Summary: The processes whereby developing neurones acquire morphological features that are common to entire populations (thereby allowing the definition of neuronal types) are still poorly understood. A mathematical model of neuronal arborizations may be useful to extract basic parameters or organization rules, hence helping to achieve a better understanding of the underlying growth processes.
We present a parsimonious statistical model, intended to describe the topological organization of neuritic arborizations with a minimal number of parameters. It is based on a probability of splitting which depends only on the centrifugal order of segments. We compare the predictions made by the model of several topological properties of neurones with the corresponding actual values measured on a sample of honeybee (olfactory) antennal lobe neurones grown in primary culture, described in a previous study. The comparison is performed for three populations of segments corresponding to three neuronal morphological types previously identified and described in this sample. We show that simple assumptions together with the knowledge of a very small number of parameters allow the topological reconstruction of representative (bi-dimensional) biological neurones. We discuss the biological significance (in terms of possible factors involved in the determinism of neuronal types) of both common properties and cell-type specific features, observed on the neurones and predicted by the model.