From labels to latents: revealing state-dependent hippocampal computations and non-spatial replay with Jump Latent Variable Model

Neural activity is often interpreted by imposing external labels (e.g., position during locomotion) and decoding within that space (e.g. replay). While powerful, such supervision can mask structure in the data that do not correspond to the label. Unsupervised methods, in turn, often assume smooth latent dynamics and miss genuine discontinuities. We introduce a conceptually simple, computationally efficient latent variable model that infers both (i) the latent variables organizing population activity and (ii) whether their dynamics are continuous or fragmented in time. Fitting reduces to an expectation-maximization (EM) procedure that alternates two operations familiar to systems neuroscience - tuning-curve estimation and label decoding - without requiring external labels. Applied to rodent hippocampal recordings, our method identifies a continuity-fragmentation axis that characterizes population activity across sleep-wake brain states, modulated by cholinergic inputs. For a spatial navigation task, the model reveals distinct population patterns at the same physical position that supervised spatial decoding fails to detect. Moreover, the model enables replay analysis beyond spatial trajectories, and finds prevalent pre-play events associated with immobile and off-maze behaviors and post-task replay of locomotion. By not relying on externally imposed spatial labels, our approach exposes structure that supervised approaches obscure and provides a powerful tool for datasets lacking behavioral tracking.