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Researchers simultaneously recorded neural activity in the hippocampus (dCA1) and orbitofrontal cortex (mOFC) while rats learned new goal locations daily in a maze task. They found that both brain regions encode goal information but with distinct profiles: dCA1 showed stronger spatial coding while mOFC showed more dynamic learning-related updates. The regions demonstrated increased synchronization during navigation, and combining their activity improved prediction of learning state compared to either region alone, suggesting coordinated brain network activity supports flexible goal-directed behavior.
Why it matters
Understanding how the brain integrates spatial maps with changing goals could inform treatments for navigation and memory disorders, and may provide insights for developing more adaptive artificial intelligence systems that can flexibly update objectives while maintaining stable environmental representations.
by Jiasong Li, Lingwei Tang, Xinhang Wei, Yumin Chen, Haibing Xu
Flexible goal‑directed navigation requires integrating changing goal information with a stable spatial map, yet how cortico-hippocampal circuits accomplish this remains unclear. We simultaneously recorded medial orbitofrontal cortex (mOFC) and dorsal CA1 (dCA1) while rats learned daily changing goal locations on a cheeseboard maze. Rats rapidly learned new goal locations and retained memory for them in the post‑probe session. Both regions contained goal‑related neuronal representations, but their profiles differed: dCA1 showed stronger spatial specificity, whereas mOFC showed more prominent learning‑related updating of goal‑related activity. Combining dCA1 and mOFC activity improved decoding of behavioral stage and learning block relative to either region alone, consistent with complementary contributions to ongoing behavior and learning state. Across learning, these population‑level differences were accompanied by stronger theta‑range synchronization and theta–gamma coupling during navigation than during goal periods. A recurrent network model with dynamic synaptic efficacy captured qualitative features of efficient acquisition and flexible goal updating, providing a candidate computational framework for how learning‑related temporal coordination could contribute to adaptive navigation.