bioRxivNeurosciencePreprint
The Primate Hippocampus Constructs a Temporal Scaffold Anchored to Behavioral Events
Jon W. RueckemanniD, Yoni BrowningiD, Autumn J Mallory, Brian Kim, Adrienne L. FairhalliD, Elizabeth A. BuffaloiD
Abstract
The hippocampus has been attributed a range of divergent functions, including roles in memory1,2 and navigation3-5, but how its moment-to-moment neuronal activity supports cognition remains poorly understood. The activity of individual hippocampal neurons is correlated with numerous perceptual features and task variables6-14, raising the question of whether these response properties reflect distinct mechanisms or support a single generalized computation. Here, we show that these diverse response properties reflect a unified organizing principle in which the hippocampus segments experience into discrete events, with population activity transitioning between discrete neural states at behaviorally salient moments. Recording from monkeys performing a virtual spatial alternation task, we found that population activity did not evolve smoothly over time but instead shifted abruptly at each relevant event. These discontinuities segmented activity into distinct ensembles, effectively chunking separate task epochs. Notably, many neuronal responses persisted across visually distinct environments, demonstrating that these dynamics reflect abstract task structure rather than specific sensory features. These results reveal that the hippocampus constructs a temporal scaffold anchored to relevant behavioral events, with each neural state tracking a distinct task phase. This organizational principle may explain the diverse neural correlates observed across studies: rather than individually encoding perceptual or behavioral variables, hippocampal neurons collectively signal the current phase of a behavioral sequence. Our findings suggest that the hippocampus parses experience into meaningful elements and tracks "position" within a learned behavioral structure.
- organism: non-human primate
- assay: imaging
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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