Skip to main content

Research Repository

Advanced Search

Approximation of statistical analysis and estimation by morphological adaptation in a model of slime mould

Jones, Jeff; Adamatzky, Andrew


Jeff Jones


True slime mould Physarum polycephalum approximates a range of complex computations via growth and adaptation of its protoplasmic transport network, stimulating a large body of recent research into how such a simple organism can perform such complex feats. The properties of networks constructed by slime mould are known to be influenced by the local distribution of stimuli within its environment. But can the morphological adaptation of slime mould yield any information about the global statistical properties of its environment? We explore this possibility using a particle based model of slime mould.We demonstrate how morphological adaptation in blobs of virtual slime mould may be used as a simple computational mechanism that can coarsely approximate statistical analysis, estimation and tracking. Preliminary results include the approximation of the geometric centroid of 2D shapes, approximation of arithmetic mean from spatially represented sorted and unsorted data distributions, and the estimation and dynamical tracking of moving object position in the presence of noise contaminated input stimuli. The results suggest that it is possible to utilise collectives of very simple components with limited individual computational ability (for example swarms of simple robotic devices) to extract statistical features from complex datasets by means of material adaptation and sensorial fusion.


Jones, J., & Adamatzky, A. (2015). Approximation of statistical analysis and estimation by morphological adaptation in a model of slime mould. International Journal of Unconventional Computing, 11(1), 37-62

Journal Article Type Article
Publication Date Jan 1, 2015
Journal International Journal of Unconventional Computing
Print ISSN 1548-7199
Publisher Old City Publishing
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Pages 37-62
Keywords morphological computation, physarum polycephalum, centroid, arithmetic mean, noisy estimation, sensorial fusion
Publisher URL


You might also like

Downloadable Citations