Machine Learning Methods for Studying Latent Neural Activity Dynamics

arXiv:2606.10530v1 Announce Type: new Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons. In this paper, we provide a comprehensive survey that outlines the trajectory of Latent Variable Models (LVMs) from early state-space models to more recent deep generative models. We organize the literature into three closely related domains: (1) Single-Region Latent Dynamics, which includes models such as linear dynamical systems to more complex dynamics represented by Recurrent Neural N

Source: arXiv cs.LG — read the full report at the original publisher.

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