title: Random distance dependent attachment as a model for neural network generation in the Caenorhabditis elegans.
abstract: BACKGROUND
  The topology of the network induced by the neurons connectivity's in the Caenorhabditis elegans differs from most common random networks. The neurons positions of the C.elegans have been previously explained as being optimal to induce the required network wiring. We here propose a complementary explanation that the network wiring is the direct result of a local stochastic synapse formation process.
  
  
  RESULTS
  We show that a model based on the physical distance between neurons can explain the C.elegans neural network structure, specifically, we demonstrate that a simple model based on a geometrical synapse formation probability and the inhibition of short coherent cycles can explain the properties of the C.elegans' neural network. We suggest this model as an initial framework to discuss neural network generation and as a first step toward the development of models for more advanced creatures. In order to measure the circle frequency in the network, a novel graph-theory circle length measurement algorithm is proposed.
authors.affiliation: Math Department and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 52900, Israel.
authors.forename: Royi
authors.initials: R
authors.lastname: Itzhack
authors.forename: Yoram
authors.initials: Y
authors.lastname: Louzoun
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*