Network mechanisms underlying representational drift in area CA1 of hippocampus

Devalle, Federico; Roxin, Alex

doi:10.1101/2022.11.10.515946

Cited by 4 publications

(5 citation statements)

References 74 publications

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“…A possible explanation for the relationship between lap-by-lap dynamics and the tendency to remap is that those PFs with less spatial precision simply receive higher variability in the activation of the CA3 inputs they receive (Zutshi et al, 2022;Davoudi and Foster, 2019;Devalle and Roxin, 2022). Alternatively, evidence suggests that all CA1 pyramidal cells may receive synaptic input regarding all locations in an environment from CA3 (Grienberger et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

The Precision of Place Fields Governs Their Fate across Epochs of Experience

Chiu,

Dong,

Krishnan

et al. 2023

eNeuro

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Spatial memories are represented by hippocampal place cells during navigation. This spatial code is dynamic, undergoing changes across time – known as representational drift – and across changes in internal state, even while navigating the same spatial environment with consistent behavior. A dynamic code may provide the hippocampus a means to track distinct epochs of experience that occur at different times or during different internal states and update spatial memories. Changes to the spatial code include place fields that remap to new locations and place fields that vanish, while others are stable. However, what determines place field fate across epochs remains unclear. We measured the lap-by-lap properties of place cells in mice during navigation for a block of trials in a rewarded virtual environment. We then determined the position of the place fields in another block of trials in the same spatial environment either separated by a day (a distinct temporal epoch) or during the same session but with reward removed to change reward expectation (a distinct internal state epoch). We found that place cells with remapped place fields across epochs tended to have lower spatial precision during navigation in the initial epoch. Place cells with stable or vanished place fields tended to have higher spatial precision. We conclude that place cells with less precise place fields have greater spatial flexibility, allowing them to respond to, and track, distinct epochs of experience in the same spatial environment, while place cells with precise place fields generally preserve spatial information when their fields reappear.Significance StatementSpatial representations in the hippocampus support spatial memory and are comprised of place cells and their corresponding place fields. Recent work has revealed that spatial representations are not as stable as once thought. Instead, they undergo large changes across days—a phenomenon called representational drift. We present results showing an association between the spatial precision of place fields on one day and their drift across subsequent days. We find this association holds true when spatial representations drift in response to changes in reward expectation. We discuss a synaptic-level conceptual model that links place field precision with drift. This study advances our understanding of the mechanisms behind representational drift, providing insight into how the hippocampus updates spatial memories for continual learning.

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Section: Resultsmentioning

confidence: 99%

The Precision of Place Fields Governs Their Fate across Epochs of Experience

Chiu,

Dong,

Krishnan

et al. 2023

eNeuro

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Section: Discussionmentioning

confidence: 99%

Place field precision during an episode predicts place field fate across episodes

Chiu

Dong

Krishnan

et al. 2023

Preprint

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Spatial memories are represented by hippocampal place cells during navigation. This spatial code is dynamic, undergoing changes across time - known as drift - and across changes in internal state, even while navigating the same spatial environment with consistent behavior. A dynamic spatial code may be a way for the hippocampus to track distinct episodes that occur at different times or during different internal states and update spatial memories. Changes to the spatial code include place fields that remap to new locations and place fields that vanish, while others are stable. However, what determines place field fate across episodes remains unclear. We measured the lap-by-lap properties of place cells in mice during navigation for a block of trials in a rewarded virtual environment. We then had mice navigate the same spatial environment for another block of trials either separated by a day (a distinct temporal episode) or during the same session but with reward removed to change reward expectation (a distinct internal state episode). We found that, as a population, place cells with remapped place fields across episodes had lower spatial precision during navigation in the initial episode. Place cells with stable or vanished place fields generally had higher spatial precision. We conclude that place cells with less precise place fields have greater spatial flexibility, allowing them to respond to, and track, distinct episodes in the same spatial environment, while place cells with precise place fields generally preserve spatial information when their fields reappear.

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“…Such an arrangement suggests the full population vector of LEC neurons drifts in a multiscale fashion over time. Within HC, representational drift of neuronal ensembles has been demonstrated over even wider scales, from seconds to months (Devalle & Roxin, 2022;Geva et al, 2023;Hainmueller & Bartos, 2018;Keinath et al, 2022;Kinsky et al, 2018; J. S. Lee et al, 2020;Liu et al, 2022;Mankin et al, 2015;Manns et al, 2007;Mau et al, 2018Mau et al, , 2020A. Rubin et al, 2015;Umbach et al, 2022;Y.…”

Section: Behavioral and Neural Evidence Of Multiscale Drift In Tempor...mentioning

confidence: 99%

Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe.

Antony,

Liu,

Zheng

et al. 2024

Psychological Review

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Some neural representations gradually change across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization-generally ascribed only to the neocortex-can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning.

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Network mechanisms underlying representational drift in area CA1 of hippocampus

Cited by 4 publications

References 74 publications

The Precision of Place Fields Governs Their Fate across Epochs of Experience

The Precision of Place Fields Governs Their Fate across Epochs of Experience

Place field precision during an episode predicts place field fate across episodes

Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe.

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