Noise-induced bistability in a simple mutual inhibition system.

In this study, we study noise-induced bistability in a simple bivariate mutual inhibition system with slow fluctuating responses to external signals. We give a general condition that the marginal stationary probability density of one of the two variables experiences a transition from a unimodal shape to a bimodal one. We show that the transition occurs even when the stationary probability density of the response to external signals is monotone. The mechanism for the transition is investigated in terms of the calculation of the mean first passage time. We also discuss the genericity of the transition mechanism.

Biased Lévy-walk pattern in the exploratory behavior of the Physarum plasmodium.

The plasmodium of Physarum polycephalum is a unicellular and multinuclear giant amoeba. The plasmodium has the ability to sense and adapt to many kinds of environmental stimuli, and its optimization behavior in closed spaces has been analyzed extensively. However, few studies have tested the behavior of the plasmodium in an open spaces, despite the biological importance of the adaptability of biological entities in such conditions. Accordingly, we established an experimental setup with a very large and strictly homogeneous substrate and observed the long-term exploratory behavior of the plasmodium. As a result, we found that the movement of the plasmodium was consistent with a Lévy-walk, but with anisotropic bias.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Kanizsa illusory contours appearing in the plasmodium pattern of Physarum polycephalum.

The plasmodium of Physarum polycephalum is often used in the implementation of non-linear computation to solve optimization problems, and this organismal feature was not used in this analysis to compute perception and/or sensation in humans. In this paper, we focused on the Kanizsa illusion, which is a well-known visual illusion resulting from the differentiation-integration of the visual field, and compared the illusion with the adaptive network in the plasmodium of P. polycephalum. We demonstrated that the network pattern mimicking the Kanizsa illusion can be produced by an asynchronous automata-fashioned model of the foraging slime mold and by the real plasmodia of P. polycephalum. Because the protoplasm of the plasmodium is transported depending on both local and global computation, it may contain differentiation-integration processes. In this sense, we can extend the idea of perception and computation.

An adaptive and robust biological network based on the vacant-particle transportation model.

A living system reveals local computing by referring to a whole system beyond the exploration-exploitation dilemma. The slime mold, Physarum polycephalum, uses protoplasmic flow to change its own outer shape, which yields the boundary condition and forms an adaptive and robust network. This observation suggests that the whole Physarum can be represented as a local protoplasmic flow system. Here, we show that a system composed of particles, which move and are modified based upon the particle transformation that contains the relationship between the parts and the whole, can emulate the network formed by Physarum. This system balances the exploration-exploitation trade-off and shows a scale-free sub-domain. By decreasing the number of particles, our model, VP-S, can emulate the Physarum adaptive network as it is attracted to a food stimulus. By increasing the number of particles, our model, VP-D, can emulate the pattern of a growing Physarum. The patterns produced by our model were compared with those of the Physarum pattern quantitatively, which showed that both patterns balance exploration with exploitation. This model should have a wide applicability to study biological collective phenomena in general.

A model of network formation by Physarum plasmodium: interplay between cell mobility and morphogenesis.

The plasmodium of Physarum polycephalum has attracted much attention due its intelligent adaptive behavior. In this study, we constructed a model of the organism and attempted to simulate its locomotion and morphogenetic behavior. By modifying our previous model, we were able to get closer to the actual behavior. We also compared the behavior of the model with that of the real organism, demonstrating remarkable similarity between the two.

Minimal model of a cell connecting amoebic motion and adaptive transport networks.

A cell is a minimal self-sustaining system that can move and compute. Previous work has shown that a unicellular slime mold, Physarum, can be utilized as a biological computer based on cytoplasmic flow encapsulated by a membrane. Although the interplay between the modification of the boundary of a cell and the cytoplasmic flow surrounded by the boundary plays a key role in Physarum computing, no model of a cell has been developed to describe this interplay. Here we propose a toy model of a cell that shows amoebic motion and can solve a maze, Steiner minimum tree problem and a spanning tree problem. Only by assuming that cytoplasm is hardened after passing external matter (or softened part) through a cell, the shape of the cell and the cytoplasmic flow can be changed. Without cytoplasm hardening, a cell is easily destroyed. This suggests that cytoplasmic hardening and/or sol-gel transformation caused by external perturbation can keep a cell in a critical state leading to a wide variety of shapes and motion.

Class-specific binding of two aminoacyl-tRNA synthetases to annexin, a Ca2+- and phospholipid-binding protein.

Annexins are a family of Ca2+/phospholipid-binding proteins that have diverse functions. To understand the function of annexin in Physarum polycephalum, we searched for its binding proteins. Here we demonstrate the presence of two novel annexin-binding proteins. The homology search of partial amino acid sequences of these two proteins identified them as aminoacyl-tRNA synthetases (ARSs). Furthermore, antibody against aminoacyl-tRNA synthetases cross-reacted with one of two proteins. Our results imply the interaction between intracellular membrane dynamics and protein translation system, and may give a clue to understand the mechanism of some myositis diseases, which have been known to produce autoantibodies against ARSs.