  Given the inner complexity of the human nervous system, insight into the
dynamics of brain activity can be gained from understanding smaller and simpler
organisms, such as the nematode C. Elegans. The behavioural and structural
biology of these organisms is well-known, making them prime candidates for
benchmarking modelling and simulation techniques. In these complex neuronal
collections, classical, white-box modelling techniques based on intrinsic
structural or behavioural information are either unable to capture the profound
nonlinearities of the neuronal response to different stimuli or generate
extremely complex models, which are computationally intractable. In this paper
we show how the nervous system of C. Elegans can be modelled and simulated with
data-driven models using different neural network architectures. Specifically,
we target the use of state of the art recurrent neural networks architectures
such as LSTMs and GRUs and compare these architectures in terms of their
properties and their accuracy as well as the complexity of the resulting
models. We show that GRU models with a hidden layer size of 4 units are able to
accurately reproduce with high accuracy the system's response to very different
stimuli.
