Bennet Sakelaris

Recently, a novel form of C. elegans associative learning was reported, where nematodes learn to reach a target arm in a testing maze, after they have successfully located reward in a similar training maze (Gourgou et al. 2021). We present a mathematical model of C. elegans chemosensory and locomotive circuitry, activated by reward-released chemical cues, and coupled with motor neurons and assumed neuronal components. We show that such a circuit can mediate learning by acquiring a turning bias even after a single training session. We rely on experimental results, and we verify the realistic nature of our biophysical model by comparing our findings to new experimental data, extracted through the implementation of a custom-made maze tracking algorithm.