Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome - Valencia Urbina, Carlos E.; Cannas, Sergio A.; Gleiser, Pablo M. (Frontiers in Neurorobotics, 2023)
We analyze the neural dynamics and its relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to keep track simultaneously of the detailed microscopic dynamics of all the neurons and also register the actions of the robot in the environment in real time, avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the microscopic dynamics associated with the emergent macroscopic behavior, some of which have already been observed in biological worms. These results suggest that some basic complex macroscopic behaviors observed in living beings can be almost completely determined by the underlying structure of the associated neural network, being relatively independent of the detailed neuronal dynamics.