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Given the inner complexity of the human nervous system, insight into thedynamics of brain activity can be gained from understanding smaller and simplerorganisms, such as the nematode C. Elegans. The behavioural and structuralbiology of these organisms is well-known, making them prime candidates forbenchmarking modelling and simulation techniques. In these complex neuronalcollections, classical, white-box modelling techniques based on intrinsicstructural or behavioural information are either unable to capture the profoundnonlinearities of the neuronal response to different stimuli or generateextremely complex models, which are computationally intractable. In this paperwe show how the nervous system of C. Elegans can be modelled and simulated withdata-driven models using different neural network architectures. Specifically,we target the use of state of the art recurrent neural networks architecturessuch as LSTMs and GRUs and compare these architectures in terms of theirproperties and their accuracy as well as the complexity of the resultingmodels. We show that GRU models with a hidden layer size of 4 units are able toaccurately reproduce with high accuracy the system's response to very differentstimuli.

Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data