Analyzing the scalability of R'-tree regarding the neuron touch detection task

Abstract: A common task within research of neuronal morphology is neuron touch detection, that is finding the points in space where two neurites approach each other to form a synapse. In order to make efficient use of cache memory, it is important to store points that are close in space close in memory. One data structure that aims to tackle this complication is the R'-tree. In this thesis, a spatial query for touch detection was implemented and the scalability of the R'-tree was estimated on realistic neuron densities and extrapolated to explore execution times on larger volumes. It was found that touch detection on this data structure scaled much like the optimal algorithm in 3D-space and more specifically that the computing power needed to analyze a meaningful portion of the human cortex is not readily available.