A neural network model of Caenorhabditis elegans and simulation of chemotaxis-related information processing in the neural network

The nematode Caenorhabditis elegans (C. elegans) is a simple multi-cellular organism consisting of around 1,000 cells including 302 neurons, and is the only creature whose connectome has been fully mapped. For these reasons, (C. elegans) is ideal for studying information-processing mechanisms embedded in the neural network. This paper proposes a neural network model of C. elegans with the actual neural structure preserved to simulate the organism's attraction to sodium chloride (NaCl). To implement attractant behavior, the organism's neural network must calculate the temporal and spatial gradients of NaCl concentration; however, the mechanism behind this complex information processing in the worm's neural network has not yet been fully elucidated. As a first step to analyze the information processing mechanism, the parameters of the neural network model were adjusted using the backpropagation through time (BPTT) algorithm, and the neural network model was verified for its ability to generate temporal and spatial gradients. Simulation for neuron ablation experiment was then carried out, and the results exhibited same trends as the biological experiment indicating that our approach can be used to predict the results of biological experiments, and can therefore be used as a tool to provide guidelines for such experiments.