Journal Article

Random distance dependent attachment as a model for neural network generation in the <i>Caenorhabditis elegans</i>

Royi Itzhack and Yoram Louzoun

in Bioinformatics

Volume 26, issue 5, pages 647-652
Published in print March 2010 | ISSN: 1367-4803
Published online January 2010 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btq015
Random distance dependent attachment as a model for neural network generation in the Caenorhabditis elegans

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology

GO

Show Summary Details

Preview

Motivation: 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.

Contact: royi.its@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5462 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.