  True slime mould Physarum polycephalum approximates a range of complex
computations via growth and adaptation of its proto- plasmic 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 in- fluenced 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 approx- imate
statistical analysis, estimation and tracking. Preliminary results include the
approximation of the geometric centroid of 2D shapes, ap- proximation 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 ex- ample swarms of simple
robotic devices) to extract statistical features from complex datasets by means
of material adaptation and sensorial fusion.
