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Hippocampal Replay of Extended Experience
Open accessAbstract
During pauses in exploration, ensembles of place cells in the rat hippocampus re-express firing sequences corresponding to recent spatial experience. Such "replay" co-occurs with ripple events: short-lasting (approximately 50-120 ms), high-frequency (approximately 200 Hz) oscillations that are associated with increased hippocampal-cortical communication. In previous studies, rats exploring small environments showed replay anchored to the rat's current location and compressed in time into a single ripple event. Here, we show, using a neural decoding approach, that firing sequences corresponding to long runs through a large environment are replayed with high fidelity and that such replay can begin at remote locations on the track. Extended replay proceeds at a characteristic virtual speed of approximately 8 m/s and remains coherent across trains of ripple events. These results suggest that extended replay is composed of chains of shorter subsequences, which may reflect a strategy for the storage and flexible expression of memories of prolonged experience.
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“The reactivation of task-relevant regions was found to be particular prominent during sleep oscillatory events linked to memory consolidation, such as spindles and SOs.”
From Sleep—A brain-state serving systems memory consolidation (2023) · cites this paper as [108]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“both sleep and wake replay can represent knowledge about an environment and also share anticipatory features, such that they can e.g., include offline ''pre-activations'' of trajectories entailing visible but yet unexplored paths and locations.”
From Sleep—A brain-state serving systems memory consolidation (2023) · cites this paper as [148]
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“Spindle activity is increased over cortical regions involved in prior learning and, moreover, positively correlates with post sleep retrieval performance 180,181 and appears to accompany memory reactivations during sleep.108,114”
From Sleep—A brain-state serving systems memory consolidation (2023) · cites this paper as [108]
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“Reactivations of hippocampal cell firing patterns occur also during waking when the animal rests after task performance or during brief pauses of active behavior like explo-ration or running in a maze (191, 268, 556, 673, 870; see Ref. 175 for a review).”
From About Sleep's Role in Memory (2013) · cites this paper as [268]
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“In larger experimental environments, both forward and reverse replay can occur over multiple SW-R events thereby covering longer distances (268).”
From About Sleep's Role in Memory (2013) · cites this paper as [268]
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“…replay detection in the decoded spatial domain affords advantages over methods that examine firing order across individual units, such as pair-wise correlation (Wilson and McNaughton, 1994) and spike-sequence detection (Diba and Buzsáki, 2007; Foster and Wilson, 2006; Lee and Wilson, 2004).”
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“This interpretation of the functional role of awake replay is also consistent with work suggesting a high degree of overlap in the cognitive processes supporting both episodic recall and the evaluation of future events in humans (Buckner and Carroll, 2007; Schacter et al., 2007).”
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Self-citationSleep-dependent memory consolidation
Stickgold, R. (2005) · Nature · doi:10.1038/nature04286 · Cited by 1,857 · cited 2×
“For example, the reverse replay event in Figure 4D proceeds in the B/A direction (from 7.5 to 2.5 m on the track) but uses the hippocampal ensemble code associated with running in the opposite A/B direction, as indicated by the blue color.”
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Items 701–800 of 806
Routing of non-mnemonic hippocampal ripples to subcortical structuresPreprint
Tingley, D., & Buzsáki, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/610402
“10 of the Radon transform; (Toft, 1996)), of the posterior probability matrix calculated with a Bayesian approach (Davidson et al., 2009)(replay score 2), and 3) the linear weighted correlation of the posterior probability matrix calculated with a Bayesian approach (Grosmark and Buzsáki, 2016)( replay score 3).”
MethodBuzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0011
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0012
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0005
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0013
Enhancing Brain Performance by Externalizing Thought
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0009
Internally Organized Activity During Offline Brain States
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0008
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0004
Neural correlates of navigation in large-scale space
Tanni, S. (2020) · UCL Discovery (University College London)
Behavior-dependent spatial maps enable efficient theta phase codingPreprint
Parra-Barrero, E., Diba, K., & Cheng, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.14.439787
“We performed a Bayesian decoding of positions from the ensemble neuronal firing (Zhang et al., 1998; Davidson et al., 2009) within each theta cycle in overlapping 90°bins with a step size of 30°.”
MethodInfluentialBhasin, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.19.456964
“Most replay studies have focused primarily on CA3 or CA1 (Buzsáki, 1986; Lee & Wilson, 2002; Csicsvari et al., 2007; Davidson et al., 2009), and more still have looked at fidelity of replays, whether in sleep or awake state, (Foster & Wilson, 2006; Karlsson & Frank, 2009) or on direction of replays…”
BackgroundAdvanced Age Has Dissociable Effects on Hippocampal CA1 and CA3 RipplesPreprint
DiCola, N. M., Lacy, A. L., Bishr, O. J., Kimsey, K. M., Whitney, J. L., Lovett, S. D., Burke, S. N., & Maurer, A. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.27.457373
“…or periods of quiet wakefulness (Buzsaki, 1986; Chrobak and Buzsaki, 1996; Foster and Wilson, 2006; Jackson et al., 2006; O’Neill et al., 2006; Csicsvari et al., 2007; Diba and Buzsáki, 2007; Davidson et al., 2009; Karlsson and Frank, 2009; Sullivan et al., 2011; Buzsáki, 2015; Giri et al., 2019).”
BackgroundBayesian Algorithmic Decoding of Acceleration and Speed Software (BADASS)
Wirtshafter, H. S., & Wilson, M. A. (2021) · Software Impacts · doi:10.1016/j.simpa.2021.100125
Self-citationAn Examination of the Influence of Landmarks in Human Spatial Navigation Using a Virtual Water Maze
Thornberry, C. (2019) · Maynooth University ePrints and eTheses Archive (Maynooth University)
Kaufman, A. B., Call, J., & Kaufman, J. C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108564113.008
Maler, L. (2011) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.13328963.14692063
Wolfer, D. P. (2013) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.718023375.793481793
Neural Networks with Nonlinear Couplings
Jahnke, S. (2015) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-5056
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0014
Faculty Opinions recommendation of Fast Sequences of Non-spatial State Representations in Humans.
Kiebel, S. J. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.726435693.793559771
Separable Signaling Streams of NMDA Receptors Support Distinct Aspects of Spatial Cognition
Dumas, T. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.23963-1
(2022) · Cambridge University Press eBooks · doi:10.1017/9781108768450.019
Memory Consolidation: Neural Data Analysis and Mathematical Modeling
Tatsuno, M., & Eckert, M. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-44982-7_31-1
Disk-Drive-Like Operations in the HippocampusPreprint
Nicola, W., Dupret, D., & Clopath, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.05.511000
“Evidence for the affirmative to this hypothesis is present in the literature in the form of so-called extended replays, where a trajectory is replayed as multiple, sequential sharp-wave-ripple complexes [28, 29].”
BackgroundMemory Consolidation: Neural Data Analysis and Mathematical Modeling
Tatsuno, M., & Eckert, M. J. (2022) · Springer eBooks · doi:10.1007/978-3-031-03945-4_31
Neuromorphic Neural Interfaces
Broccard, F. D., Joshi, S., Wang, J., & Cauwenberghs, G. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_41 · Cited by 1
Postnatal maturation of in vivo membrane potential dynamics during hippocampal ripplesPreprint
Noguchi, A., & Ikegaya, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.10.527963
“In addition to the characteristics of individual SWRs, we reported an age-dependent increase in successive SWRs (Davidson et al., 2009; Yamamoto and Tonegawa, 2017), which are defined herein as more than two successive SWRs with interevent intervals of 250 ms. Successive SWRs are thought to arise…”
BackgroundInfluentialDevelopment of Hippocampal Replay with Learning in Spatial Memory of Precise Goal Locations
Li, Z., Zhu, N., Wang, Y., Wang, X., Wang, L., & Zheng, C. (2022) · International Conference on Biomedical and Bioinformatics Engineering (ICBBE) · doi:10.1145/3574198.3574241
“The resulting R-squared value was compared to 1,500 regression values where the order of the time bins were shuffled [14, 18].”
MethodStochastic characterization of navigation strategies in an automated variant of the Barnes mazePreprint
Lee, J.-Y., Jung, D., & Royer, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.14.536859
“However, in our experimental paradigm, this behavior may have been promoted by the vestibule-reward association rather than anxiety.”
BackgroundGrid Codes versus Multi-Scale, Multi-Field Place Codes for SpacePreprint
Dietrich, R., Waniek, N., Stemmler, M., & Knoll, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.18.545252
“These studies revealed that place cells can express multiple place fields under certain circumstances Kjelstrup et al. (2008); Park et al. (2011); Davidson et al. (2009); Rich et al. (2014) and that the size of these fields can vary O’Keefe and Burgess (1996); Fenton et al. (2008).”
BackgroundDorsomedial prefrontal cortex (DMPFC) prioritizes social learning during restPreprint
Jimenez, C. A., & Meyer, M. L. (2023) · doi:10.31234/osf.io/5rzv6
Theta cycle dynamics of spatial representations in the lateral septumPreprint
Bzymek, K., & Kloosterman, F. (2023) · eLife · doi:10.7554/elife.90207
Self-citationThermoMaze: A behavioral paradigm for readout of immobility-related brain eventsPreprint
Vöröslakos, M., Zhang, Y., McClain, K., Huszár, R., Rothstein, A., & Buzsáki, G. (2023) · eLife · doi:10.7554/elife.90347.1
Altered patterning of neural activity in a tauopathy mouse modelPreprint
Hoffman, C., Cheng, J., Morales, R., Ji, D., & Dabaghian, Y. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.23.586417
“Indeed, a replay may last between a couple of dozens to a couple of hundreds of milliseconds, which is comparable to the characteristic interval between spikes triggered by the animal’s physical passing through place fields [60, 64–68].”
BackgroundState-Dependent Online Reactivations for Different Learning Strategies in ForagingPreprint
Son, S., Wang, M. Z., Hayden, B. Y., & Yoo, S. B. M. (2024) · SSRN Electronic Journal · doi:10.1101/2024.03.25.586512 · Cited by 1
Mou, X., & Ji, D. (2025) · eLife · doi:10.7554/elife.97884.3
Learning Sequence Attractors in Recurrent Networks with Hidden NeuronsPreprint
Lu, Y., & Wu, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4786003
Marchesi, P., Bos, J. J., Vinck, M. A., & Pennartz, C. M. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.18.590059
Spatial Map: Through the Lens of Behavioral Tag and Capture
Raghuraman, R., & Hussaini, S. A. (2024) · Springer eBooks · doi:10.1007/978-3-031-54864-2_21
Stochastic characterization of navigation strategies in an automated variant of the Barnes mazePreprint
Lee, J.-Y., Jung, D., & Royer, S. (2024) · eLife · doi:10.7554/elife.88648.2
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.002.0006
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.002.0003
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.002.0004
Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive BehaviorPreprint
Liu, J., Younk, R., Drahos, L. M., Nagrale, S. S., Yadav, S., Widge, A. S., & Shoaran, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.06.597165
“This is achieved by assessing the extent of information that neural activity conveys about a symptom or behavior and examining how this information varies across different brain areas, experimental conditions, and states of disorder [51–53].”
BackgroundStochastic characterization of navigation strategies in an automated variant of the Barnes mazePreprint
Lee, J.-Y., Jung, D., & Royer, S. (2024) · eLife · doi:10.7554/elife.88648.3
Lee, J.-Y., Jung, D., & Royer, S. (2024) · eLife · doi:10.7554/elife.88648.3.sa4
Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding
Luo, D., Giri, B. K., Diba, K., & Kemere, C. T. (2024) · Neural Computation · doi:10.1162/neco_a_01685
“…replay trajectories show a continuum of conformity to the original experience, including variability in momentum and both forward and reverse reexpression (Lee & Wilson, 2002; Foster & Wilson, 2006; Diba & Buzsáki, 2007; Csicsvari et al., 2007; Davidson et al., 2009; Krause & Drugowitsch, 2022).”
BackgroundSelf-avoidance dominates the selection of hippocampal replayPreprint
Mallory, C. S., Widloski, J., & Foster, D. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.18.604185
“On the linear track, replays were classified as forward if the decoded heading direction aligned with the direction of replay motion across the track, and reverse if they were opposite (i.e., a trajectory depicting the animal running backwards) 9,10,19 ( Fig.”
BackgroundMethodInfluentialThermoMaze: A behavioral paradigm for readout of immobility-related brain eventsPreprint
Vöröslakos, M., Zhang, Y., McClain, K., Huszár, R., Rothstein, A., & Buzsáki, G. (2024) · eLife · doi:10.7554/elife.90347.2
Noise-Driven Membrane Adaptation for Robust Context-Dependent Learning in Spiking Neural Networks
Wang, Y., & Yang, S. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662715
“…to well test NALIF's performance related to SNN context-dependent learning, we constructed a specific context-dependent learning task, which is often used to explore the learning and neural representation of contextual item conjunctions [17], which is suitable for our training and testing needs.”
MethodEmergence of novel-path sequences during a maze task in hippocampal CA3 spiking neural network model
Takada, K., & Tateno, K. (2024) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.16.64
Modeling flexible behavior with remapping-based hippocampal sequence learning
Ito, Y., & Toyoizumi, T. (2026) · eLife · doi:10.7554/elife.106506.4
Theories of Hippocampal Function
McKenzie, S., Gobbo, F., & Morris, R. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780190065324.003.0014
Shimbo, A., Sekine, Y., Kashiwagi, S., & Fujisawa, S. (2025) · eLife · doi:10.7554/elife.105155.1
Shimbo, A., Sekine, Y., Kashiwagi, S., & Fujisawa, S. (2025) · eLife · doi:10.7554/elife.105155
Hippocampal reactivation and replay: Findings and insights from rodent studies
Kim, B., & Diba, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00091-2 · Cited by 1
Hippocampal Replay Mechanisms for Adaptive Memory Optimization: A Sleep-Inspired AI Framework
Vita, F. D., Catalfamo, E., Bruneo, D., & Talanov, M. O. (2025) · BioNanoScience · doi:10.1007/s12668-025-02070-7
“In this paper, we propose that hippocampal memory consolidation, modelled in particularly through bidirectional replay mechanisms [6, 7], plays a crucial role in optimizing memory and enabling adaptive behaviour over time [8].”
A cortical–hippocampal communication undergoes rebalancing after new learningPreprint
Hall, A. F., & Wang, D. V. (2025) · eLife · doi:10.7554/elife.107370
“…CA1 of the hippocampus, facilitate hippocampal reactivations and accompany cortical reactivations of memory-related neurons, a process referred to as replay ( Buzsáki, 2015 ; Buzsáki et al., 1992 ; Davidson et al., 2009 ; Diba and Buzsáki, 2007 ; Foster and Wilson, 2006 ; Lee and Wilson, 2002 ).”
A cortical–hippocampal communication undergoes rebalancing after new learningPreprint
Hall, A. F., & Wang, D. V. (2025) · eLife · doi:10.7554/elife.107370.1
Mou, X., & Ji, D. (2025) · eLife · doi:10.7554/elife.97884.2
Shin, J. D., Satchell, M., Miller, P. C., & Jadhav, S. P. (2026) · eLife · doi:10.1101/2025.09.15.676366
A Bio-realistic Synthetic Hippocampus for Robotic Cognition
Vallverdú, J., Feinstein, X., Robertson, P., Kipelkin, I., & Talanov, M. O. (2025) · BioNanoScience · doi:10.1007/s12668-025-02229-2
Németh, E., Chartouny, A., Jedlovszky, K., Freire, I. T., & Khamassi, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685837
“Finding References Replay locations Initiation bias [5, 6, 22] Reward bias [23, 24, 25, 2, 26, 27] Preferential replay of decision points [23, 5, 28]”
Long‐Term Memory Updating Parallels Altered Awake and Sleep Hippocampal Replays
Tong, J., Xing, Y., Zheng, Y., Wang, L., Shao, S., Wu, J., Ma, L., Liu, S., Liu, N., Qi, X., et al. (2025) · Advanced Science · doi:10.1002/advs.202416480
Sleep-modulated disinhibition enables replay for memory consolidation, accelerated by ripplesPreprint
Dutta, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693276
“…during simulated sequence replay underlies ripple diversity: strong LI yields discrete chains (doublets, triplets) that link sequence fragments [99, 116, 117]; intermediate LI concatenates them into prolonged events preserving replay sequence [80, 105, 117]; weak LI induces fusion and sequence…”
A statistical note on the detection of autoassociative dynamics in hippocampal place-cell sequencesPreprint
Gönner, L., Vitay, J., & Hamker, F. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/365239
“This approach assumes that all N units follow independent Poisson firing statistics, and that occupancy is uniform across locations (Davidson et al., 2009).”
BackgroundA unifying account of replay as context-driven memory reactivationPreprint
Zhou, Z., Kahana, M. J., & Schapiro, A. C. (2025) · eLife · doi:10.7554/elife.99931.2
Capturing rapid learning in an extended successor representation theory of the cognitive mapPreprint
Cho, S., & McClelland, J. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.25.696522
“These roll-outs transiently activate representations of non-current but salient states, enabling remote feature prediction and state-value estimation necessary for planning 49,53,54 .”
Michon, F., Gazzola, V., & Keysers, C. (2026) · Nature Communications · doi:10.64898/2026.01.03.697465
Disruption of theta-timescale spiking impairs learning but spares hippocampal replayPreprint
Joshi, A., Comrie, A. E., Bray, S. R., Mankili, A., Guidera, J. A., Nevers, R., Sun, X., Monroe, E. J., Kharazia, V., Ly, R., et al. (2026) · eLife · doi:10.7554/elife.109375 · Cited by 1
A microcircuit model for initiation and reward-dependent termination of hippocampal replayPreprint
Musset, H., & Fukai, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.18.700234 · Cited by 1
“We used Bayesian decoding to reconstruct the trajectory from the spikes within a short time window and the tuning curves of the place-coding cells (Zhang et al. 1998; Davidson et al. 2009).”
Nilsson, S., Reid, M. J., Smith, M. T., & Coon, W. G. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1682450 · Cited by 1
Modeling flexible behavior with remapping-based hippocampal sequence learningPreprint
Ito, Y., & Toyoizumi, T. (2026) · eLife · doi:10.7554/elife.106506.2
Anterior and posterior retrosplenial cortex form distinct visuospatial circuits in the mouse
Wei, Y.-T., Couto, J., Kloosterman, F., & Bonin, V. (2026) · Nature Communications · doi:10.1038/s41467-026-70762-z · Cited by 1
“We employed a Bayesian algorithm 64,65 to estimate the probability of position given the Δ F / F 0 of a fi xed number of randomly selected neurons.”
Self-citationLa somniloquie : un modèle pour l'étude de la consolidation mnésique verbale pendant le sommeil
Uguccioni, G. (2015) · theses.fr (ABES) · doi:10.70675/e340385dzc46bz4e59z9c16z216cb0dd5810
Hippocampal plasticity underlying learning and memory processes in healthy and diseased conditions
Petsophonsakul, P. (2017) · theses.fr (ABES) · doi:10.70675/5fdda64ez871dz42c8za517zf44c3618705e
Carponcy, J. (2016) · theses.fr (ABES) · doi:10.70675/73b8e92fzcf31z47b3z9f72z5646ff84d048
Sarazin, M. (2021) · theses.fr (ABES) · doi:10.70675/bbd73c82z58a3z46c2za17cz56614cc8acbd
Bryzgalov, D. V. (2021) · theses.fr (ABES) · doi:10.70675/c11854e0ze39ez4a3fza554zf56b05c49055 · Cited by 1
Hippocampo-cortical interaction underlying episodic memory
Bochereau, A. (2020) · theses.fr (ABES) · doi:10.70675/8f206effz0c76z40d0z91e6z15ccb174c9f1
Shin, J. D., Satchell, M., Miller, P., & Jadhav, S. P. (2026) · eLife · doi:10.7554/elife.110795
“Previous studies have shown that the refractoriness of certain oscillatory events, such as spindles and SWRs, create distinct segregated periods for selective population activation ( Davidson et al., 2009 ; Darevsky et al., 2024 ; Mallory et al., 2025 ; Judák et al., 2022 ).”
Shin, J. D., Satchell, M., Miller, P., & Jadhav, S. P. (2026) · eLife · doi:10.7554/elife.110795.1
Neurodatascience: Past, Present, and Future
Cooper, K. W., Shahbaba, B., & Fortin, N. J. (2026) · Data Science in Science · doi:10.1080/26941899.2026.2619222
Optimal Transport Theory to Extract Spiking MotifsPreprint
Grimaldi, A., Sotomayor-Gómez, B., Gilson, M., Perrinet, L., Alamia, A., & Vinck, M. A. (2026) · Research Square · doi:10.21203/rs.3.rs-9518938/v1
Ramelteon facilitates hippocampal ripple occurrence and amplitude in micePreprint
Nakashima, M., Miyano, M., Kuroyanagi, H., Sasahara, A., Ikegaya, Y., & Matsumoto, N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.08.723673
Woodard, G. E., & Julien, C. (2026) · Behavioural Brain Research · doi:10.1016/j.bbr.2026.116267
Wagner, M., Chen, Y., Karuvally, A., Cameron, M., & Sejnowski, T. (2026) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2025.0260 · Cited by 2
Peterson, J. G., Erickson, M. T., Sheehan, A., Damphousse, C. C., & Redish, A. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.18.745488
Neural decoding of tourists' immersion in augmented reality
Lin, G., Li, M., Huang, X., & Liu, Y. (2026) · Annals of Tourism Research · doi:10.1016/j.annals.2026.104275
Prefrontal–thalamic goal states organize spatially aligned hippocampal maps
Golipour, Z., Mozaffarilegha, M., Yen, S.-F., Üstüner, C., & Ito, H. (2026) · Nature Communications · doi:10.1038/s41467-026-77240-6
“Following previous work 26,45 , we used short decoding windows to capture time-compressed dynamics (20-msbins shiftedevery5ms;seeMethods).”
A cortical–hippocampal communication undergoes rebalancing after new learningPreprint
Hall, A. F., & Wang, D. V. (2026) · eLife · doi:10.7554/elife.107370.2
Drift-diffusion dynamics of hippocampal replay
Wu, Z., & Wei, X.-X. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014761
Metadata
- Authors
- Davidson, T. J., Kloosterman, F., & Wilson, M. A.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kloosterman, Fabian
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2009-08
- Volume
- 63
- Issue
- 4
- Pages
- 497-507
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4364032
- OpenAlex
- W2079161053
- Cited by
- 806
- References
- 53
- Citations by year
- 2026: 41 · 2025: 54 · 2024: 68 · 2023: 57 · 2022: 56 · 2021: 65 · 2020: 49 · 2019: 72 · 2018: 42 · 2017: 41 · 2016: 49 · 2015: 42 · 2014: 57 · 2013: 40 · 2012: 26
- FWCI
- 11.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1477 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- National Institutes of Health (MH061976)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- hippocampal replay, place cells, spatial memory, neural decoding, memory storage
- MeSH terms
- Animals, Exploratory Behavior, Hippocampus, Learning, Male, Spatial Behavior, Time Factors, Rats, Long-Evans, Rats
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