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. 2024 Mar 29;383(6690):1478-1483.
doi: 10.1126/science.adk8261. Epub 2024 Mar 28.

Selection of experience for memory by hippocampal sharp wave ripples

Wannan Yang  1   2 , Chen Sun  3 , Roman Huszár  1   2 , Thomas Hainmueller  1   4 , Kirill Kiselev  2 , György Buzsáki  1   2
Affiliations

Selection of experience for memory by hippocampal sharp wave ripples

Wannan Yang et al. Science. .

Abstract

Experiences need to be tagged during learning for further consolidation. However, neurophysiological mechanisms that select experiences for lasting memory are not known. By combining large-scale neural recordings in mice with dimensionality reduction techniques, we observed that successive maze traversals were tracked by continuously drifting populations of neurons, providing neuronal signatures of both places visited and events encountered. When the brain state changed during reward consumption, sharp wave ripples (SPW-Rs) occurred on some trials, and their specific spike content decoded the trial blocks that surrounded them. During postexperience sleep, SPW-Rs continued to replay those trial blocks that were reactivated most frequently during waking SPW-Rs. Replay content of awake SPW-Rs may thus provide a neurophysiological tagging mechanism to select aspects of experience that are preserved and consolidated for future use.

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Conflict of interest statement

Competing interests: The authors declare that they have no competing interests.

Figures

Fig. 1.
Fig. 1.. Trial block identity can be decoded from the distinct temporal evolution of neuronal population activity.
(A) Illustration of two (out of four) shanks of the dual-sided probe. (B) The figure-eight maze task, where mice alternated between left and right arms for water reward (blue droplets). The mouse’s trajectory along maze corridors is color coded by its linearized position. (C) Trials 1 (right arm traversal) and 2 (left arm traversal) during the figure-eight maze task. (Top) The linearized position of the mouse. Red, right traversal; green, left traversal. (Bottom) A raster plot of 422 pyramidal cells that were simultaneously recorded from the right dorsal CA1 region of a mouse’s hippocampus, sorted by seqNMF, and color coded according to the linearized position. This follows the same color scheme as in (B). (D) Trials 3 to 31 [of 70 trials in total (fig. S1)] of an example session. E, error trials. (E) (Left) Running trajectory of a mouse in the figure-eight maze. (Right) UMAP embedding of population activity. Each point corresponds to the low-dimensional representation of one-binned spiking data. Both are colored according to the mouse’s position, as in (B). (F) The same session as (E), but the running trajectory and neural manifold are colored by trial block number [see color key in (H)]. (G) Neural manifold of the same session as in (E) and (F) made by using semisupervised UMAP trained on blocks of five trials. The manifold is colored by linearized position. (H) Same as (G), but the neural manifold is colored by trial block number. (I) Confusion matrix of trial block decoding from the original high-dimensional space [same session as (A) to (H)]. (J) Trial block identity decoding errors from across all sessions using UMAP, PCA, Bayesian decoding, decoding from original high-dimensional space with Euclidean distance (EUD), and cosine similarity (COS) as distance metrics. Each dot in the violin plot indicates one session, pooled across n = 26 sessions from six animals. Decoding error was measured in units of trial block in which five trials were binned to one trial block.
Fig. 2.
Fig. 2.. Contribution of single cell and subpopulation of cells to the trial block identity coding.
(A) Trial-by-trial firing rate of an example neuron during left-arm (top) and right-arm trials (bottom) in the figure-eight maze. Color indicates the trial block number. (B) Raster plot of an example neuron (top) and a simulated neuron (bottom), generated from a Poisson process model based on the across-trial mean firing rate of the real neuron (materials and methods). (C) (Top) UMAP manifold generated from the population activity of all the neurons in one example session. (Bottom) UMAP embedding from the simulated population activity. Both are colored by linearized position. (D) Same as (C) but colored by the trial block number [see color key in (E)]. (E) Neural manifold of the same session as (C) and (D) made by using the semisupervised UMAP trained on blocks of five trials. (F) Confusion matrix of trial block decoding results for the real (top) and simulated population (bottom). For decoding results from other decoding methods, see fig. S7, E to G. (G) Trial block decoding error of real (purple) and simulated (green) data across all sessions (decoded from the original high-dimensional space) (***P < 10−10, unpaired t test; n = 26 sessions from six animals). The dashed line indicates the chance level from trial-shuffled data (fig. S8). (H) Neural manifold generated from place cells (left) and nonplace cells (right) from the same session as (A) to (F) by using semisupervised UMAP trained on blocks of five trials. (I) Trial block decoding error of all cells (red), place cells (PC), and its size-matched control from all cells (SM-PC, purple), as well as nonplace cells (NPC) and their size-matched control (SM_NPC, blue). Decoding error was measured in units of trial block in which successive five trials were binned to one trial block. (N.S., not significant; unpaired t test; n = 26 sessions from six animals.) (J) Decoding error when downsampling the cells in the example session to subsamples of varying cell numbers (from 50 to 450 cells). Error bar indicates the standard error of the mean (SE) across 1000 subsamples. Decoding error was measured in units of trial block in which successive five trials were binned to one trial block). The red line shows the fitted exponential curve.
Fig. 3.
Fig. 3.. Maze SPW-Rs replay of trial block identity.
(A) Spiking activity of an example trial in the figure-eight maze. (Top) Linearized position of the mouse. (Bottom) Raster plot of neuronal spiking activity sorted by seqNMF. Purple stars, SPW-Rs. (B) Zoomed-in display of a replay event. (Top) Local field potential (LFP) filtered in the ripple band. (Bottom) Raster plot of neurons belonging to a sequence factor with significant reactivation strength (materials and methods). Neurons were sorted in the same order as in (A). (C) All waking SPW-R events (red) in this example session were embedded with the neural manifold data during navigation (light gray), and the noise cloud consisted of negative samples (dark gray). Two clusters of SPW-R replay events were distinguished: off- and on-manifold events (materials and methods) (figs. S12 and S13). (D) SPW-R replays were classified as significant if they were (1) close to the maze manifold and (2) their trajectory length along the manifold was short in comparison to shuffled data. (E) Percentage of significant replays in the maze (number of significant maze replay events/total number of maze SPW-R events) compared with shuffled data. (***P < 10−4, unpaired t test; n = 26 sessions from six animals). (F) UMAP embedding and the decoded position for the same event as in (B). (Left) The neural manifold during maze running (“position manifold,” colored by position). (Right) The position content of each replay time bin was decoded to a position bin along the maze trajectory according to the position label of its nearest neighbor on the manifold. The black triangle represents the physical location of the mouse when the replay event took place, and the pink-purple dots represent the neural embedding of seven successive time bins of a SPW-R replay event (each time bin was 20 ms). (G) (Left) The same event was embedded with the “trial manifold” and was colored by trial block number. (Right) Trial content of each replay time bin was decoded as the trial block label of its nearest neighbor on the manifold (each trial block corresponds to five trials). (H) Distribution of differences between the actual trial block of SPW-Rs events and their decoded trial block identity across all sessions.
Fig. 4.
Fig. 4.. Replay of trial block identity and maze segments during sleep can be predicted from waking SPW-Rs in the maze.
(A) Spiking activity in the figure-eight maze. (Top) Linearized position of the mouse. (Bottom) Raster plot of neuronal spikes, sorted by seqNMF. Purple stars represent SPW-Rs. (B) Zoomed-in display of an awake replay event in the maze. The raster plot contains neurons belonging to the sequence factor with significant reactivation strength (materials and methods). Neurons were sorted in the same order as in (A). (C) Decoded position and trial block identity of successive 20-ms bins (one to six bins) of the same SPW-R replay event in (B). The black triangle represents the physical location of the mouse when the replay event took place. (D) Raster plot of a SPW-R replay event during sleep in the home cage. (E) Decoded position and trial block identity of successive 20-ms bins of the same SPW-R replay event in panel (D). (A), (B), and (D) were colored according to the linearized position of the mouse; (C) and (E) were colored according to trial block number. (F) Percentage of significant replays during pre- and postexperience sleep (presleep and postsleep, respectively) in the home cage compared with shuffled data. (Presleep versus postsleep, ***P < 10−5; postsleep versus shuffled data, ***P < 10−8; presleep versus shuffled data, ***P < 10−8; unpaired t test; n = 26 sessions from six animals). (G) Distribution of the trial blocks decoded from the population spike content of SPW-Rs in an example session. (Top) Maze and postsleep replay trial block distribution pattern. (Bottom) Maze and presleep replay trial block distribution pattern. (H) Correlation between trial block distributions across trial blocks during maze and postsleep SPW-Rs replay (decoded from the original high-dimensional space; Pearson correlation coefficient, R = 0.86; P < 3.1 × 10−34; n = 16 sessions from five animals) (results from all four different decoding methods, see fig. S18). (I) The predictive relationship between trial block distribution patterns of postsleep SPW-Rs and other candidate variables, including the trial block distribution patterns of theta cycle, theta power, presleep, and trial-shuffled data (***P < 10−23 for awake replay; ***P < 10−3 for theta cycle number; the relative predictive power of a given metric was considered nonsignificant when it overlapped with zero; n = 16 sessions from five animals) (for results from all four different decoding methods, see fig. S18). (J) Proportion of maze segment replays (left arm and right arm) during awake and postexperience sleep SPW-Rs in an example session. (K) Proportion of sessions with same rank order between maze segments during awake and post-maze sleep SPW-R replays, compared with shuffle data. (*P < 0.05, chi-square test; n = 13 sessions from five animals).
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