2017/12/08 by Richard Mayne
Agricultural and Biological Sciences · Computer Science · Engineering · #Algorithm #Artificial intelligence #Biocrusts and Microbial Ecology #Biology #Computer science #Constrained Shortest Path First #Engineering #Graph #K shortest path routing #Key (lock) #Path (computing) #Physarum polycephalum #Plant and Biological Electrophysiology Studies #Process (computing) #Range (aeronautics) #Shortest path problem #Slime Mold and Myxomycetes Research #Theoretical computer science #cs.ET
paper · pdf · doi:10.1007/978-3-319-77510-4_14
To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 2018
arxiv created 2017/12/08 · openalex created_date 2017/12/22 · openalex publication_date 2018/01/01 · arxiv updated 2019/05/14 · openalex updated_date 2026/08/05
Shortest path problems are a touchstone for evaluating the computing performance and functional range of novel computing substrates. Much has been published in recent years regarding the use of biocomputers to solve minimal path problems such as route optimisation and labyrinth navigation, but their outputs are typically difficult to reproduce and somewhat abstract in nature, suggesting that both experimental design and analysis in the field require standardising. This chapter details laboratory experimental data which probe the path finding process in two single-celled protistic model organisms, Physarum polycephalum and Paramecium caudatum, comprising a shortest path problem and labyrinth navigation, respectively. The results presented illustrate several of the key difficulties that are encountered in categorising biological behaviours in the language of computing, including biological variability, non-halting operations and adverse reactions to experimental stimuli. It is concluded that neither organism examined are able to efficiently or reproducibly solve shortest path problems in the specific experimental conditions that were tested. Data presented are contextualised with biological theory and design principles for maximising the usefulness of experimental biocomputer prototypes.