Summary Recent evidence suggests that the hippocampus may integrate overlapping memories into relational representations, or schemas, that link indirectly related events and support flexible memory expression. Here we explored the nature of hippocampal neural population representations for multiple features of events and the locations and contexts in which they occurred. Hippocampal networks developed hierarchical organizations of associated elements of related but separately acquired memories within a context, and distinct organizations for memories where the contexts differentiated object-reward associations. These findings reveal neural mechanisms for the development and organization of relational representations.
It has long been hypothesized that a primary function of the hippocampus is to discover and exploit temporal relationships between events. Previously, it has been reported that sequences of "time cells" in the hippocampus extend for tens of seconds. Other studies have shown that neuronal firing in the hippocampus fluctuates over hours and days. Both of these mechanisms could enable temporal encoding of events over very different timescales. However, thus far, these two classes of phenomena have never been observed simultaneously, which is necessary to ascribe broad-range temporal coding to the hippocampus. Using in vivo calcium imaging in unrestrained mice, we observed sequences of hippocampal neurons that bridged a 10 s delay. Similar sequences were observed over multiple days, but the set of neurons participating in those sequences changed gradually. Thus, the same population of neurons that encodes temporal information over seconds can also be used to distinguish periods of time over much longer timescales. These results unify two previously separate paradigms of temporal processing in the hippocampus that support episodic memory.
Summary To provide a substrate for remembering where in space events have occurred, place cells must reliably encode the same positions across long time scales. However, in many cases place cells exhibit instability by randomly reorganizing their place fields between experiences, challenging this premise. Recent evidence suggests that, in some cases, instability could also arise from coherent rotations of place fields, as well as from random reorganization. To investigate this possibility, we performed in vivo calcium imaging in dorsal hippocampal region CA1 of freely moving mice while they explored two arenas with different geometry and visual cues across eight days. The two arenas were rotated randomly between sessions, and then connected, allowing us to probe how cue rotations, the integration of new information about the environment, and the passage of time concurrently influenced the spatial coherence of place fields. We found that spatially coherent rotations of place field maps in the same arena predominated, persisting up to six days later, and that they frequently rotated in a manner that did not match that of the arena rotation. Furthermore, place field maps were flexible, as mice frequently employed a similar, coherent configuration of place fields to represent each arena despite their differing geometry and eventual connection. These results highlight the ability of the hippocampus to retain consistent relationships between cells across long time scales and suggest that, in many cases, apparent instability might result from a coherent rotation of place fields.
Trajectory-dependent splitter neurons in the hippocampus encode information about a rodent’s prior trajectory during performance of a continuous alternation task. As such, they provide valuable information for supporting memory-guided behavior. Here, we employed single-photon calcium imaging in freely moving mice to investigate the emergence and fate of trajectory-dependent activity through learning and mastery of a continuous spatial alternation task. In agreement with others, the quality of trajectory-dependent information in hippocampal neurons correlated with task performance. We thus hypothesized that, due to their utility, splitter neurons would exhibit heightened stability. We find that splitter neurons were more likely to remain active and retained more consistent spatial information across multiple days than other neurons. Furthermore, we find that both splitter neurons and place cells emerged rapidly and maintained stable trajectory-dependent/spatial activity thereafter. Our results suggest that neurons with useful functional coding exhibit heightened stability to support memory guided behavior.
The population of hippocampal neurons actively coding space continually changes across days as mice repeatedly perform tasks. Many hippocampal place cells become inactive while other previously silent neurons become active, challenging the idea that stable behaviors and memory representations are supported by stable patterns of neural activity. Active cell replacement may disambiguate unique episodes that contain overlapping memory cues, and could contribute to reorganization of memory representations. How active cell replacement affects the evolution of representations of different behaviors within a single task is unknown. We trained mice to perform a delayed nonmatching to place task over multiple weeks, and performed calcium imaging in area CA1 of the dorsal hippocampus using head‐mounted miniature microscopes. Cells active on the central stem of the maze “split” their calcium activity according to the animal's upcoming turn direction (left or right), the current task phase (study or test), or both task dimensions, even while spatial cues remained unchanged. We found that, among reliably active cells, different splitter neuron populations were replaced at unequal rates, resulting in an increasing number of cells modulated by turn direction and a decreasing number of cells with combined modulation by both turn direction and task phase. Despite continual reorganization, the ensemble code stably segregated these task dimensions. These results show that hippocampal memories can heterogeneously reorganize even while behavior is unchanging.
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