The plasmodium of Physarum polycephalum is a unicellular multinucleate giant amoeba. The plasmodium exhibits many types of taxes and uses these movements to adapt to its environment. In our previous study, we revealed...
详细信息
The plasmodium of Physarum polycephalum is a unicellular multinucleate giant amoeba. The plasmodium exhibits many types of taxes and uses these movements to adapt to its environment. In our previous study, we revealed that the plasmodium also exhibits magnetotaxis. In this study, we further investigated factors related to this magnetotaxis for use as a controlling factor in bio-computing and proposed a hypothesis on magnetotactic response of the plasmodium. Additionally, as a demonstration of magnetically controlled bio-computing, we constructed a magnetically controlled Physarum logic gate.
The plasmodium of the true slime mold Physarum polycephalum is a unicellular, multinuclear giant amoeba that is attracting much attention in the field of bio-computing as a living computing substrate. To observe how t...
详细信息
The plasmodium of the true slime mold Physarum polycephalum is a unicellular, multinuclear giant amoeba that is attracting much attention in the field of bio-computing as a living computing substrate. To observe how the plasmodium of Physarum polycephalum responds to a contradictory situation, in which there is no single optimal solution, we applied bi-modal stimuli, consisting of a mixture of an attractant and a repellent, to plasmodia. The plasmodia showed diverse responses that could not be explained by a simple model of the stimulus-response system. We constructed a simulation model of the behavior that replicated the behavioral diversity with a simple combination of molecular apparatuses. In summary, we demonstrated the diversity of the behaviors of the plasmodium and how these behaviors may arise.
作者：
Kim, Song-JuAono, MasashiHara, MasahikoHanyang Univ
Fus Technol Ctr 5F RIKEN HYU Collaborat Res Ctr Fluctoorder Funct Res TeamAdv Sci InstRIKEN Seoul 133791 South Korea RIKEN
RIKEN HYU Collaborat Res Ctr Adv Sci Inst Fluctoorder Funct Res Team Wako Saitama 3510198 Japan
We propose a model - the "tug-of-war (TOW) model" - to conduct unique parallel searches using many nonlocally-correlated search agents. The model is based on the property of a single-celled amoeba, the true ...
详细信息
We propose a model - the "tug-of-war (TOW) model" - to conduct unique parallel searches using many nonlocally-correlated search agents. The model is based on the property of a single-celled amoeba, the true slime mold Physarum, which maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a "nonlocal correlation" among the branches, i.e., volume increment in one branch is immediately compensated by volume decrement(s) in the other branch(es). This nonlocal correlation was shown to be useful for decision making in the case of a dilemma. The multi-armed bandit problem is to determine the optimal strategy for maximizing the total reward sum with incompatible demands, by either exploiting the rewards obtained using the already collected information or exploring new information for acquiring higher payoffs involving risks. Our model can efficiently manage the "exploration-exploitation dilemma" and exhibits good performances. The average accuracy rate of our model is higher than those of well-known algorithms such as the modified epsilon-greedy algorithm and modified softmax algorithm, especially, for solving relatively difficult problems. Moreover, our model flexibly adapts to changing environments, a property essential for living organisms surviving in uncertain environments. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
We propose a model - the "tug-of-war (TOW) model" - to conduct unique parallel searches using many nonlocally correlated search agents. The model is based on the property of a single-celled amoeba, the true ...
详细信息
ISBN:
(纸本)9783642135224
We propose a model - the "tug-of-war (TOW) model" - to conduct unique parallel searches using many nonlocally correlated search agents. The model is based on the property of a single-celled amoeba, the true slime mold Physarum, which maintains a constant intracellular resource volume while collecting environmental information by concurrently expanding and shrinking its branches. The conservation law entails a "nonlocal correlation" among the branches, i.e., volume increment in one branch is immediately compensated by volume decrement(s) in the other branch(es). This nonlocal correlation was shown to be useful for decision making in the case of a dilemma. The multi-armed bandit problem is to determine the optimal strategy for maximizing the total reward sum with incompatible demands. Our model can efficiently manage this "exploration exploitation dilemma" and exhibits good performances. The average accuracy rate of our model is higher than those of well-known algorithms such as the modified c-greedy algorithm and modified soft max algorithm.
We propose an amoeba-based knowledge discovery or data mining system, that is implemented using an amoeboid organism and an associated control system. The amoeba system can be considered as one of the new non-trad...
详细信息
We propose an amoeba-based knowledge discovery or data mining system, that is implemented using an amoeboid organism and an associated control system. The amoeba system can be considered as one of the new non-traditional computing paradigms, and it can perform intriguing, massively parallel computing that utilizes the chaotic behavior of the amoeba. Our system is a hybrid of a traditional Anowledge-based unit implemented on an ordinary computer and an amoeba-based search unit, with an interface of an optical control unit. The solutions in our system can have one-to-one mapping to solutions of other well known areas such as neural networks and genetic algorithms. This mapping feature allows the amoeba to use and apply techniques developed in other areas. Various forms of knowledge discovery processes are introduced. Also, a new type of knowledge discovery technique, called "autonomous meta-problem solving," is discussed.
An amoeboid organism, Physarum, exhibits rich spatiotemporal oscillatory behavior and various computational capabilities. Previously, the authors created a recurrent;neurocomputer incorporating the amoeba as a computi...
详细信息
ISBN:
(纸本)9783642037443
An amoeboid organism, Physarum, exhibits rich spatiotemporal oscillatory behavior and various computational capabilities. Previously, the authors created a recurrent;neurocomputer incorporating the amoeba as a computing substrate to solve optimization problems. In this paper, considering the amoeba to be a network of oscillators coupled such that they compete for constant amounts of resources, we present a model of the amoeba-based neurocomputer. The model generates a number of oscillation modes and produces not only simple behavior to stabilize a single mode but also complex behavior to spontaneously switch among different modes, which reproduces well the experimentally observed behavior of the amoeba. To explore the significance of the complex behavior, we set a test problem used to compare computational performances of the oscillation modes. The problem is a kind of optimization problem of how to allocate a limited amount of resource to oscillators such that conflicts among them can be minimized. We show that the complex behavior enables to attain a wider variety of solutions to the problem and produces better performances compared with the simple behavior.
暂无评论