Talpir, I.; Livneh, Y.

The insular cortex is involved in diverse processes including bodily homeostasis, emotions, and cognition. Yet little is known about how it processes information at the level of neuronal populations, precluding an understanding of the computations it performs. We leveraged recent advances in machine learning to study insular cortex population activity patterns (i.e., neuronal manifold) in mice performing goal-directed behaviors. We find that the insular cortex activity manifold is remarkably consistent across different animals and under different motivational states. Activity dynamics within the neuronal manifold are highly stereotyped during rewarded trials, enabling robust prediction of trial outcomes across different mice, and across various natural and artificial motivational states. Comparing goal-directed behavior with self-paced free consumption, we find that the stereotyped activity patterns reflect goal-directed reward anticipation, and not licking, taste, or valence. These findings reveal a core computation in insular cortex that could explain its involvement in pathologies involving aberrant motivations.