Unsupervised learning of control signals and their encodings in $\textit{C. elegans}$ whole-brain recordings

Recent whole brain imaging experiments on $\textit{C. elegans}$ has revealed that the neural population dynamics encode motor commands and stereotyped transitions between behaviors on low dimensional manifolds. Efforts to characterize the dynamics on this manifold have used piecewise linear models to describe the entire state space, but it is unknown how a single, global dynamical model can generate the observed dynamics. Here, we propose a control framework to achieve such a global model of the dynamics, whereby underlying linear dynamics is actuated by sparse control signals. This method learns the control signals in an unsupervised way from data, then uses $\textit{ Dynamic Mode Decomposition with control}$ (DMDc) to create the first global, linear dynamical system that can reconstruct whole-brain imaging data. These control signals are shown to be implicated in transitions between behaviors. In addition, we analyze the time-delay encoding of these control signals, showing that these transitions can be predicted from neurons previously implicated in behavioral transitions, but also additional neurons previously unidentified. Moreover, our decomposition method allows one to understand the observed nonlinear global dynamics instead as linear dynamics with control. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.