Computer Science

Graduate Research Forum Details

Extracting Neural Network Motifs for C. elegans chemotaxis

Abstract

Although anatomical connectivity of the nematode Caenorhabditis elegans has been almost completely described, determination of the neurophysiological basis of behavior is just beginning. Here, we performed a stochastic search to determine neural network parameters sufficient for a model worm to exhibit chemotaxis, a form of spatial orientation behavior in which turning probability is modulated by the rate of change of chemical concentration. To better comprehend network solutions, we developed a novel method (Neural Dynamic Clustering) to identify neural dynamic motifs. We identified two types of motifs, one of which had been previously identified, and validated the behavior generated by the motifs against experimental chemotaxis.