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

Mounting empirical evidence indicates that neural "tuning" can be highly variable within an individual across time and especially across individuals. Furthermore, modulatory effects can change the relationship between neurons in the brain as a function of behavioral or other conditions, meaning that the changes in activity (the derivative) may be as important as the activity itself. However, current computational models fail to capture the nonstationarity and variability of neural coding, preventing the quantitative evaluation of these effects, especially during individuals adaptation to their environments. Here we present a novel way to study the effects of adaptation in one of the most well-studied organisms, C. elegans, leveraging recent advances in dynamical systems modeling, specifically decomposed Linear Dynamical Systems (dLDS).Our approach enables the discovery of multiple parallel neural processes on different timescales using a low-dimensional 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 a single aligned latent space underlying multiple individual worms activity. By analyzing individual worms and neurons, we found evidence that 1) changes in interneuron connectivity mediate efficient task-switching and 2) changes in sensory neuron connectivity show a mechanism of adaptation.