Algorithmic description of hippocampal granule cell dendritic morphology

Recent efforts in computational neuroanatomy have aimed at accurately reproducing all relevant statistical details of dendritic morphology with stochastic models based on local rules and parameters measured from real neurons. Here we present a solution of this problem for dentate gyrus granule cells based on a hidden Markov algorithm. The correctness of the model is supported by the statistical agreement between distributions of emergent parameters measured from population of traced and virtual neurons. The algorithm relies on two local hidden variables, one of which might be associated with dendritic microtubules, and another may represent the time of development.