In this paper, the authors propose a simple model of cell motility inspired by the plasmodium of Physarum polycephalum. The model focuses on the “softness” of aggregations of protoplasm. The model... Show More

In this paper, the authors propose a simple model of cell motility inspired by the plasmodium of Physarum polycephalum. The model focuses on the “softness” of aggregations of protoplasm. The model has only two parameters, yet generates a variety of final states, as well as the morphological changes of Physarum according to the condition of the culture medium.

It is important to understand living systems, mimic them, and design them. A directed network can represent a neural signal flow that living systems have. To understand the network, the authors... Show More

It is important to understand living systems, mimic them, and design them. A directed network can represent a neural signal flow that living systems have. To understand the network, the authors extract two types of community structure by converting directed network of C.elegans into bipartite network. The extracted community structure and its connections give some properties of communities. Namely, the neural network of C.elegans has 12 and 10 deeply correlated communities and many single size communities. Also, it has many small collecting communities and a few large repeating communities in itself.

The plasmodium of Physarum polycephalum is a unicellular and multinuclear giant amoeba. In this paper, the authors investigate four allometric laws in the exploratory behavior of the plasmodium, and... Show More

The plasmodium of Physarum polycephalum is a unicellular and multinuclear giant amoeba. In this paper, the authors investigate four allometric laws in the exploratory behavior of the plasmodium, and integrate them into one schema based on the dynamics of cytoplasmic streaming. This study reveals a novel function of the tubular structure of the plasmodium, shedding new light on the adaptive behavior of the organism.

Recently, new empirical research of flocking behavior has been accumulated. Scale-free proportion has revealed how a flock can appear to behave as if it has one mind and body. The notion of... Show More

Recently, new empirical research of flocking behavior has been accumulated. Scale-free proportion has revealed how a flock can appear to behave as if it has one mind and body. The notion of scale-free proportion implies that the correlated domain within a flock is not constant size, but is proportional to flock size. Scale-free proportion can be explained by previous models, such as BOIDS based on the fixed radius neighborhood where an agent interacts with others if the critical valued parameter and a huge neighborhood are given. However, it is hard to explain under the normal neighborhood condition. The authors propose a new computational model that, although also based on BOIDS, incorporates mutual anticipation, which is implemented by modeling the resonance between the potential transitions available to each agent, allowing overlap between them. Via mutual anticipation, this model implements interactions not only among individuals but also between individuals and the field. The authors show that this model reveals the dynamic and robust structure of a flock or swarm, as well as scale-free proportion over a wide range of the flock sizes, comparing previous models, and that its predictions correlate well with empirical field data.

The authors propose a novel model for a swarm in which inherent noise positively contribute to generate a swarm. In the authors’ model diverse behaviors of individuals are mutually anticipated to... Show More

The authors propose a novel model for a swarm in which inherent noise positively contribute to generate a swarm. In the authors’ model diverse behaviors of individuals are mutually anticipated to give rise to robust collective behavior. Because a swarm is generated due to inherent perturbation, a swarm can be maintained even under highly perturbed conditions. Thus, the model reveals robust rather than stable collective behavior. The authors elaborate behavior of the model with respect to density and polarization. The authors show that mutual anticipation structure can be expressed as a fixed point with respect to a particular operation derived by equivalence relation, a collection of the fixed points can form a particular algebraic structure, called a lattice, and a swarm as a mobile network can be characterized by the structure of a lattice.

The objective of this paper is to solve the dynamic plane coverage problem by the movement of multiple robots, for example, sprinkling water to a large field by several vehicles or aircrafts, in... Show More

The objective of this paper is to solve the dynamic plane coverage problem by the movement of multiple robots, for example, sprinkling water to a large field by several vehicles or aircrafts, in which all of the points in the field should be covered by the robots in an almost equal density. One of the ways to solve it is the swarm leading control method, in which one of the robots, called a target, moves along a path in the field, and all the other robots move around the target with a fixed distance. In the process, the topology of the robots affects to the efficiency of the dynamic plane coverage problem. If the topology is a tight one, the swarm can be stable but the coverage area can be limited in a small area. On the other hand, if it is a loose one, an opposite thing can be happened. In this paper, the relation between the topology and the efficiency is discussed numerically.

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors... Show More

Statistical machine learning approach to understand human behaviors has been attracting considerable amounts of attention in recent years. If the authors understand more about humans, the authors can make more user-friendly machines. In this paper, the authors propose the driver recognition method from their record of manipulations using support vector machine. The authors demonstrate the efficiency of the authors’ method using the Segway. The performance of the recognition is quite good especially when the authors introduce the pre-process with FFT.