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Caenorhabditis elegans (C. elegans) illustrated remarkable behavioralplasticities including complex non-associative and associative learningrepresentations. Understanding the principles of such mechanisms presumablyleads to constructive inspirations for the design of efficient learningalgorithms. In the present study, we postulate a novel approach on modelingsingle neurons and synapses to study the mechanisms underlying learning in theC. elegans nervous system. In this regard, we construct a precise mathematicalmodel of sensory neurons where we include multi-scale details from genes, ionchannels and ion pumps, together with a dynamic model of synapses comprised ofneurotransmitters and receptors kinetics. We recapitulate mechanosensoryhabituation mechanism, a non-associative learning process, in which elements ofthe neural network tune their parameters as a result of repeated input stimuli.Accordingly, we quantitatively demonstrate the roots of such plasticity in theneuronal and synaptic-level representations. Our findings can potentially giverise to the development of new bio-inspired learning algorithms.

Non-Associative Learning Representation in the Nervous System of the Nematode Caenorhabditis elegans