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

Niizato, Takayuki; Shirakawa, Tomohiro; Gunji, Yukio Pegio

doi:10.1016/j.biosystems.2010.02.003

Cited by 19 publications

(16 citation statements)

References 9 publications

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“…In our previous paper (Gunji et al, 2008;Niizato et al, 2010), we reported that the system implementing the transportation of the vacant-particle can approximate amebic motion and solve the minimal path of a maze. If the quick transformation from sol to gel is not implemented, the transportation of the vacant-particle is never constrained, which entails the collapse of the aggregation.…”

Section: Discussionmentioning

confidence: 99%

“…Our basic model, which is here called by VP, (Gunji et al, 2008;Niizato et al, 2010) is defined in a two-dimensional cell space, and the state of a cell can be defined as a vacant state, sol, gel and a vacant-particle state. A Physarum is expressed as an aggregation (AG) of cells that is composed of sol and/or gel cells.…”

Section: Vp: Basic Model Based On Vacant-particle Transportationmentioning

confidence: 99%

“…We previously proposed an asynchronous cellular automaton for the Physarum in this context (Gunji et al, 2008;Niizato et al, 2010). The model Physarum is composed of locally transported particles constrained by the trajectory of the transportation that is implemented by asynchronous updating.…”

Section: Introductionmentioning

confidence: 99%

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

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Section: Discussionmentioning

confidence: 99%

Section: Vp: Basic Model Based On Vacant-particle Transportationmentioning

confidence: 99%

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

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et al. 2011

Journal of Theoretical Biology

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“…The properties of CELL are loop cutting, pseudopodium withdrawal and choice of shorter path. Niizato et al [60] claimed that their original model remained insufficient for showing how network formation occurs. So they tried to create a model of a cell that has the ability to form a network.…”

Section: Cellular Automata Basicsmentioning

confidence: 98%

Hardware Acceleration of Cellular Automata Physarum polycephalum Model

Dourvas

Tsompanas

Sirakoulis

et al. 2015

Parallel Process. Lett.

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During the past decades, computer science experts were inspired from the study of biological organisms. Moreover, bio-inspired algorithms were produced that many times can give excellent solutions with low computational cost in complex engineering problems. In our case, the plasmodium of Physarum polycephalum is capable of finding the shortest path solution between two points in a labyrinth. In this study, we implement a Cellular Automata (CA) model in hardware, which attempts to describe and, moreover, mimic the behavior of the plasmodium in a maze. Beyond the successful implementation of the CA-based Physarum model in software, in order to take full advantage of the inherent parallelism of CA, we focus on a Field Programmable Gate Array (FPGA) implementation of the proposed model. Namely, two different implementations were considered here. Their difference is on the desired precision produced by the numerical representation of CA model parameters. Based on the corresponding results of the shortest path in the labyrinth,the modeling efficiency of both approaches was compared depending on the resulting error propagation. The presented FPGA implementations succeed to take advantage of the CA's inherit parallelism and improve the performance of the CA algorithm when compared with software in terms of computational speed and power consumption. As a result, the implementations presented here, can also be considered as a preliminary CA-based Physarum polycephalum IP core which produces a biological inspired solution to the shortest-path problem.

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“…For example, the mazesolving by the amoeba is performed by selection of the shorter tubes connecting two food sources at the entrance and the exit of the maze, after full search of the maze space [16]. For these tubular dynamics, some models have already been proposed [26,27,29,28,5,6,18]. Furthermore, by utilizing the locomotive and tactic activities of the sheets, the plasmodium can be used in some logical operations [1,2].…”

Section: Adaptive Computational and Memory Ability Of The Plasmodiummentioning

confidence: 98%

An associative learning experiment using the plasmodium of Physarum polycephalum

Shirakawa

Gunji

Miyake

2011

Nano Communication Networks

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

Cited by 19 publications

References 9 publications

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

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

Hardware Acceleration of Cellular Automata Physarum polycephalum Model

An associative learning experiment using the plasmodium of Physarum polycephalum

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