Modeling dendritic morphological complexity of deep layer cat superior colliculus neurons

Dendritic complexity of deep layer cat superior colliculus neurons has been studied by means of a stochastic model for dendritic outgrowth with randomly branching and elongating neurites. Branching probabilities are assumed to depend on the position and the number of segments in the growing tree. It is demonstrated that the shape properties of model generated dendrites conform closely to those of the observed trees. These "ndings make plausible that (i) dendritic development proceeds following a "rst phase of elongation and branching, and a second phase of elongation only, and (ii) newly formed segments after a branching event have a short initial length. 2001 Elsevier Science B.V. All rights reserved.