Morphological analysis and modeling of neuronal dendrites.

Morphological data on two classes of neurons from mammalian midbrain have quantitatively been analyzed for dendritic shape parameters. Their frequency distributions were used to optimize the parameters of a dendritic growth model which describes dendritic morphology by a stochastic growth process of segment branching. The model assumes randomness with respect to both the selection of the branching segment out of the tree segments and the occurrence of the branching event in time. Model-generated trees have shape properties closely matching the observed ones. The dendritic trees of each of the two classes of neurons are represented by a specific set of growth model parameters, thus achieving morphological data compression.