A central objective in neuroscience is to understand how the brain orchestrates movement. Recent advances in automated tracking technologies have made it possible to document behavior with unprecedented temporal resolution and scale, generating rich datasets which can be exploited to gain insights into the neural control of movement. One common approach is to identify stereotypical motor primitives using cluster analysis. However, this categorical description can limit our ability to model the effect of more continuous control schemes. Here we take a control theoretic approach to behavioral modeling and argue that movements can be understood as the output of a controlled dynamical system. Previously, models of movement dynamics, trained solely on behavioral data, have been effective in reproducing observed features of neural activity. These models addressed specific scenarios where animals were trained to execute particular movements upon receiving a prompt. In this study, we extend this approach to analyze the full natural locomotor repertoire of an animal: the zebrafish larva. Our findings demonstrate that this repertoire can be effectively generated through a sparse control signal driving a latent Recurrent Neural Network (RNN). Our model's learned latent space preserves key kinematic features and disentangles different categories of movements. To further interpret the latent dynamics, we used balanced model reduction to yield a simplified model. Collectively, our methods serve as a case study for interpretable system identification, and offer a novel framework for understanding neural activity in relation to movement.

MOTIVATION: How to partition a dataset into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be broadly applied. One recent method, clusterdp, based on search of density peaks, can be applied successfully to cluster many kinds of data, but it is not fully automatic, and fails on some simple data distributions. RESULTS: We propose an alternative approach, clusterdv, which estimates density dips between points, and allows robust determination of cluster number and distribution across a wide range of data, without any manual parameter adjustment. We show that this method is able to solve a range of synthetic and experimental datasets, where the underlying structure is known, and identifies consistent and meaningful clusters in new behavioral data. AVAILABILITY AND IMPLEMENTATION: The clusterdv is implemented in Matlab. Its source code, together with example datasets are available on: https://github.com/jcbmarques/clusterdv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.