Abstract
How learning generalizes across contexts is a fundamental question in neuroscience, with broad implications for adaptive behavior and cognition. The hippocampus (HPC) plays a key role in contextual learning, but HPC cells exhibit place-specific activity that reorganizes, or 'remaps' across environments, raising the question of how stable, task-relevant representations can be preserved. Here, we used calcium imaging to monitor hippocampal neuron activity as rats performed a conditioning task across multiple spatial contexts. We asked whether hippocampal neurons, which encode both spatial locations and task-relevant features, could maintain stable representations of the task despite remapping of spatial codes. To assess representational consistency, we applied dimensionality reduction and machine learning to construct manifold embeddings of population-level HPC activity. We found that task-related neural representations remained stable across different environments, even as spatial representations shifted. Moreover, these representations exhibited similar geometric structure not only across contexts within individual animals, but also across different animals, suggesting the presence of a shared neural syntax for associative learning in the hippocampus. These findings bridge a critical gap between memory and navigation research, revealing how stable cognitive representations emerge from dynamic spatial codes. They provide new insight into conserved hippocampal encoding strategies, with potential relevance for understanding flexible memory, learning, and their disruption in neuropsychiatric disorders.