Linking the connectome to action: Emergent dynamics in a robotic model of C. elegans
We analyse the neural dynamics and its relation with the emergent behaviour 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 robot movement in complex environments, avoiding collisions with obstacles. Working with a robotic model 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. This allowed us to study the interplay between connectome and complex behaviors. We found that some basic features of the global neural dynamics and their correlation with behaviour observed in the worm appear spontaneously in the robot, suggesting they are just an emergent property of the connectome.
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