{"paper_id":"09da3545-690a-49c3-ac6e-0e66cef14d69","body_text":"1 \n \n \n \n \n \nEpisodic experience drives ripple reorganization and synaptic changes  \nin the hippocampus \n \n \nJunko Ishikawa1, Takuto Tomokage1, and Dai Mitsushima1,2,* \n \n \n1 Department of Physiology, Yamaguchi University Graduate School of Medicine, \nYamaguchi, 755-8505, Japan. \n2 The Research Institute for Time Studies, Yamaguchi University, \nYamaguchi, 753-8511, Japan. \n \n \nCorresponding authors:  \nProf. Dai Mitsushima, e-mail: mitsu@yamaguchi-u.ac.jp \n \n \nCONFLICT OF INTEREST \nThe authors declare no competing interests. \n \nEthical statement: \nOur manuscript confirming the study is reported in accordance with ARRIVE guidelines \n(https://arriveguidelines.org). \n \nData availability statement: \nAny additional information required to reanalyze the data reported in this paper is available \nfrom the lead contact upon request. Although the data are not publicly available due to the \ninclusion of data for ongoing research, all data will be uploaded to the web server of the \nYamaguchi University Graduate School of Medicine upon acceptance of the research \npaper. In addition, all data will be deposited in an open repository. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n2 \n \nABSTRACT \nThe hippocampus plays a key role in encoding episodic memory by transforming recent \nexperience into persistent neuronal and synaptic modifications. However, the physiological \nprocesses that link real-world experience to network-level and cellular-level changes \nremain incompletely understood. Here, we investigated how distinct types of episodic \nexperience reorganize ensemble firing dynamics and synaptic input in the hippocampal \nCA1 region of freely moving rats. \n     We identified spontaneous \"super bursts\"—brief episodes of high-frequency firing \nacross neuronal populations—that emerged specifically during emotionally salient \nexperiences. These bursts were followed by increased ripple firings, characterized as short, \nsynchronous events associated with memory consolidation. Using information entropy \nanalysis, we found that ripple firing patterns became more diverse after experience, \nindicating enhanced variability in neural representation. Ex vivo whole-cell patch-clamp \nrecordings revealed that miniature excitatory and inhibitory synaptic currents in CA1 \npyramidal neurons also underwent experience-specific reorganization. \n     Together, these findings propose a cascading mechanism in which episodic \nexperience triggers coordinated ensemble activity, leading to ripple reorganization and \nsynaptic remodeling, thereby contributing to the initial encoding of memory. Our study \nintegrates systems-level neuronal dynamics with cellular-level synaptic plasticity, offering \nnew physiological insights into how brain circuits adapt to experience. \n \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n3 \n \nKey points: \nl The hippocampal CA1 region encodes episodic memory through experience-\ndependent changes in neuronal activity and synaptic input. \nl Distinct episodic experiences evoke high-frequency \"super bursts\" in CA1, followed by \ndiversification of ripple firing patterns. \nl Information entropy of ripple firings increased after experience, suggesting enhanced \nrepresentational diversity. \nl Ex vivo recordings revealed experience-specific reorganization of excitatory and \ninhibitory synaptic currents in CA1 pyramidal neurons. \nl These findings propose a cascade mechanism linking episodic experience to ripple \nreorganization and synaptic remodeling during memory encoding. \n \nKeywords: hippocampus, episodic memory, ripple firing, synaptic plasticity, super burst, \ninformation entropy, memory encoding \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n4 \n \nINTRODUCTION \nEvery day we encounter events that encompass the elements of \"when, where, and \nwhat.\" The hippocampus serves as the primary site for processing episodic memory (1), \nwhere spatiotemporal information (2,3) or specific episodes are represented (4). \nSpecifically, CA1 neurons in the dorsal hippocampus encode the location and context in \nwhich an animal identifies novel environments (5), and transient inactivation of these \nneurons impairs performance on memory tests (6). Learning to avoid a stressful location \nnecessitates synaptic plasticity in dorsal CA1 neurons (7,8). Additionally, CA1 neurons are \nessential for object recognition (9), and several junction place cells have been shown to \nrepresent information about the location of another animal when housed together (10). \nHowever, the mechanisms by which memory processes differentiate between distinct \nepisodes and retain prior experience remain completely unknown.  \nCA1 neurons often synchronize with neighboring neurons during their functions, and \nthe number of synchronizing neurons increases with task difficulty (11). Since the \nspatiotemporal firing patterns of multiple neurons can convey a neural code (12,13), large-\nscale electrophysiological activity of single neurons has been extensively studied (14). \nHowever, to analyze the firing activity behind ripples–thought to contain memory-related \ninformation–it is crucial to capture the coordinated firing activity of neighboring neurons with \nhigh spatiotemporal resolution. Isolating the synchronized firing activity of neighboring \nneurons that from a single ripple remains challenging. Moreover, the amplitude and shape \nof spikes can vary over time within the same neuron, particularly in burst-firing neurons \n(15). Additionally, dendritic plateau potentials and the complex burst firing they generate \nhave been observed in learning animals (16). Therefore, in this study, we did not perform \nspike sorting; instead, we recorded the multi-unit firing activity of neighboring CA1 neurons \nin freely moving rats before and after the acquisition of episodic memory, examining the \nshape changes induced by the experienced episodes. By exposing animals to four \nepisodes with different emotional content, our multiple-unit recording approach enabled \nepisode-specific discrimination and captured temporal changes before and after the \nexperiences. \nEmotions such as happiness, fear, and sadness significantly influence memory \nstrength (17-20), with these mechanisms relying on neurotransmission in the hippocampus. \nFor instance, emotional arousal enhances learning thorough noradrenergic activation of \ndorsal CA1 neurons, which drives GluA1-containing AMPA receptors into synapses (21). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n5 \n \nAdditionally, tyrosine hydroxylase-expressing neurons in the locus coeruleus may facilitate \nmemory by co-releasing dopamine in the hippocampus (22). However, conclusive evidence \nregarding whether emotional arousal influences the firing of CA1 neurons and their \ntemporal dynamics in freely moving animals is still lacking. Furthermore, while many \nstudies have established a causal relationship between synaptic plasticity and learning \n(7,8,23), conclusive evidence for spontaneous high-frequency firing during learning has yet \nto be observed. \nIn conjunction with excitatory synaptic plasticity, the maturation of inhibitory neurons \nhas been shown to be essential for hippocampal learning (24,25). We recently discovered \ncell/tract-specific plasticity that positively correlates with acquired performance (26,27). \nAdditionally, synaptic plasticity during the initial phase rapidly generates a diversity of \nexcitatory and inhibitory postsynaptic currents within 10 minutes of experience, which are \nknown to persist for over 60 minutes (28). Given that synaptic plasticity is a necessary \nprocess for generating rapid post-episodic diversity, we hypothesized that episode-specific \ndiversity may arise to accommodate different types of episodes (29). To test this \nhypothesis, we examined the integrated plasticity of excitatory and inhibitory synapses in \nCA1 pyramidal neurons and found evidence for episode-type-specific diversity.  \nIn this study, we identified three cellular and synaptic events that represent recent \nexperiences in hippocampal CA1 neurons by monitoring multiple neural activities during \nlearning and performing slice-patch clamp analyses. \n \nRESULTS \nRats can learn experimental episodic memory \n     To mimic the events in humans that lead to episodic memory, adult male rats were \nexposed for 10 min to one of four different episodes (Fig. 1A): restraint stress, social \ninteraction with a female or a male, and observation of a novel object (Fig. 1B). To assess \nacquired memory, the rats were re-exposed to the same episode and their behavior was \nassessed (30,31). Rats that experienced restraint stress showed fewer vocalizations during \nthe second exposure (t6 = 3.476, P = 0.0129). Similarly, rats exposed to a female, male, or \nnovel object consistently reduced latency to vaginal inspection (t8 = 3.492, P = 0.0082) or \nattack (t7 = 4.192, P = 0.0041) and object observation time (t9 = 2.901, P = 0.0176) during \nthe second encounter, suggesting memory acquisition (32). \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n6 \n \nMultiple-unit activities and classification of firing patterns \n     To monitor the encoding process of the experience, we recorded multiple-unit firing \nactivity from CA1 before (15 min), during (10 min), and after (30 min) each episode using \nan electrode that could record neural activity from many neighboring neurons (Figs. 1C and \n1D). The number of recordings was no-experience control (N = 7), restraint stress (N = 9), \ncontact with a female (N = 11), contact with a male (N = 11), and a novel object (N = 9). \nAlthough the neural activity could not separate multiple neurons into single units (Fig. S1), \nspike half-width analysis classified the 284 estimated well-separated single neurons, \nyielding 220 estimated pyramidal neurons (> 0.7 msec), 38 estimated interneurons (< 0.4 \nmsec), and 26 unclassified neurons (0.4 to 0.7 msec), suggesting the location of the \nrecording in the pyramidal cell layer (Fig. S1D). We found that neighboring CA1 neurons \nexhibited several unique firing patterns particularly after the onset of an episode. Based on \nthe following criteria, we extracted the events of super bursts and ripple firings (Fig. 1E). \n     Prior to the experience, CA1 neurons showed mostly sporadic firings (Fig. 1F, Movie \nS1), with some ripple firings when the rats were in their home cage. Using the 100 - 300 \nseconds of low-noise data prior to the experience, we calculated the basal firing rate for \neach individual (n = 12390). We then extracted spontaneous high-frequency firings (Figs. \n1E, 1G, 2A, and 2B), defining a super burst as one with a firing rate higher than three \nstandard deviations (SDs) of the mean firing rate before the episode (n = 327). CA1 \nneurons often exhibited ripple firings separated by no-firing silent periods (Fig. 1H, Movie \nS2). We detected sharp-wave ripples (150 – 300 Hz), and in this study we analyzed the \nfiring patterns behind them as ripple firings (300 – 10 kHz), showing short-duration (56.3 ± \n16.5 msec ± SD, n = 5333), high-frequency clustered firings with a signal-to-noise ratio of at \nleast 6:1 (Fig. 1I). Super bursts and ripple firings were clearly distinguished by their \nduration (Fig. 1E and S2).  \n \nSuper bursts represent episode-type specific features \n     We started recording in the home cage before the experience, and examined \nchanges in the occurrence and duration of super bursts with each episodic experience \n(Figs. 2C and 2D). To statistically evaluate this change, we used a two-way analysis of \nvariance (ANOVA) with experience as the between-group factor and time as the within-\ngroup factor. The results showed a significant effect of episode (events 3 min−1, F4, 546 = \n2.482, P = 0.058; duration, F4, 546 = 3.145, P = 0.024), time (events 3 min−1, F13, 546 = 4.937, \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n7 \n \nP < 0.0001; duration, F13, 546 = 7.288, P < 0.0001), and their interaction (events 3 min−1, F52, \n546 = 1.567, P = 0.009; duration, F52, 546 = 3.095, P < 0.0001). To further analyze the effect \nof time in individual episodes, we performed post hoc ANOVAs. Both, the occurrence and \nduration were significantly increased by restraint (events 3 min−1, F13, 104 = 5.174, P < \n0.0001; duration, F13, 104 = 7.697, P < 0.0001), contact with a female (events 3 min−1, F13, 130 \n= 2.589, P = 0.0032; duration, F13, 130 = 2.852, P = 0.0012), and contact with a male (events \n3 min−1, F13, 130 = 1.940, P = 0.031; duration, F13, 130 = 1.880, P = 0.038). In contrast, they \nremained unchanged after contact with a novel object (events 3 min−1, F13, 104 = 0.788, P = \n0.671; duration, F13, 104 = 0.783, P = 0.676), suggesting an episode-dependent generation \nof super bursts in neighboring CA1 neurons. No significant change was observed in the \ncontrol group that did not experience episodes (events 3 min−1, F13, 78 = 0.844, P = 0.614; \nduration, F13, 78 = 1.093, P = 0.377). \n    Finally, occurrence and duration of super burst at each time point were compared two-\ndimensionally using a two-way multivariate analysis of variance (MANOVA; Fig. 2C vs 2D) \nwith experience as the between-group factor and time as the within-group factor. Two-way \nMANOVA (F8, 1304 = 9.761, P < 0.0001) and post-hoc MANOVAs further revealed episode-\nspecific differences in 8 “experience vs. experience” pairs in each of the total 10 \ncomparison pairs (Table 1, see also Figs. 2C,D). \n     To further investigate whether the features of individual super bursts differ between \nepisodes, the duration and relative firing rate of individual super bursts were plotted \n(triangles in Fig. 2E) and the integrated two parameters analyzed by one-way MANOVA. \nThe X-axis is the duration (sec) and the Y-axis is the firing frequency with pre-experience \nset to 1. MANOVA (F8, 558 = 11.535, P < 0.0001) and post-hoc MANOVAs revealed \nepisode-specific differences in 6 “experience vs. experience” pairs in each of the total 10 \ncomparison pairs (Table 1, see also Fig. 2E).  \n      \nEpisodic experience consistently increases no-firing silent periods \n     Since the silent period without firing highlighted the ripple firings (Fig. 3A), the silent \nperiod may be important to support memory processing. For the rate of silent period (Fig. \n3B), a two-way ANOVA showed significance within time (F2, 84 = 18.167, P < 0.0001), but \nneither the main effect of experience (F4, 84 = 1.006, P = 0.415) nor the interaction was \nsignificant (F8, 84 = 1.508, P = 0.167). In the within-group temporal analysis, the rate of \nsilent periods increased with restraint stress (F2, 16 = 3.740, P = 0.047) and contact with \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n8 \n \nfemale (F2, 20 = 7.018, P = 0.005), male (F2, 20 = 8.557, P = 0.002), and novel object (F2, 16 = \n6.063, P = 0.011). Episode consistently increased silent periods regardless of experience \ntype, whereas no significant change was observed in the no-experience control (F2, 12 = \n0.534, P = 0.599) (Table S1; detailed ANOVA results for silent periods across episodes). \n \nChanges in ripple firings depends on the nature of the episodic experience \n     Hippocampal ripple oscillations (140 – 250 Hz) are known as oscillatory patterns \nobserved on an electroencephalogram during sleep or immobility that are important for \nmemory and action planning. High frequency clustered firing appears in conjunction with \nripple oscillations (33). Here, we recorded 300 – 10 kHz band to detect individual firings \nbehind the ripples. Since the clustered firings by multiple neurons were almost all \nsynchronized with a ripple, we defined the clustered firings as a “ripple firings”. To examine \nthe episode-induced ripple firings, we first counted the occurrence. \n     For the occurrence of ripple firings (Fig. 3C), a two-way ANOVA showed significance \nwithin time (F2, 84 = 10.649, P < 0.0001) and interaction (F8, 84 = 3.422, P = 0.002), but the \nmain effect of experience was not significant (F4, 84 = 0.882, P = 0.483). We found a within-\ngroup temporal increase in the occurrence of ripple firings with restraint stress (F2, 16 = \n18.672, P < 0.0001) and contact with female (F2, 20 = 6.908, P = 0.005) or male (F2, 20 = \n4.992, P = 0.017), while the occurrence decreased with novel object (F2, 16 = 3.881, P = \n0.042). Episodic experience altered the occurrence of ripple firings, depending on the \nnature of experience. In contrast, no significant change was observed in the no-experience \ncontrol (F2, 12 = 1.991, P = 0.179). Detailed ANOVA results for the occurrence of ripple \nfirings across episodes were shown in Table S2. \n \nSuper bursts correlated more with ripple firings than with silent periods.  \nDepolarization of CA1 neurons is known to be the initial trigger of synaptic plasticity \n(34,35). Although an intrinsic spontaneous trigger has not been found in free-\nmoving/learning animals, we were able to record super bursts especially during the \nemotional episode. Since not only excitatory/inhibitory synaptic transmission is necessary \nfor ripple generation (33, 34-38), we predicted super bursts as an event that induces \nsynaptic plasticity. To address this issue, we analyzed whether super bursts correlated with \nripple firings or silent period in individual animals. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n9 \n \n    Although the increase in silent periods was not correlated with the number or total \nduration of super bursts (Figs. S3A and B), the increase in ripple firings was positively \ncorrelated with the number or total duration of super bursts (Figs. S3C and D).  \n \nThe diversity of ripple firings represents recent experience \n     The ripple appears in conjunction with a sharp wave, and this oscillation is known as \nsharp-wave/ripple complexes (SPW-Rs). Suppression of SPW-Rs impairs learning and \nmemory (39,40), and learning prolongs the duration of SPW-R (41). The firing sequence \nduring SPW-Rs replays the sequence of the location during spatial learning (42), and \nspikes during SPW-R duration are increased by learning (41). These observations suggest \nthat ripples contain learning information, and the firings that co-occur with ripples serve as \nan element. Because ripple firings are composed of many spikes from multiple neurons, we \nhypothesized that ripple firings might provide episode-type specific features representing \nthe recent experience. \n     Here, we extracted four features from individual ripple firings (Fig. 3D) and analyzed \nthem using two-way repeated measures of ANOVA, where a between-group factor was \nexperience and a within-group factor was time (Fig. 3E). Post-hoc ANOVAs further showed \nthe differences in temporal dynamics (Table S3; detailed ANOVA results in individual \nfeatures across episodes). Values for all features mostly increased with episodic \nexperience, but the number of peaks decreased after contact with a novel object. These \nchanges were maintained for at least 40 min, while none of the four features changed in the \nno-experience control.  \n     Based on Shannon's information theory, we also calculated the appearance \nprobability of the four features (Fig. 3F). First, we determined the distribution of the \nappearance probability before the experience, followed by the analysis of the appearance \nprobability of all ripple firings individually. The diversity of ripple firings expanded \nsignificantly with the experience, increasing the information entropy per single ripple-firings, \nand these changes were also maintained for at least 40 min (Fig. 3F). Two-way repeated \nmeasures of ANOVA showed overall significance on the 4 self-entropies of ripple firings, \nand post-hoc ANOVAs further showed the differences in temporal dynamics (Table S4; \ndetailed ANOVA results for individual self-entropies across episodes. \n     Finally, we analyzed the episode-type specificity using MANOVA: the four features for \neach ripple-firings were integrated and the waveform diversification was evaluated \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n10 \n \ncomprehensively. Due to the difficulty of plotting in four dimensions, individual ripple \nfeatures were plotted in three dimensions (Fig. 3G), but the diversity of ripple firing was \nactually compared in four-dimensional distribution. Two-way MANOVA and post-hoc \nMANOVAs further revealed episode-specific differences in all “experience vs. experience” \npairs in any of the total 10 comparison pairs. The temporal dynamics of ripple diversity \nstrongly suggested that both episode-dependent and episode type-specific changes in the \nripple firings in hippocampal CA1 neurons, while no significant temporal change was \nobserved in the no-experience control (Table 2, see also Fig. 3G).  \n     Similarly, the integrated four self-entropy data for each ripple firings were analyzed \nusing MANOVA and post-hoc MANOVAs, suggesting episode-specific differences in all \n“experience vs. experience” pairs in any of the total 10 comparison pairs. These results \nindicate both episode-dependent and episode-type specific changes in the information \nentropy per single ripple-firings, while the overall temporal change in the no-experience \ncontrol was not significant (Table 2, multi-dimensional self-entropy plot was not shown). \n     Furthermore, the post-experience features of ripple firings were significantly \ncorrelated with the number and total duration of super bursts in the same animal, but not \nwith the pre-experience features of ripple firings. The results show that not only the number \nof ripple firings (Figs. S3C and D), but also the changes in their features were under the \ninfluence of the preceding events, the super bursts (Fig. 3H). Super bursts may contribute \nto the generation of new ripple firings after the episodic experience. \n \nEpisode-type specific diversification of postsynaptic currents at CA1 synapses \n     To further investigate experience-induced synaptic plasticity, we prepared brain slices \nfor patch clamp analysis 40 min after the episodic experiences. By sequentially recording of \nmEPSCs (at −60 mV) and mIPSCs (at 0 mV) from the same neurons (29), we measured \nfour parameters from individual CA1 neurons (Fig. 4A) and plotted them in virtual space \n(Fig. 4B): amplitudes and frequencies for both mEPSCs and mIPSCs. Postsynaptic \ncurrents are thought to correspond to the response elicited by a single vesicle of glutamate \nor GABA, while the number of synapses affects the frequency of events (43). \n     The results of one-way ANOVA on four individual parameters are shown in Figure 4C \n(Table S7; detailed ANOVA results across episodes). Although no experience controls \nshowed a low and narrow distribution range of the synaptic strength, experience diversified \nit (Figs. 4B and D). Restraint stress increased both mEPSC and mIPSC amplitudes. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n11 \n \nContact with a female rat increased both amplitudes and mEPSC frequency. In contrast, \ncontact with a male rat increased all four parameters, and contact with a novel object \nincreased only mIPSC amplitude. These results suggest that the strength or number of \nexcitatory and inhibitory synapses is altered depending on recent experience. \n     Finally, we analyzed the episode type specificity using MANOVA: the data from four \nfeatures (amplitude/frequency of mEPSC/mIPSC) were integrated and comprehensively \nevaluated. Four-dimensional virtual plots revealed episode-type specific synaptic plasticity \nin individual CA1 neurons (Fig. 4B, F16, 551 = 4.729; P < 0.001). Table 3 shows the \ndifferences between two specific episodes: post-hoc MANOVAs revealed episode-specific \ndifferences in 9 “experience vs. experience” pairs in each of the total 10 comparison pairs \n(see also Fig. 4B).  \n     Based on the Shannon entropy, we further quantified synaptic diversity by measuring \nthe population differences in mE(I)PSC amplitude and frequency compared to no-\nexperience controls (44). The diversity of synaptic input strength increased significantly with \nthe experience, increasing the information entropy per individual CA1 neuron (Fig. 4E and \nTable S8; detailed ANOVA results across episodes). \n     Similarly, the integrated self-entropy data showed episode-type specific diversity at \nexcitatory and inhibitory CA1 synapses (Fig. 4D, F16, 551 = 4.361; P < 0.001). Table 3 shows \nthe differences in the self-entropy between two specific episodes: post-hoc MANOVAs \nrevealed episode-specific differences in 9 “experience vs. experience” pairs in each of the \ntotal 10 comparison pairs (see also Fig. 4D).  \n \nDISCUSSION  \nLong-term potentiation, established over five decades ago as a synaptic model of learning \nand memory (34), has recently demonstrated causal validity through targeted optogenetic \nmanipulation studies (8,23). However, a critical paradox persists between experimental \ninduction paradigms and natural learning conditions: while exogenous high-frequency \nstimulation protocols (e.g. 100 Hz, 1 sec) with coordinated presynaptic activation and \npostsynaptic depolarization reliably induce synaptic strengthening (45,46), the endogenous \nsources of such precisely patterned activity in behaving animals remain unidentified. Our \nstudy addressed this fundamental question by characterizing spontaneous \"super bursts\" - \nself-organized high-frequency firing patterns (above 3 standard deviations of basal firing) in \nCA1 neurons that arise specifically during episodic encoding.  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n12 \n \n     Acetylcholine (ACh) is one of the leading candidates for eliciting super bursts, known \nto induce plasticity at dorsal CA1 synapses (47,48). Indeed, both restraint stress and \nlearning increase ACh release in the dorsal CA1 in vivo (29,49), and the positive correlation \nbetween ACh release and contextual fear performance is blocked by scopolamine (50). \nFurthermore, prior mechanistic studies have established that microinjections of ACh \nantagonists inhibit learning-induced synaptic plasticity, suggesting that ACh is essential for \nthe induction of super bursts and subsequent synaptic diversity (29). \n     The features of post-experience ripple firings, but not pre-experience ripple firings, \nshowed a significant correlation with the total number and duration of super bursts in the \nsame animals (Fig. 3H). Additionally, experiencing an emotional episode significantly \nincreased the occurrence of super bursts (Fig. 2), and excitatory synapses were \nstrengthened in these groups (Fig. 4C). Based on these findings, we hypothesize a causal \nrelationship between the diversification of post-super burst ripple firing in hippocampal CA1 \nneurons and subsequent memory acquisition (Fig. 5). To establish this causal relationship, \nit is essential to evaluate ripple firing diversity, synaptic plasticity, and learning ability while \ncontrolling for experience-induced super bursts. In a preliminary study, a causal relationship \nwas suggested, as administration of scopolamine prior to restraint stress prevented the \noccurrence of super bursts, suppressed the diversity of ripple firings, and reduced freezing \nperformance in the same subjects (51). \n     In contrast to restraint stress or social experiences, only inhibitory synaptic currents \nand silent periods increased in response to novel objects. More importantly, the increase in \nGABAA currents was not specific to novel objects. For all four types of episodic \nexperiences, both inhibitory synaptic currents (Fig. 4C) and silent periods without firing (Fig. \n3B) exhibited a consistent increase across the experience groups. Since novel elements \nare present in all four types of experiences, the consistent increase in inhibitory synaptic \ncurrents and the formation of silent periods may be essential for processing information \nrelated to novel objects and animals. While microinjection of higher doses of GABAA \nreceptor agonists into CA1 impairs memory for novel objects (52,53), it is also known that \nsuppression of neuronal activity can facilitate information processing (54, 55), and a \ndecrease in background firing after an experience may emphasize ripple firings to convey \ninformation. Although the causal relationship between strengthening inhibitory synapses \nand increasing silent periods remains to be established, inhibitory synapses are recognized \nas necessary for learning and memory, as well as for ripple wave formation (24, 33, 56). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n13 \n \n     Sharp wave-ripple complexes (SPW-Rs) - high-frequency oscillations (140–250 Hz) \ncoupled with sharp waves (33) - play fundamental roles in memory consolidation and \nbehavioral planning (57,58). Experimental suppression of SPW-Rs consistently disrupts \nboth learning acquisition and memory retrieval processes (39,40), whereas successful \nlearning induces progressive prolongation of SPW-R durations (41). This bidirectional \nrelationship strongly supports their mechanistic involvement in memory formation. Notably, \nSPW-Rs exhibit precise temporal replay of spatial navigation sequences (42) and show \nlearning-dependent amplification of spike participation within the ripple cycle (41), further \nsubstantiating their role as hippocampal memory engrams. Given the frequent occurrence \nof plateau potentials in CA1 neurons during learning states (16), our study specifically \ninvestigates the 300–10 kHz frequency band to characterize unsorted multi-unit activity \npatterns underlying ripple-generation mechanisms. \n     Our investigation of high-frequency EEG dynamics surrounding episodic experiences \nrevealed two distinct patterns of ripple activity modulation: experience-dependent global \nchange and episode-specific reorganization across four quantitative features of ripple \nwaveforms (amplitude, duration, arc length, and spike peaks; Figs. 3E-G). Comprehensive \nanalysis of 82,601 ripple firings waveform pairs using Euclidean distance metrics revealed \nsignificant morphological specificity, with individual events retaining unique spatiotemporal \nsignatures (Fig. S4). Crucially, our similarity network analysis identified single-parameter \ndiscriminability - inter-ripple distance thresholds successfully encoded recent experience \ncategories (classification accuracy: 92.3 ± 4.1%, P < 0.001; Table S9; detailed ANOVA \nresults across episodes). This parametric sensitivity to experience history provides \nmechanistic evidence for content-specific information encoding within hippocampal ripple \ndiversity. \n     The roles of excitatory and inhibitory synaptic transmission in SPW-R generation (59-\n61) and spike regulation during ripples are well established (33). In hippocampal slice \nmodels, SPW-Rs originate from CA3, propagate to CA1, and are critically dependent on \nAMPA receptor activation - their pharmacological blockade abolishes SPW-R generation \n(37,38). Inhibitory mechanisms involve two key processes: (i) CA3 interneurons coordinate \npyramidal cell phase-locking through GABAA receptor-mediated synchronous IPSCs (62), \nand (ii) pre-SWR inhibitory activity governs pyramidal cell spike timing and enables \nsequence pattern diversification (59). At the synaptic level, miniature events \n(mEPSCs/mIPSCs) reflect single vesicle glutamate/GABA release, with event frequency \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n14 \n \nmodulated by synaptic number (43). Our study linked these findings to recent experience \nby demonstrating integrative plasticity at both synapse types, with two distinct patterns of \nsynaptic modulation: experience-dependent change and episode-specific reorganization \nacross four quantitative features of synaptic currents (mEPSC amplitude/frequency and \nmIPSC amplitude/frequency; Table 3). \n \nOur model consists of three stages \n     Our experimental observations show that experience-dependent super bursts exhibit \ninitial episode-type specificity in temporal organization patterns (Table 1). This leads us to \npropose a cascading plasticity model in which these patterned bursts: Establish \nexperience-specific synaptic configurations through selective dendritic integration (Table 3), \nShape ripple diversity by creating distinct excitation-inhibition balances across microcircuits \n(Table 2). While the causal relationships between experience, synaptic plasticity, and ripple \ndynamics require further elucidation, three lines of evidence support this framework: 1) \nNecessity of synaptic plasticity: Single-unit in vivo recordings show that NMDA receptor-\ndependent plasticity provides the cellular basis for encoding novel experience, as \nevidenced by impaired place field formation following plasticity blockade (63). 2) Memory-\nspecific reorganization: Aversive stimuli induce rapid place cell remapping (≤ 5 minutes \npost-event), suggesting experience-driven reconfiguration of network firing motifs for \nmemory storage (64). 3) Experience-structured replay: Large-scale neural ensemble \nanalyses reveal that sequential replay events progress through distinct stages (exploration \n→ consolidation → retrieval) with experience-dependent structured evolution (65). \n     In this study, we identified three distinct hippocampal CA1 events associated with the \nrepresentation of recent experience. The functional hierarchy between CA3 and CA1 is \nmost evident in ripple oscillations, the disruption of which is known to impair memory \nconsolidation (39,40,66). Our results further suggest that sequential ripple firing patterns \nmay coordinate the processing cascade for recent experiences (65). Although the causal \nmechanism requires further investigation, we recently reported the following: 1) Plasticity in \ninhibitory GABAA synapses is essential for contextual learning, 2) hippocampal commissure \nstimulation synchronized with super burst initiation blocks learning and synaptic \ndiversification; and 3) optogenetic inhibition of cholinergic input to the hippocampus only \nduring contextual experience specifically impairs learning in ChAT-Cre mice (67, 68). Based \non these findings, we propose an experience-dependent information processing pathway \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n15 \n \ncharacterized by: i) ripple diversification following experience-specific super bursts, and ii) \nsubsequent integrative plasticity at excitatory / inhibitory synapses. As illustrated in Figure \n5, this model predicts that targeted modulation of these three events – particularly through \ntemporal coordination of their activation patterns – could establish novel paradigms for \nmemory enhancement. Future studies using precise optogenetic manipulation of these \ncircuit elements could both validate this hypothesis and reveal potential therapeutic \napplications for memory disorders. \n \nMATERIALS AND METHODS \nAnimals \nMale Sprague-Dawley rats (CLEA Japan, Tokyo, Japan) were housed individually at 24 ± \n1°C under a 12-hour light/dark cycle (lights on: 8:00 AM–8:00 PM) with ad libitum access to \nfood (MF, Oriental Yeast Co. Ltd., Tokyo). Animals aged 15–25 weeks were used for in vivo \nrecordings. Episodic stimuli were provided by 8–15-week-old male or female rats, housed \nseparately without electrodes. All procedures were approved by the Yamaguchi University \nAnimal Care and Use Committee and conformed to institutional and governmental \nregulations including the NIH Guide for the Care and Use of Laboratory Animals. \n \nSurgery \nRats were anesthetized with sodium pentobarbital (50 mg/kg, i.p.) and placed in a \nstereotaxic frame. Movable tungsten electrodes (50–80 kΩ; KS-216, Unique Medical Co., \nTokyo) were chronically implanted just above the dorsal CA1 region (AP: −3.0 to −3.6 mm; \nML: ±1.4 to ±2.6 mm; DV: 2.0–2.2 mm) and secured with dental cement. Animals with \nincorrect electrode placements were excluded. \n \nMultiple-unit Recording and Spike Detection \nWe started recording multiple-unit activity while the animals were in their familiar home \ncages and monitored their spontaneous behavior. Neural signals were recorded from the \nimplanted electrodes via a headstage amplifier and shielded cable (MEG-2100 or MEG-\n6116; Nihon Kohden, Tokyo, Japan), filtered at 150–10,000 Hz, and digitized at 25 kHz \nusing Spike2 software (Cambridge Electronics Design Ltd., Cambridge, UK).  \n     Super bursts were defined as transient events with firing rates exceeding 3SD of \nbaseline. Ripple firings were identified as short-duration (56.3 ± 16.5 msec ± SD, n = 5333) \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n16 \n \nhigh-frequency events (signal-to-noise ratio ≥ 6:1) following sharp-wave ripples (150–300 \nHz). To detect sharp-wave ripples, we recorded signal (150–10 kHz) and filtered at 150– \n300 Hz and 300–10 kHz. Using the 150–300 Hz signal, we calculated the root mean square \n(RMS) and the threshold for the event detection was set to +6 SD above the mean of the \nbaseline. Ripple firings were accompanied with the sharp-wave ripples. Grooming or teeth \ngrinding show the characteristic of symmetrical amplitude of upper and lower peaks, and \nwere excluded from the analysis. \n     Behavioral states during ripple firings were eye closed stationary 16.0 ± 7.0%, eye \nopen stationary 59.7 ± 11.0%, or eye open moving 24.3 ± 11.3% (1631 events in 9 rats). \nSilent periods were defined as inter-spike intervals exceeding +3 SDs of baseline. For \nvisual spike classification only (e.g., Fig. S1), template matching and spike width criteria \nwere used to distinguish putative pyramidal neurons (>0.7 ms) from interneurons (<0.4 ms). \n \nBehavioral Protocol and Recording Schedule \nFollowing ≥15 min of baseline recording in the home cage, rats were exposed to one of four \nepisodic conditions for 10 min: restraint stress, social interaction with a female or male, or \nexposure to a novel object (LEGO®/DUPLO® brick, 15×8×3 cm). Restraint involved tying \nthe limbs with soft cloth and fixing the animal to a wooden board (49). Recordings \ncontinued for ≥30 min post-exposure. The next day, animals were re-exposed to the same \nepisode for memory assessment (Fig. 1B). \n \nHistology \nAfter experiments, rats were deeply anesthetized (sodium pentobarbital 400 mg/kg, i.p.) \nand transcardially perfused with 0.1 M phosphate buffer containing 4% paraformaldehyde. \nBrains were post-fixed, cryoprotected in 10–30% sucrose, and sectioned coronally at 40 \nµm. Sections were stained with hematoxylin and eosin, and electrode placements were \nconfirmed using a rat brain atlas (69). \n \nSlice Patch-Clamp Recordings \nForty minutes after the start of episodic exposure, rats were deeply anesthetized, and \ncoronal brain slices (350 µm) including CA1 were prepared using ice-cold dissection buffer \nand vibratome (Leica VT-1200). Slices were transferred to physiological recording solution \n(22–25°C, aerated with 5% CO₂/95% O₂). Whole-cell voltage-clamp recordings were \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n17 \n \nobtained from CA1 pyramidal neurons using pipettes (4–7 MΩ) filled with cesium-based \ninternal solution. \n     Miniature excitatory (mEPSCs) and inhibitory (mIPSCs) synaptic currents were \nsequentially recorded at −60 mV and 0 mV, respectively, in the presence of 0.5 µM \ntetrodotoxin. AMPA and GABAA receptor blockers (CNQX, bicuculline, 10 µM each) \nconfirmed event identity (29,44,70,71). \n \nQuantification of Entropy and Statistical Analysis  \nThe appearance probability of ripple waveform or synaptic features was estimated using \none-dimensional kernel density analysis with Silverman’s rule for bandwidth (72-74). \nShannon entropy was computed in bits and log(1+x) transformed for parametric analysis \n(75). Statistical tests included one-way and two-way ANOVA, repeated-measures ANOVA, \nMANOVA (Wilks' lambda), and post hoc Fisher’s LSD. Spearman’s rank correlation was \nused to evaluate associations. Shapiro–Wilk and F tests assessed normality and variance \nhomogeneity. Significance was set at P < 0.05. \n \nACKNOWLEDGMENTS   The authors would like to thank Drs. Sora Takayama, Koushi \nSeo, and Ryo Sato for the comprehensive python analysis of ripple firings. This work was \nsupported by Grant-in-Aid for Scientific Research B, 16H05129 (DM) and 19H03402 (DM), \nGrant-in-Aid for Scientific Research C, 26350988 (JI), 17K01987 (JI) and 25460314 (DM), \nand Scientific Research in Innovative Areas, 26115518 from the Ministry of Education, \nCulture, Sports, Science and Technology of Japan (DM). This project was also supported \nby YU AI project of center for information and data science education. \n \nAUTHOR CONTRIBUTIONS   DM and JI designed and performed the experiments. TT, \nJI, and DM analyzed firing events. DM and JI wrote the manuscript. DM organized the \nstudy, and all authors reviewed the manuscript. \n \nSUPPLEMENTAL INFORMATION  \nSupplemental information can be found online. It is on the server of Yamaguchi University \nGraduate School of Medicine.  \n• Movie S1 \n• Movie S2 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n18 \n \nREFERENCES \n1. Scoville WB, Milner B, Loss of recent memory after bilateral hippocampal lesions. J \nNeurol Neurosurg Psychiatry 20, 11–21 (1957). \n2. O'Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence \nfrom unit activity in the freely-moving rat. Brain Res. 34, 171-175, (1971). \n3. Mitsushima D, Takase K, Funabashi T, Kimura F. Gonadal steroids maintain 24-h \nacetylcholine release in the hippocampus: organizational and activational effects in \nbehaving rats. J. Neurosci, 29, 3808–3815 (2009). \n4. Gelbard-Sagiv H, Mukamel R, Harel M, Malach R, Fried I, Internally generated \nreactivation of single neurons in human hippocampus during free recall. Science 322, \n96–101 (2008). \n5. Tanaka KZ, He H, Tomar A, Niisato K, Huang AJY, McHugh TJ, The hippocampal \nengram maps experience but not place. Science 361, 392–397 (2018). \n6. Drieskens DC, Neves LR, Pugliane KC, De Souza IBMB, Lima ADC, Salvadori \nMGDSS, Ribeiro AM, Silva RH, Barbosa FF, CA1 inactivation impairs episodic-like \nmemory in rats. Neurobiol. Learn. Mem. 145, 28–33 (2017). \n7. Mitsushima D, Ishihara K, Sano A, Kessels HW, Takahashi T, Contextual learning \nrequires synaptic AMPA receptor delivery in the hippocampus. Proc. Natl. Acad. Sci. \nU.S.A. 108, 12503–12508 (2011). \n8. Goto A, Bota A, Miya K, Wang J, Tsukamoto S, Jiang X, Hirai D, Murayama M, \nMatsuda T, McHugh TJ, Nagai T, Hayashi Y, Stepwise synaptic plasticity events drive \nthe early phase of memory consolidation. Science 374, 857-863 (2021).  \n9. Sun Y, Jin S, Lin X, Chen L, Qiao X, Jiang L, Zhou P, Johnston KG, Golshani P, Nie \nQ, Holmes TC, Nitz DA, Xu X, CA1-projecting subiculum neurons facilitate object-\nplace learning. Nat Neurosci. 22, 1857-1870, (2019). \n10. Danjo T, Toyoizumi T, Fujisawa S, Spatial representations of self and other in the \nhippocampus. Science. 359, 213-218, (2018). \n11. Sakurai Y, Takahashi S, Conditioned enhancement of firing rates and synchrony of \nhippocampal neurons and firing rates of motor cortical neurons in rats. Eur. J. \nNeurosci. 37, 623–639 (2013). \n12. Grinvald A, Arieli A, Tsodyks M, Kenet T, Neuronal assemblies: single cortical neurons \nare obedient members of a huge orchestra. Biopolymers 68, 422–436 (2003). \n13. Kobayashi K, Matsuo N, Persistent representation of the environment in the \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n19 \n \nhippocampus. Cell Rep. 42, 111989 (2023). \n14. Miyawaki H, Mizuseki K, De novo inter-regional coactivations of preconfigured local \nensembles support memory. Nat. Commun. 13, 1272 (2022).  \n15. Einevoll GT, Franke F, Hagen E, Pouzat C, Harris KD, Towards reliable spike-train \nrecordings from thousands of neurons with multielectrodes. Cur Opin Neurobiol 22, \n11-17 (2012). \n16. Bittner KC, Milstein AD, Grienberger C, Romani S, Magee JC, Behavioral time scale \nsynaptic plasticity underlies CA1 place fields. Science 357, 1033-1036 (2017). \n17. Christianson SA, The handbook of emotion and memory: research and theory. by \nLawrence Erlbaum Associates, Inc. New Jersey (1992). \n18. McGaugh JL, Memory – a century of consolidation. Science 287, 248–251 (2000).  \n19. Richter-Levin G, Akirav I, Emotional tagging of memory formation-in the search for \nneural mechanisms. Brain Res. Rev. 43, 247–256 (2003). \n20. LeDoux JE, Emotion circuits in the brain. Annu. Rev. Neurosci 23, 155–184. (2000). \n21. Hu H, Real E, Takamiya K, Kang MG, Ledoux J, Huganir RL, Malinow R, Emotion \nenhances learning via norepinephrine regulation of AMPA-receptor trafficking. Cell \n131, 160–173 (2007). \n22. Takeuchi T, Duszkiewicz AJ, Sonneborn A, Spooner PA, Yamasaki M, Watanabe M, \nSmith CC, Fernández G, Deisseroth K, Greene RW, Morris RG, Locus coeruleus and \ndopaminergic consolidation of everyday memory. Nature 537, 357–362 (2016).  \n23. Ripoli C, Dagliyan O, Renna P, Pastore F, Paciello F, Sollazzo R, Rinaudo M, \nBattistoni M, Martini S, Tramutola A, Sattin A, Barone E, Saneyoshi T, Fellin T, \nHayashi Y, Grassi C, Engineering memory with an extrinsically disordered kinase. Sci. \nAdv. 9, eadh1110 (2023). \n24. Lovett-Barron M, Kaifosh P, Kheirbek MA, Danielson N, Zaremba JD, Reardon TR, \nTuri GF, Hen R, Zemelman BV, Losonczy A, Dendritic inhibition in the hippocampus \nsupports fear learning. Science 343, 857–863 (2014). \n25. Ramsaran AI, Wang Y, Golbabaei A, Aleshin S, de Snoo ML, Yeung BA, Rashid AJ, \nAwasthi A, Lau J, Tran LM, Ko SY, Abegg A, Duan LC, McKenzie C, Gallucci J, \nAhmed M, Kaushik R, Dityatev A, Josselyn SA, Frankland PW, A shift in the \nmechanisms controlling hippocampal engram formation during brain maturation. \nScience 380, 543-551 (2023). \n26. Yang Y, Sakimoto Y, Mitsushima D, Postnatal development of synaptic plasticity at \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n20 \n \nhippocampal CA1 synapses: correlation of learning performance with pathway-specific \nplasticity. Brain Sciences 14(4): 382. https://doi.org/10.3390/brainsci14040382 (2024). \n27. Kida H, Toyoshima S, Kawakami R, Sakimoto Y, Mitsushima D. Properties of layer V \npyramidal neurons in the primary motor cortex that represents acquired motor \nskills. Neuroscience, 559:54-63, 2024. \n28. Sakimoto Y, Kida H, Mitsushima D, Temporal dynamics of learning-promoted synaptic \ndiversity in CA1 pyramidal neurons. FASEB J. 33, 14382–14393 (2019). \n29. Mitsushima D, Sano A, Takahashi T, A cholinergic trigger drives learning-induced \nplasticity at hippocampal synapses. Nat. Commun. 4, 2760 (2013). \n30. Whishaw IQ, Kolb B, The behavior of the laboratory rat: a handbook with tests. Oxford \nUniversity Press, NY (2005). \n31. Mitsushima D, Yamada K, Takase K, Funabashi T, Kimura F, Sex differences in the \nbasolateral amygdala: the extracellular levels of serotonin and dopamine, and their \nresponses to restraint stress in rats. Eur. J. Neurosci. 24, 3245–3254 (2006). \n32. Ishikawa J, Tomokage T, Mitsushima D, A possible coding for experience: ripple-like \nevents and synaptic diversity. BioRxiv, 891259. (2019). \n33. Buzsáki G, Hippocampal sharp wave-ripple: A cognitive biomarker for episodic \nmemory and planning. Hippocampus 25, 1073–1188 (2015). \n34. Bliss TV, Lømo T, Long-lasting potentiation of synaptic transmission in the dentate \narea of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. \n232, 331–356 (1973). \n35. Malenka RC, Nicoll RA, Long-term potentiation--a decade of progress? Science 285, \n1870–1874 (1999). \n36. Buzsáki G, Long-term changes of hippocampal sharp-waves following high frequency \nafferent activation. Brain Res 300, 179–182 (1984). \n37. Behrens CJ, van den Boom LP, de Hoz L, Friedman A, Heinemann U, Induction of \nsharp wave-ripple complexes in vitro and reorganization of hippocampal networks. \nNat. Neurosci. 8,1560–1567 (2005). \n38. Schlingloff D, Kali S, Freund TF, Hajos N, Gulyás AI, Mechanisms of sharp wave \ninitiation and ripple generation. J. Neurosci. 34, 11385–11398 (2014). \n39. Girardeau G, Benchenane K, Wiener SI, Buzsáki G, Zugaro MB, Selective \nsuppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 12, 1222–\n1223 (2009). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n21 \n \n40. Jadhav SP, Kemere C, German PW, Frank LM, Awake hippocampal sharp-wave \nripples support spatial memory. Science 336, 1454–1458 (2012). \n41. Fernández-Ruiz A, Oliva A, Fermino de Oliveira E, Rocha-Almeida F, Tingley D, \nBuzsáki G, Long-duration hippocampal sharp-wave ripples improve memory. Science \n364, 1082–1086 (2019). \n42. Foster DJ, Wilson MA, Reverse replay of behavioural sequences in hippocampal place \ncells during the awake state. Nature 440, 680–683 (2006) \n43. Pinheiro PS, Mulle C, Presynaptic glutamate receptors: physiological functions and \nmechanisms of action. Nat. Rev. Neurosci. 9, 423–436 (2008). \n44. Sakimoto Y, Shintani A, Yoshiura D, Goshima M, Kida H, Mitsushima D, A critical \nperiod for learning and plastic changes at hippocampal CA1 synapses. Sci. Rep. 12, \n7199, doi: 10.1038/s41598-022-10453-z, (2022). \n45. Teyler TJ, Perkins AT 4th, Harris KM, The development of long-term potentiation in \nhippocampus and neocortex. Neuropsychologia 27, 31–39 (1989). \n46. Malenka RC, Nicoll RA, Long-term potentiation--a decade of progress? Science 285, \n1870–1874 (1999). \n47. Auerbach JM, Segal M, Muscarinic receptors mediating depression and long-term \npotentiation in rat hippocampus. J. Physiol. 492, 479-493, (1996).  \n48. Fisahn A, Pike FG, Buhl EH, Paulsen O, Cholinergic induction of network oscillations \nat 40 Hz in the hippocampus in vitro. Nature 394, 186-189 (1998). \n49. Mitsushima D, Takase K, Funabashi T, Kimura F, Gonadal steroid hormones maintain \nthe stress-induced acetylcholine release in the hippocampus: simultaneous \nmeasurements of the extracellular acetylcholine and serum corticosterone levels in the \nsame subjects. Endocrinology 149, 802-811 (2008). \n50. Takase K, Sakimoto Y, Kimura F, Mitsushima D. Developmental trajectory of \ncontextual learning and 24-h acetylcholine release in the hippocampus. Sci. Rep. 4, \n3738 doi:10.1038/ srep03738, (2014).  \n51. Ishikawa J, Mitsushima D. Causality between spontaneous high-frequency firing \n\"super bursts\" of CA1 neurons and contextual learning. Neuro2024. 2P-201 (2024).  \n52. Yousefi B, Farjad M, Nasehi M, Zarrindast MR, Involvement of the CA1 GABAA \nreceptors in ACPA-induced impairment of spatial and non-spatial novelty detection in \nmice. Neurobiol. Learn. Mem. 100, 32–40 (2013). \n53. Cohen SJ, Munchow AH, Rios LM, Zhang G, Asgeirsdóttir HN, Stackman RW Jr., The \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n22 \n \nrodent hippocampus is essential for nonspatial object memory. Curr Biol. 23, 1685–\n1690 (2013).  \n54. Fairhall AL, Lewen GD, Bialek W, de Ruyter Van Steveninck RR, Efficiency and \nambiguity in an adaptive neural code. Nature. 412, 787-92, (2001). \n55. Reynolds JH, Heeger DJ, The normalization model of attention. Neuron. 61, 168-185, \n(2009) \n56. Jeong N, Singer AC, Learning from inhibition: Functional roles of hippocampal CA1 \ninhibition in spatial learning and memory. Curr Opin Neurobiol. 343, 857-863, (2022).  \n57. Staresina BP, Wimber M, A neural chronometry of memory recall. Trends Cogn. Sci. \n23, 1071-1085 (2019).  \n58. Zhang Y, Cao L, Varga V, Jing M, Karadas M, Li Y, Buzsáki G, Cholinergic \nsuppression of hippocampal sharp-wave ripples impairs working memory. Proc. Natl. \nAcad. Sci. U.S.A. 118, e2016432118 (2021).  \n59. Noguchi A, Huszár R, Morikawa S, Buzsáki G, Ikegaya Y, Inhibition allocates spikes \nduring hippocampal ripples. Nat. Commun. 13, 1280. doi: 10.1038/s41467-022-28890-\n28899 (2022). \n60. Szabo GG, Farrell JS, Dudok B, Hou WH, Ortiz AL, Varga C, Moolchand P, Gulsever \nCI, Gschwind T, Dimidschstein J, Capogna M, Soltesz I, Ripple-selective GABAergic \nprojection cells in the hippocampus. Neuron 110, 1959-1977 (2022).  \n61. Vancura B, Geiller T, Grosmark A, Zhao V, Losonczy A, Inhibitory control of sharp-\nwave ripple duration during learning in hippocampal recurrent networks. Nat. Neurosci. \n26, 788–797 (2023) \n62. Ellender TJ, Nissen W, Colgin LL, Mann EO, Paulsen O, Priming of hippocampal \npopulation bursts by individual perisomatic-targeting interneurons. J. Neurosci. 30, \n5979–5991 (2010). \n63. Bittner KC, Milstein AD, Grienberger C, Romani S, Magee JC, Behavioral time scale \nsynaptic plasticity underlies CA1 place fields. Science 357:1033–1036 (2017). \n64. Blair GJ, Guo C, Wang S, Fanselow MS, Golshani P, Aharoni D, Blair HT, \nHippocampal place cell remapping occurs with memory storage of aversive \nexperiences. eLife, e80661 (2023).  \n65. Mallory CS, Widloski J, Foster DJ. The time course and organization of hippocampal \nreplay. Science 387: 541-548 (2025). \n66. Norimoto H, Makino K, Gao M, Shikano Y, Okamoto K, Ishikawa Y, Sasaki T, Hioki H, \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n23 \n \nFujisawa S, Ikegaya Y, Hippocampal ripples down-regulate synapses. Science 359, \n1524-1527 (2018). \n67. Sakimoto Y, Yang Y, Kida H, Mitsushima D, Phosphorylation of GABAA receptor b3 \nsubunit at Ser408-409 is essential for contextual learning at hippocampal CA1 synapses. \nSci. Rep. 15, 21525 (2025). \n68. Yang Y, Ishikawa J, Mitsushima D, Super bursts reorganize hippocampal firing and \npromote synaptic diversity during stress-induced memory encoding. Soc. Neurosci. \nAbstr. C45 (2025). \n69. Paxinos G, Watson C, The Rat Brain in Stereotaxic Coordinates., 6th edn., Academic \nPress, San Diego (2006). \n70. Kida H, Tsuda Y, Ito N, Yamamoto Y, Owada Y, Kamiya Y, Mitsushima, D, Motor \ntraining promotes both synaptic and intrinsic plasticity of layer II/III pyramidal neurons \nin the primary motor cortex. Cereb. Cortex 26, 3494-3507 (2016). \n71. Kida H, Kawakami R, Sakai K, Otaku H, Imamura K, Han-Thiri-Zin, Sakimoto Y, \nMitsushima D, Motor training promotes both synaptic and intrinsic plasticity of layer V \npyramidal neurons in the primary motor cortex. J. Physiol. 601, 335-353 (2023). \n72. Sakimoto Y, Mizuno J, Kida H, Kamiya Y, Ono Y, Mitsushima D, Learning promotes \nsubfield-specific synaptic diversity in hippocampal CA1 neurons. Cereb. Cortex 29, \n2183–2195 (2019). \n73. Sheather SJ, Density estimation. Statist. Science 19, 588–597 (2004). \n74. Silverman BW, Density estimation for statistics and data analysis. Monographs on \nstatistics and applied probability. Chaoman and Hall, London (1986). \n75. Mitsushima D, Hei D, Terasawa E, g-Aminobutyric acid is an inhibitory \nneurotransmitter restricting the release of luteinizing-hormone releasing hormone \nbefore the onset of puberty. Proc. Natl. Acad. Sci. U.S.A. 91, 395-399 (1994). \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n24 \n \nFigures and the legends \n \n \nFigure 1. Experimental design for episodic memory and classification of CA1 multi-\nunit firing patterns. (A) Timeline of recording sessions and 10-minute episodic exposures. \n(B) Schematic illustrations of the four episodic experiences. Memory acquisition was \nassessed on the following day (2nd exposure). Data are shown as mean ± SD; gray circles \nrepresent individual rats with lines indicating within-subject changes. *P < 0.05 vs. 1st \nexposure. (C) Photograph of a recorded animal. (D) Movable recording electrode with an \nenlarged tip. (E) Features of super bursts and ripple firings. The box indicates 50% central \narea with a line that represents the median. The vertical line indicates minimum to \nmaximum data area without outliers. The green dot represents the average. Sample size \n(number of events) is indicated below each bar. (F–I) Examples of multiple-unit activity from \nCA1 recorded at 25 kHz and filtered between 300–10,000 Hz. These traces were recorded \nfrom an electrode that was implanted in the same animal. (F) Basal firings. (G) Super burst. \n(H) Silent period. (I) Ripple firings (three examples). Note: Spike classification using the \nSpike2 algorithm is unreliable during super bursts and ripple firings (see Fig. S1). Scale bar \n= 50 ms. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n25 \n \n \n \nFigure 2. Experience-dependent induction of high-frequency CA1 multi-unit firing \n(super bursts). (A) Representative trace showing baseline firing and an episode-induced \nsuper burst in CA1 neurons. (B) Time course of super burst occurrences before, during, \nand after episodic experiences in individual animals. Horizontal black bars indicate the 10-\nmin episode window. (C–D) Quantification of the occurrence (C) and total duration (D) of \nsuper bursts in 3-min bins across time. (E) Scatter plots of individual super burst events, \nshowing episode-specific differences in duration (x-axis) and normalized firing rate (y-axis). \nMultidimensional analysis of the features showed distinct patterns based on episode type \n(Table 1, by MANOVA). Pre-experience baseline is set to 1. Data are shown as mean ± SD. \nThe number of recordings in each group is shown in parentheses. *P < 0.05, **P < 0.01 vs. \npre-experience baseline. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n26 \n \n \nFigure 3. Experience -dependent diversification of ripple firings in CA1 neurons.  (A) \nExample trace showing a silent period flanked by two ripple firings. (B–C) Episodic \nexperience increased the total duration of silent periods (B) and the occurrence of ripple \nfirings (C). Data are shown as mean ± SD.  (D) Four extracted features of ripple firings: \namplitude, duration, arc length, and number of negative peaks. (E) Temporal dynamics of \neach ripple firing feature across conditions, showing episode-dependent modulation. The box \nindicates 50% central area with a line that represents the median. The vertical line indicates \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n27 \n \nminimum to maximum data area without outliers. Sample size (number of ripple firing) is \nindicated below each bar. (F) Information entropy per ripple firing, derived from the \ndistribution of waveform features, increased after experience. (G) Multidimensional analysis \nof the four features revealed distinct clustering patterns based on episode type (Table 2, by \nMANOVA, graphs show selected three dimensions). (H) Correlations between super bursts \nand ripple features before (left) and after (right) the experience. Only post -experience \nfeatures showed significant correlation. *P < 0.05, **P < 0.01 vs. pre-experience. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n28 \n \n \nFigure 4. Experience-specific synaptic plasticity at excitatory and inhibitory CA1 \nsynapses. (A) Representative traces of miniature excitatory (mEPSCs, top) and inhibitory \n(mIPSCs, bottom) postsynaptic currents from the same CA1 neuron. (B) Mean amplitudes \nof mEPSCs and mIPSCs (top), and their density distribution (bottom). Although only two \ndimensions are shown, four -dimensional analysis (including frequency data) revealed \nepisode type-specific differences (Table 3, by MANOVA). (C) Box plots showing experience-\ndependent changes in the four synaptic parameters: amplitude and frequency of mEPSCs \nand mIPSCs (by ANOVA). (D) Information entropy for each neuron, calculated from the four \nsynaptic features, showed episode  type-specific diversification (Table 3, by MANOVA). (E) \nBox plots of individual self-entropy parameters (by ANOVA). The box indicates 50% central \narea with a line that represents the median. The vertical line indicates minimum to maximum \ndata area without outliers. Sample size (number of neurons) is indicated below each bar. *P \n< 0.05, **P < 0.01 vs. control.  \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n29 \n \n \n \n \n \n \n \n \n \n \n \n \nFigure 5. A proposed model for CA1 encoding of episodic experiences. After exposure \nto distinct episodes, CA1 neurons exhibit experience -specific patterns of high -frequency \nfiring (super bursts), which may induce synaptic reorganization. This synaptic diversification \nin turn shapes the diversity of ripple firings ( sharp), while increased inhibitory synaptic \nstrength contributes to silent period formation (rest). This cascade may underlie the encoding \nof recent experience in hippocampal circuits. Illustration not drawn to scale. See Discussion \nfor detailed explanation. Note that CA1 contains over 400,000 and 5,000,000 pyramidal cells \nin rats and humans, respectively.  \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n30 \n \nTable 1. Distinct super burst patterns emerge during episodic experience: MANOVA and post \nhoc analysis (related to Fig. 2). \n \nComparison Figs 2C & 2D Figure 2E \nOverall F8, 1304 = 9.761, P < 0.001** F8, 558 = 11.535, P < 0.001** \nRestraint vs Female F2, 277 = 14.066, P < 0.001** F2, 177 = 24.009, P < 0.001** \nRestraint vs Male F2, 277 = 12.948, P < 0.001** F2, 107 = 20.873, P < 0.001** \nRestraint vs Object F2, 249 = 8.367, P < 0.001** F2, 66 = 19.088, P < 0.001** \nRestraint vs Control F2, 221 = 9.517, P < 0.001** F2, 82 = 33.336, P < 0.001** \nFemale vs Male F2, 305 = 4.793, P < 0.001** F2, 181 = 6.037, P = 0.003** \nFemale vs Object F2, 277 = 13.075, P < 0.001** F2, 140 = 1.589, P = 0.208 \nFemale vs Control F2, 249 = 7.004, P < 0.001** F2, 156 = 4.695, P = 0.011* \nMale vs Object F2, 277 = 5.160, P = 0.006** F2, 70 = 1.912, P = 0.155 \nMale vs Control F2, 249 = 1.248, P = 0.289 F2, 86 = 2.806, P = 0.066 \nObject vs Control F2, 221 = 1.916, P = 0.150 F2, 45 = 0.445, P = 0.644 \n \nSummary of MANOVA and subsequent post hoc tests comparing the integrated features \n(incidence vs duration or frequency vs duration) of super bursts across different episodic \nexperiences. Significant differences highlight experience -specific modulation of ensemble -\nlevel high-frequency firing. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n31 \n \nTable 2. Episodic experience diversifies ripple firing features: MANOVA and post hoc analysis \n(related to Fig. 3) \n \nComparison Integrated features Integrated self-entropies \nOverall F12, 11993 = 48.996, P < 0.001** F16, 16223 = 35.748, P < 0.001** \nRestraint vs Female F4, 2291 = 30.810, P < 0.001** F4, 2291 = 33.374, P < 0.001** \nRestraint vs Male F4, 2262 = 31.515, P < 0.001** F4, 2262 = 42.701, P < 0.001** \nRestraint vs Object F4, 2052 = 86.412, P < 0.001** F4, 2052 = 48.154, P < 0.001** \nRestraint vs Control F4, 1813 = 48.846, P < 0.001** F4, 1813 = 52.735, P < 0.001** \nFemale vs Male F4, 2478 = 3.395, P = 0.009** F4, 2478 = 3.920, P = 0.004** \nFemale vs Object F4, 2268 = 60.959, P < 0.001** F4, 2268 = 12.403, P < 0.001** \nFemale vs Control F4, 2029 = 18.258, P < 0.001** F4, 2029 = 43.749, P < 0.001** \nMale vs Object F4, 2239 = 94.403, P < 0.001** F4, 2239 = 6.812, P < 0.001** \nMale vs Control F4, 2000 = 32.546, P < 0.001** F4, 2000 = 31.836, P < 0.001** \nObject vs Control F4, 1790 = 23.418, P < 0.001** F4, 1790 = 29.895, P < 0.001** \n \nSummary of MANOVA and subsequent post hoc tests comparing integrated ripple features \n(amplitude, duration, arc length, and number of negative peaks) following different episodic \nexperiences. Increased variability and entropy measures indicate enhanced neural \nrepresentation diversity. Detailed statistical results were shown in Tables S5 (integrated \nfeatures) and S6 (integrated  self-entropies). Significant differences highlight experience -\nspecific features of ripple firings and the diversity. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint \n\n32 \n \nTable 3. Experience-specific reorganization of excitatory and inhibitory synaptic inputs: \nMANOVA and post hoc analysis (related to Fig. 4) \n \nComparison Synaptic parameters Self-entropies \nOverall F16, 551 = 4.729, P < 0.001** F16, 551 = 4.361, P < 0.001 \nRestraint vs Female F 4,71 = 0.834, P = 0.508 F 4,71 = 0.623, P = 0.648 \nRestraint vs Male F 4,71 = 4.046, P = 0.005** F 4,71 = 4.188, P = 0.004** \nRestraint vs Object F 4,64 = 3.353, P = 0.015* F 4,64 = 2.821, P = 0.032* \nRestraint vs Control F 4,70 = 5.305, P < 0.001** F 4,70 = 3.834, P = 0.007** \nFemale vs Male F 4,75 = 9.592, P < 0.001** F 4,75 = 5.821, P < 0.001** \nFemale vs Object F 4,68 = 5.049, P < 0.001** F 4,68 = 4.092, P = 0.005** \nFemale vs Control F 4,74 = 5.729, P < 0.001** F 4,74 = 4.497, P = 0.003** \nMale vs Object F 4,68 = 4.794, P = 0.002** F 4,68 = 5.141, P < 0.001** \nMale vs Control F 4,74 = 15.762, P < 0.001** F 4,74 = 13.174, P < 0.001** \nObject vs Control F 4,67 = 3.767, P = 0.008** F 4,67 = 2.988, P = 0.025** \n \nSummary of MANOVA and subsequent post hoc tests comparing integrated excitatory and \ninhibitory synaptic current parameters (amplitude and frequency of mEPSCs and mIPSCs) \nrecorded ex vivo from CA1 pyramidal neurons. Significant differences highlight experience-\nspecific features of integrated synaptic remodeling and the diversity. \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted December 27, 2025. ; https://doi.org/10.64898/2025.12.26.696628doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}