CrEIMBO: Cross Ensemble Interactions in Multi-view Brain Observations
arxiv(2024)
摘要
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.
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