Modelling learning in Caenorhabditis elegans chemosensory and locomotive circuitry for T‐maze navigation. (9th January 2022) - Sakelaris, Bennet G.; Li, Zongyu; Sun, Jiawei; Banerjee, Shurjo; Booth, Victoria; Gourgou, Eleni
Abstract: Recently, a new type of Caenorhabditis elegans associative learning was reported, where nematodes learn to reach a target arm in an empty T‐maze, after they have successfully located reward (food) in the same side arm of a similar, baited, training maze. Here, we present a simplified mathematical model of C. elegans chemosensory and locomotive circuitry that replicates C. elegans navigation in a T‐maze and predicts the underlying mechanisms generating maze learning. Based on known neural circuitry, the model circuit responds to food‐released chemical cues by modulating motor neuron activity that drives simulated locomotion. We show that, through modulation of interneuron activity, such a circuit can mediate maze learning by acquiring a turning bias, even after a single training session. Simulated nematode maze navigation during training conditions in food‐baited mazes and during testing conditions in empty mazes is validated by comparing simulated behaviour with new experimental video data, extracted through the implementation of a custom‐made maze tracking algorithm. Our work provides a mathematical framework for investigating the neural mechanisms underlying this novel learning behaviour in C. elegans . Model results predict neuronal components involved in maze and spatial learning and identify target neurons and potential neural mechanisms for future experimental investigations into this learning behaviour. Abstract : We present a mathematical framework and a model of C. elegans chemosensory and locomotive circuitry that replicates nematodes' navigation and learning in a T‐maze and predicts the underlying mechanisms. We show that, through modulation of interneuron activity, such a circuit can mediate learning by acquiring a turning bias and strengthening key neuronal connections. Model results predict neuronal components involved in maze learning and identify potential targets for future experiments.