Classification of Caenorhabditis Elegans Locomotion Behaviors With Eigenfeature-Enhanced Long Short-Term Memory Networks.

The free-living nematode Caenorhabditis elegans is an ideal model for understanding behavior and networks of neurons. Experimental and quantitative analyses of neural circuits and behavior have led to system-level understanding of behavioral genetics and process of transformation from sensory integration in stimulus environments to behavioral outcomes. The ability to differentiate locomotion behavior between wild-type and mutant Caenorhabditis elegans strains allows precise inference on and gaining insights into genetic and environmental influences on behaviors. This paper presents an eigenfeature-enhanced deep-learning method for classifying the dynamics of locomotion behavior of wild-type and mutant Caenorhabditis elegans. Classification results obtained from public benchmark time-series data of eigenworms illustrate the superior performance of the new method over several existing classifiers. The proposed method has potential as a useful artificial-intelligence tool for automated identification of the nematode worm behavioral patterns aiming at elucidating molecular and genetic mechanisms that control the nervous system.