. There, constituting cells always have heterogeneous properties across cell types (macro-heterogeneity) and within a cell type (micro-heterogeneity). Many studies have described the existence of heterogeneity in various systems at the cellular level, whereas little has investigated its effect on the systems. Unveiling how heterogeneity plays a role in the collective behavior would build a bridge from microscopic to macroscopic biological phenomena. We use the muscle remodeling in Drosophila as a model system, in which drastic reconstruction processes involve the physical interactions of various cells. At the early pupal stages, hundreds of hemocytes vigorously migrate and decompose the larval muscles into fragments called sarcolytes. We used in vivo and in silico approaches to understand how the dynamics of the sarcolyte population are characterized by hemocytes and other cells in the environment. Our in vivo imaging showed that the sarcolyte population gradually reduced the speed while changing the spatial arrangements. We also found that the sarcolyte dynamics involve macro-heterogeneity, namely, the coexistence of fat body cells and hemocytes, as well as micro-heterogeneity within hemocytes. To computationally evaluate the effects of factors determining the speed and arrangement of the sarcolyte population, we built a mathematical model assuming simplified interactions between sarcolytes, hemocytes, and fat body cells. Our simulations showed that, firstly, the efficient spreading and stable pattern formation of sarcolytes were together achieved by a delayed emergence of fat body cells and the micro-heterogeneity in hemocyte motility. Secondly, based on the similarity of observed and simulated network-like arrangements of sarcolytes, spatial confinement was another factor that causes the stabilization of sarcolytes. This study provides a pattern formation mechanism by which macro- and micro-heterogeneous migratory cells generate a 'dual-purpose' collective behavior-quickly spreading particles throughout the field while efficiently organizing them into an orderly arrangement.

Gait emergence and adaptation in animals is unmatched in robotic systems. Animals can create and recover locomotive functions "on-the-fly" after an injury whereas locomotion controllers for robots lack robustness to morphological changes. In this work, we extend previous research on emergent interlimb coordination of legged robots based on coupled phase oscillators with force feedback terms. We investigate how the coupling weights between these phase oscillators can be extracted from the morphology with a fast and computationally lightweight method based on a combination of twitching and Hebbian learning to form sensor-motor maps. The coefficients of these maps create naturally scaled weights, which not only lead to robust gait limit cycles, but can also adapt to morphological modifications such as sensor loss and limb injuries within a few gait cycles. We demonstrate the approach on a robotic quadruped and hexapod.

A swarm robotic system is a system in which multiple robots cooperate to fulfill a macroscopic function. Many swarm robots have been developed for various purposes. This study aims to design swarm robots capable of executing spatially distributed tasks effectively, which can be potentially used for tasks such as search-and-rescue operation and gathering scattered garbage in rooms. We propose a simple decentralized control scheme for swarm robots by extending our previously proposed non-reciprocal-interaction-based model. Each robot has an internal state, called its workload. Each robot first moves randomly to find a task, and when it does, its workload increases, and then it attracts its neighboring robots to ask for their help. We demonstrate, via simulations, that the proposed control scheme enables the robots to effectively execute multiple tasks in parallel under various environments. Fault tolerance of the proposed system is also demonstrated.

An optional robotic arm unit, like animals' neck, allows quadruped robot to achieve multifunctional tasks such as manipulation and patrol. However, increases in body weight due to the neck unit will make energetic issues in legged robots' locomotion more serious. This study presents a minimal decentralized coordination mechanism between a neck and limbs, especially focusing on quadruped mammals' nodding behaviors for efficient walking. The results of two dimensional (2D) simulation suggest that bilateral sensory feedback mechanism between neck and limbs plays essential roles for the quadruped robot to coordinate neck and limbs in response to the physical situation of the body parts, achieving efficient quadrupedal walking.

Snakes are able to move effectively by using terrain irregularities as scaffolds against which they push their bodies. This locomotion is attractive from a robotic viewpoint because irregularities in the environment of conventional robots interfere with their operation. In a previous work, we proposed a decentralized control mechanism of the scaffold-based locomotion of snakes, which combined curvature derivative control with local pressure reflex. Here, we practically demonstrate how a snake-like robot utilizing the proposed control scheme moves effectively by pushing its body against pegs.

Autonomous decentralized control is a key concept for the realization of highly adaptive behavior. However, universal design of autonomous decentralized control that ensures rich adaptability is still lacking. In this study, we tackle this problem through the development of a two-dimensional sheet-like robot, SheetBot. The SheetBot is a suitable model system for the establishment of universal design principles for autonomous decentralized control, because it can bend reasonably to the circumstances encountered due to its large surface area, and also because its applications are widely expected. To realize highly adaptive locomotion with SheetBot, we are inspired by the decentralized control scheme of the scaffold-based locomotion of snakes. We extend this design scheme to a two-dimensional bodily structure on the basis of a continuum model. Simulation results show that SheetBot can locomote on various kinds of irregular terrain with minimal control inputs by implementing the proposed autonomous decentralized control scheme.

Versatile gait patterns are observed in quadrupeds according to the locomotion speed, environmental conditions, and animal species. These gait patterns are generated via inter- and intra-limb coordination mechanisms, both of which are controlled in part by an intraspinal neural network called the central pattern generator (CPG). Previous CPG-based models mainly focused on the inter-limb coordination mechanisms and not on the intra-limb coordination mechanisms, although both of them should play a pivotal role in generating various gait patterns. In this study, we present an autonomous decentralized control scheme for quadruped locomotion wherein inter- and intra-limb coordination mechanisms are well coupled. Simulation results show that the quadruped exhibits transitioning between walking and running and the ability to adapt to changes in body properties by appropriately modifying the phase relationship among body points through well-balanced coupling of the inter- and intra-limb coordination mechanisms. We also present a physical robot that we are currently developing.

Autonomous decentralized control is an attractive concept for designing robots that can exhibit highly adaptive animal-like behaviour. Despite its appeal, previous robots could only adapt to a limited number of environments. To tackle this problem, we hypothesize that robots will move in various environments by simply implementing a “scaffold-exploitation mechanism”. In our previous work, we drew our inspiration from flatworms, which move over various terrains using their two-dimensional sheet-like body, and designed an autonomous decentralized control scheme for a similar robot, SheetBot. In this study, we extend our previous control scheme so that it can effectively exploit the features in its surrounding environment as scaffolds. The validity of the extended control scheme is confirmed by simulations. Our control scheme is not specific to a certain environment but could be applicable in any environment; it thus forms the basis for developing a multi-terrestrial robot whose working area covers land, sea, and even air.

Snakes actively utilize terrain irregularities and attain propulsion force by pushing their bodies against scaffolds. The objective of this study is to understand the mechanism underlying this locomotion on the basis of a synthetic approach. Our previous control scheme of a snake-like robot, in which the curvature derivative control method is combined with a reflexive mechanism using local pressures on the body wall, could not fully reproduce innate behavior of real snakes. In this study, we improve our previous control scheme. We show that the locomotion of a simulated robot with the improved control scheme is surprisingly in good agreement with the locomotion of a real snake. We also present a physical robot that we are currently developing.

Snakes actively utilize irregularities in terrains and attain propulsion force by pushing their bodies against scaffolds. The objective of this study is to understand the mechanism underlying such functionalities of snakes on the basis of a synthetic approach. We construct a model of a serpentine robot with viscoelastic properties, and we design an autonomous decentralized control scheme that employs local sensory feedback based on the muscle length and strain of the body, the latter of which is generated by the body softness. Simulation results show that the robot exhibits locomotion effectively by utilizing scaffolds such as high-friction areas and pegs, which is in fairly good agreement with biological findings.

The mechanism of multi-articular muscles that contributes to the emergence of macroscopic behavior of animals has not been clarified thus far. To address this issue, we focus on the slithering motion of snakes, which have numerous antagonistic muscle-tendon chains that span several tens of vertebrae. From theoretical analyses based on the continuum model, the acceleration of the body in the longitudinal direction is found to be proportional to the number of segments spanned by each muscle. On the basis of this theoretical result, we propose a decentralized control scheme for the efficient locomotion of a multi-articular snake-like robot. We conduct simulations using this control scheme, and the results show that the locomotion efficiency increases as the number of segments spanned by each muscle increases.

Autonomous decentralized control is a key concept in our understanding of the mechanism underlying locomotion of animals, wherein the phasic pattern of motion and the excitation pattern of muscle tonus are well reconciled. In our recent work, we proposed a decentralized control scheme in which phasic control and tonic control are well coordinated. In this study, we develop a real snake-like robot to validate our proposed control scheme. Experimental results show that the robot exhibits highly adaptive behavior in response to environmental changes. The results obtained are expected to shed new light on the design of autonomous decentralized control systems.

Autonomous decentralized control is a key concept for understanding the mechanism underlying the adaptive and versatile behavior of animals. Although the design methodology of decentralized control based on a dynamical system approach that can impart adaptability by using coupled oscillators has been proposed in previous studies, it cannot reproduce the versatility of animal behaviors comprehensively. Therefore, our objective is to understand behavioral versatility from the perspective of well-coordinated rhythmic and non-rhythmic movements. To this end, we focus on ophiuroids as simple “best” model living organisms that exhibit spontaneous role assignment of rhythmic and non-rhythmic arm movements, and we model such arm movements by using an active rotator model that can describe both oscillatory and excitatory properties. Simulation results show that the spontaneous role assignment of arm movements is successfully realized by using the proposed model, and the simulated locomotion is qualitatively equivalent to the locomotion of real ophiuroids. This fact can potentially facilitate a better understanding of the control mechanism responsible for behavioral versatility in animals.

Despite its appeal, a systematic design of an autonomous decentralized control system is yet to be realized. To bridge this gap, we have so far employed a “back-to-basics” approach inspired by true slime mold, a primitive living creature whose behavior is purely controlled by coupled biochemical oscillators similar to central pattern generators (CPGs). Based on this natural phenomenon, we have successfully developed a design scheme for local sensory feedback control leading to system-wide adaptive behavior. This design scheme is based on a “discrepancy function” that extracts the discrepancies among the mechanical system (i:e:, body), control system (i:e:, brain-nervous system) and the environment. The aim of this study is to intensively investigate the validity of this design scheme by applying it to the control of a quadruped locomotion. Simulation results show that the quadruped robot exhibits remarkably adaptive behavior in response to environmental changes and changes in body properties. Our results shed a new light on design methodologies for CPG-based decentralized control of various types of locomotion.

Decentralized control is a key concept to understand the mechanism of versatile and adaptive locomotion of animals under various environments. However, a systematic design of an autonomous decentralized control system has not been well established, because previous studies mainly focused on the control of phase relationship among body parts, i.e., phasic control, but not on the control of the spatio-temporal dynamics of muscle tonus, i.e., tonic control. In this paper, we propose a decentralized control scheme that enables to reconcile phasic control and tonic control, by taking serpentine locomotion as a practical example. Through modeling and simulations, we show that well-balanced coupling between the phasic and tonic control is crucial for the generation of adaptive and efficient locomotion.