Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors.

ABSTRACT: The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistica

SUBMITTER: Wen Q 

PROVIDER: S-EPMC2713752 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature