Our paper on modelling maze learning is accepted at the European Journal of Neuroscience!

Our work, with Bennet Sakelaris* as the leading author and in collaboration with Victoria Booth, Professor of Mathematics, was just accepted for publication at the European Journal of Neuroscience!

Congratulations, Bennet! And thanks, everyone, for the great work!

Bennet G. Sakelaris, Li Zongyu, Sun Jiawei, Banerjee Shurjo, Booth Victoria, Gourgou Eleni: “Modelling learning in C. elegans chemosensory and locomotive circuitry for T-maze navigation”.  

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 behavior to 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 behavior 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 behavior. 

*Bennet is currently a PhD student in Applied Mathematics, Northwestern University, Evanston, IL.

The model captures C. elegans food location ability (simulation results, baited maze, training)

The model captures C. elegans maze learning, that occurs after training (simulation results, testing)