Machine Learning Methods for Studying Latent Neural Activity Dynamics

The paper presents a comprehensive survey of machine learning methods for decoding latent neural activity dynamics, focusing on Latent Variable Models (LVMs) ranging from linear dynamical systems to deep generative models like Transformers and diffusion models. It categorizes the literature into single-region latent dynamics, multi-region communication, and behavior-aligned modeling, highlighting techniques such as RNNs and Neural ODEs, while addressing benchmarks and challenges in identifying causal links in neural communication. This survey is significant for practitioners as it outlines critical methodologies and evaluation metrics that can enhance neural decoding and understanding of brain dynamics.