Understanding the neural implementation of complex human behaviors is one of the major goals of neuroscience. To this end, it is crucial to find a true representation of the neural data, which is challenging due to the high complexity of the task and the low signal-to-ratio (SNR) of the signals. Here, we propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors. The key idea is to alig…