A Connectome-Based Digital Twin Caenorhabditis elegans Capable of Intelligent Sensorimotor Behavior

Abstract

Despite possessing a simple nervous system, the Caenorhabditis elegans exhibits remarkably intelligent behavior. However, the underlying mechanisms involved in sensory processing and decision making, which contribute to locomotion behaviors, remain unclear. In order to investigate the coordinated function of neurons in achieving chemotaxis behavior, we have developed a digital twin of the C. elegans that combines a connectome-based neural network model with a realistic digital worm body. Through training the digital worm using offline chemotaxis behavioral data generated with a PID controller, we have successfully replicated faithful sinusoidal crawling and intelligent chemotaxis behavior, similar to real worms. By ablating individual neurons, we have examined their roles in modulating or contributing to the regulation of behavior. Our findings highlight the critical involvement of 119 neurons in sinusoidal crawling, including B-type, A-type, D-type, and PDB motor neurons, as well as AVB and AVA interneurons, which was experimentally demonstrated. We have also predicted the involvement of DD04 and DD05 neurons and the lack of relevance of DD02 and DD03 neurons in crawling, which have been confirmed through experimentation. Additionally, head motor neurons, sublateral motor neurons, layer 1 interneurons, and layer 1 and layer 5 sensory neurons are expected to play a role in crawling. In summary, we present a novel methodological framework that enables the establishment of an animal model capable of closed-loop control, faithfully replicating realistic animal behavior. This framework holds potential for examining the neural mechanisms of behaviors in other species.