An important goal in behaviour analytics is to connect disease state or genome
variation with observable differences in behaviour. Despite advances in sensor
technology and imaging, informative behaviour quantification remains challenging.
The nematode worm C. elegans provides a unique opportunity to test analysis
approaches because of its small size, compact nervous system, and the availability
of large databases of videos of freely behaving animals with known genetic differences.
Despite its relative simplicity, there are still no reports of generative models
that can capture essential differences between even well-described mutant strains.
Here we show that a multilayer recurrent neural network (RNN) can produce diverse
behaviours that are difficult to distinguish from real worms’ behaviour and
that some of the artificial neurons in the RNN are interpretable and correlate with
observable features such as body curvature, speed, and reversals. Although the
RNN is not trained to perform classification, we find that artificial neuron responses
provide features that perform well in worm strain classification.