Automated, predictive, and interpretable inference of C. elegans escape dynamics
This work addresses the challenge of understanding intricate biological behaviors in neuroscience, though it is incremental as it applies existing AI methods to a specific domain.
The researchers tackled the problem of modeling the complex escape behavior of C. elegans in response to temperature changes by using a phenomenological approach and automated inference from data, resulting in an accurate and interpretable model that predicts worm dynamics.
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.