How can we efficiently link natural behavior or cognitive functions with the underlying neural population dynamics? To gain insight in this question, we develop ML-based tools that can infer low-dimensional trajectories underlying both neural population activity and behavior. We use for instance diffusion models for efficient generation of realistic data (both continuous voltage and discrete spikes), and recurrent neural networks (RNNs) as interpretable models of neural dynamics. Additionally, we capitalize on recent developments in machine learning that have enabled real-time behavioral tracking of animals in unconstrained lab settings and model neural activity during natural behavior.