In this study, we focused on neurons involved in the chemotaxis of Caenorhabditis elegans and constructed a neural network model based on the known connectome. The parameters of the model were adjusted using the Back Propagation Through Time (BPTT) algorithm and verified for the ability to generate internal representations of spatiotemporal gradients required for chemotaxis. We simulated the neuronal ablation experiment using the adjusted parameters and showed that the effects of sensory neurons (ASER and ASEL) on the internal representations of spatiotemporal gradient calculations can be predicted.
Neural Network Model of Caenorhabditis elegans and Simulation of Chemotaxis-related Information Processing in the Neural Network
A neural network model of Caenorhabditis elegans and simulation of chemotaxis-related information processing in the neural network, Kazuma Sakamoto, Zu Soh, Michiyo Suzuki, Yuichi Kurita, and Toshio Tsuji, IEEE SAI Intelligent Systems Conference 2015, London, UK, November 10-11, 2015.