| First Author | Parrini M | Year | 2024 |
| Journal | Curr Biol | Volume | 34 |
| Issue | 1 | Pages | 79-91.e4 |
| PubMed ID | 38101403 | Mgi Jnum | J:358733 |
| Mgi Id | MGI:7575532 | Doi | 10.1016/j.cub.2023.11.047 |
| Citation | Parrini M, et al. (2024) Circuit mechanisms of navigation strategy learning in mice. Curr Biol 34(1):79-91.e4 |
| abstractText | Navigation tasks involve the gradual selection and deployment of increasingly effective searching procedures to reach targets. The brain mechanisms underlying such complex behavior are poorly understood, but their elucidation might provide insights into the systems linking exploration and decision making in complex learning. Here, we developed a trial-by-trial goal-related search strategy analysis as mice learned to navigate identical water mazes encompassing distinct goal-related rules and monitored the strategy deployment process throughout learning. We found that navigation learning involved the following three distinct phases: an early phase during which maze-specific search strategies are deployed in a minority of trials, a second phase of preferential increasing deployment of one search strategy, and a final phase of increasing commitment to this strategy only. The three maze learning phases were affected differently by inhibition of retrosplenial cortex (RSC), dorsomedial striatum (DMS), or dorsolateral striatum (DLS). Through brain region-specific inactivation experiments and gain-of-function experiments involving activation of learning-related cFos+ ensembles, we unraveled how goal-related strategy selection relates to deployment throughout these sequential processes. We found that RSC is critically important for search strategy selection, DMS mediates strategy deployment, and DLS ensures searching consistency throughout maze learning. Notably, activation of specific learning-related ensembles was sufficient to direct strategy selection (RSC) or strategy deployment (DMS) in a different maze. Our results establish a goal-related search strategy deployment approach to dissect unsupervised navigation learning processes and suggest that effective searching in navigation involves evidence-based goal-related strategy direction by RSC, reinforcement-modulated strategy deployment through DMS, and online guidance through DLS. |
