As quantitative data become available for a particular form or function in the nervous system, it is advisable to attempt to assimilate the information in a comprehensive model of the underlying mechanisms and their interactions. This project consists in the development of such models and the necessary analytical and mathematical techniques for their implementation and testing in several areas of experimental investigation carried out by LNLC members and in other laboratories. The most succinct and informative way to represent the shape of neurons appears to be a computer program that constructs simulations of dendritic trees. Our simulation relies on the discovery that local dendritic diameter largely determines the probability of branching and terminating. This year, we significantly improved the accuracy of the model by incorporating a newly discovered dependence of these probabilities on path distance from the soma. An objective method of deriving model parameters from data was introduced. A simplified dendrite model, that explains dependencies on branch order, was formulated. The distribution of branch angles and a simple rule for generating the meander of branches were discovered.