Temporal sequence geometry enables odor recognition and generalization

Abstract

Neural activity sequences are observed throughout the brain, yet their computational roles remain elusive. In mammalian olfaction, olfactory bulb mitral and tufted cells (MTCs) encode odors with precisely timed activity patterns that tile the respiration cycle. While animals can identify odors independently of concentration within the first 100 milliseconds of inhalation, the structure governing these sequences and the role of activity extending beyond this early window remains unclear. Here, using 2-photon calcium imaging with sub-sniff resolution, we show that odor-evoked MTC sequences propagate as wavefronts through a low-dimensional odor tuning space, where timing is predicted by the tuning similarity between neurons rather than physical location. While early portions of these sequences are concentration-invariant, providing a stable anchor for odor identity, later portions systematically co-activate similarly tuned MTCs across odors, tracing the geometry of the tuning manifold. We propose a role for this later sequential activity in training the piriform cortex to learn perceptually generalizable odor representations. Using a model of Hebbian learning through sequences (HeLSeq), we demonstrate that sequential activity can reinforce synaptic connections from similarly tuned MTCs onto common piriform cortical neurons, enabling rapid generalization to novel odors from the earliest moments of inhalation. These findings support a geometric view of activity sequences and establish a general principle by which temporal sequences scaffold unsupervised manifold learning between brain networks.