On the Loading of Slime Mold Physarum polycephalum with Microparticles for Unconventional Computing Application

被引：6
作者：
Cifarelli A. [1 ,2 ]
Dimonte A. [2 ]
Berzina T. [2 ]
Erokhin V. [2 ]
机构：
[1] Department of Physics and Earth Science, University of Parma, Viale Usberti 7A, Parma
[2] CNR-IMEM, Parco Area delle Scienze, 37A, Parma
来源：
Cifarelli, Angelica (angelica.cifarelli@fis.unipr.it) | 1600年 / Springer Science and Business Media, LLC卷 / 04期
关键词：
Adaptive network; Microparticle transportation; Physarum polycephalum; Unconventional computing;
D O I：
10.1007/s12668-013-0124-3
中图分类号：
学科分类号：
摘要：
The plasmodium of Physarum polycephalum is a large single cell visible with the naked eye. The plasmodium realizes a pattern of protoplasmic veins which span sites of sources of nutrients, producing efficient network structures like cycles and Steiner minimum trees. Besides, the plasmodium can embed different chemicals; therefore, it should be possible to program the plasmodium to realize deterministic adaptive network and spatial distribution of nanoscale and microscale materials. The transported particles can be used for the modification of the physical properties of the system (electrical, optical, magnetic) facilitating the readout of the information, processed by the slime mold. Experiments with polystyrene microparticles and MnCO3 microparticles demonstrate that the plasmodium of Physarum can propagate nanoscale objects using a number of distinct mechanisms. The results of our experiments could be employed in the field of the unconventional computing and bio-computing application devices, using Physarum network as scaffolds for the development of hybrid nanocircuits and microcircuits and devices. © 2014, Springer Science+Business Media New York.
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页码：92 / 96
页数：4
相关论文
共 23 条
  • [1] Adleman L., Computing with DNA, Scientific American, 279, 2, pp. 54-61, (1998)
  • [2] Von Neumann J., Theory of self-reproducing automata, (1966)
  • [3] Von Neumann J., The Computer and the Brain, Yale University Press, (1958)
  • [4] Rozenberg G., Back T., Kok J.N., Handbook of natural computing, (2012)
  • [5] Schank R.C., Childers P.G., The cognitive computer: on language, learning, and artificial intelligence, (1984)
  • [6] Self J., Artificial intelligence and human learning: intelligent computer-aided instruction, (1988)
  • [7] Chua L., Memristor—the missing circuit element, IEEE Trans Circuit Theory, 18, pp. 507-519, (1971)
  • [8] Strukov D.B., Snider G.S., Stewart D.R., Williams R.S., The missing memristor found, Nature, 453, pp. 80-83, (2008)
  • [9] Erokhin V., Berzina T., Fontana M.P., Hybrid electronic device based on polyaniline–polyethylene oxide junction, Journal of Applied Physics, 97, (2005)
  • [10] Erokhin V., Fontana M.P., Thin film electrochemical memristive systems for bio-inspired computation, Journal of Computational and Theoretical Nanoscience, 8, pp. 313-330, (2011)
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