CrEIMBO: Cross Ensemble Interactions in Multi-view Brain Observations

Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects -- thus presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics underlying cognitive function. Current methods, however, often fail to fully harness the richness of such data as they either provide an uninterpretable representation (e.g., via "black box" deep networks) or over-simplify the model (e.g., assume stationary dynamics or analyze each session independently). Here, instead of regarding asynchronous recordings that lack alignment in neural identity or brain areas as a limitation, we exploit these diverse views of the same brain system to learn a unified model of brain dynamics. We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO identifies the composition of the per-session neural ensembles through graph-driven dictionary learning and models the ensemble dynamics as a latent sparse time-varying decomposition of global sub-circuits, thereby capturing non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits while maintaining representation interpretability due to sharing sub-circuits across sessions. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics, capturing cross-subject and inter- and intra-area variability, in high-density electrode recordings of humans performing a memory task.