Math Department and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 52900, Israel.
Bioinformatics. 2010 Mar 1;26(5):647-52. doi: 10.1093/bioinformatics/btq015. Epub 2010 Jan 16.
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.
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.
由秀丽隐杆线虫神经元连接引起的网络拓扑结构与大多数常见的随机网络不同。秀丽隐杆线虫的神经元位置以前被解释为最优的，以诱导所需的网络布线。我们在这里提出一个补充的解释，即网络布线是直接由局部随机突触形成过程的结果。
我们表明，基于神经元之间物理距离的模型可以解释秀丽隐杆线虫神经网络结构，具体来说，我们证明了一个基于简单的几何突触形成概率和抑制短相干周期的模型可以解释秀丽隐杆线虫神经网络的特性。我们建议该模型作为讨论神经网络生成的初始框架，并作为开发更高级生物模型的第一步。为了测量网络中的圆频率，提出了一种新颖的基于图论的圆长度测量算法。
