Decomposed Linear Dynamical Systems (dLDS) models reveal instantaneous, context-dependent dynamic connectivity in C. elegans

Mounting evidence indicates that neural “tuning” can be highly variable within an individual across time and across individuals. Furthermore, modulatory effects can change the relationship between neurons as a function of behavioral or other conditions, meaning that the changes in activity (the derivative) may be as important as the activity itself. Current computational models cannot capture the nonstationarity and variability of neural coding, preventing the quantitative evaluation of these effects. We therefore present a novel approach to analyze these effects in a well-studied organisms, C. elegans , leveraging recent advances in dynamical systems modeling: decomposed Linear Dynamical Systems (dLDS). Our approach enables the discovery of multiple parallel neural processes on different timescales using a set of linear operators that can be recombined in different ratios. Our model identifies “dynamic connectivity”, describing patterns of dynamic neural interactions in time. We use these patterns to identify instantaneous, contextually-dependent, hierarchical roles of neurons; discover the underlying variability of neural representations even under seemingly discrete behaviors; and learn an aligned latent space underlying multiple worms’ activity. By analyzing individual worms and neurons, we found that (1) changes in interneuron connectivity mediate efficient task-switching and (2) changes in sensory neuron connectivity show a mechanism of adaptation.