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Replay and Time Compression of Recurring Spike Sequences in the Hippocampus
Open accessAbstract
Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of hippocampal neurons. Repeating spike sequences in the recorded cell assemblies were present in both the awake and sleeping animal in excess of what might be predicted by random variations. Spike sequences observed during wheel running were “replayed” at a faster timescale during single sharp-wave bursts of slow-wave sleep. We hypothesize that the endogenously expressed spike sequences during sleep reflect reactivation of the circuitry modified by previous experience. Reactivation of acquired sequences may serve to consolidate information.
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“In fact, entire spike trains observed during a task performed during prior waking reemerged in the same temporal order during succeeding sleep, with this “replay” running at a faster speed, i.e., in a time-compressed form (715, 716, 843).”
From About Sleep's Role in Memory (2013) · cites this paper as [843]
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“1) during SWS, firing patterns observed during waking are replayed at a much faster (10 -20 times) rate, suggesting a time-compressed form of memory reactivation (160, 551, 606, 715, 843, 1090);”
From About Sleep's Role in Memory (2013) · cites this paper as [843]
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Background
Research works printed but not linked: 5
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Buzsáki, G., Carpi, D., Csicsvari, J., Dragoi, G., Harris, K., Henze, D. A., & Hirase, H. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198574002.003.0014 · Cited by 7
Self-citationNew methods for understanding systems consolidationPreprint
Tayler, K. K., & Wiltgen, B. J. (2013) · Learning & Memory · doi:10.1101/lm.029454.112 · Cited by 7
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BackgroundDynamical modulation of theta-gamma coupling during REM sleepPreprint
Bandarabadi, M., Boyce, R., Herrera, C. G., Bassetti, C. L., Williams, S., Schindler, K. A., & Adamantidis, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/169656 · Cited by 7
Accelerated signal propagation speed in human neocortical dendritesPreprint
Oláh, G., Lákovics, R., Shapira, S., Leibner, Y., Szűcs, A. L., Csajbók, É., Barzó, P., Molnár, G., Segev, I., & Tamás, G. (2024) · eLife · doi:10.7554/elife.93781 · Cited by 10
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“Additionally, hippocampal sharp wave-ripples linked to sleep are associated with the reactivation of neuronal groups active during previous wakefulness (20, 21) (Figure 1).”
BackgroundSome theoretical results on neural spike train probability models
Chan, H. P., & Loh, W. (2007) · The Annals of Statistics · doi:10.1214/009053607000000280 · Cited by 6
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Storchi, R., Biella, G. E. M., Liberati, D., & Baselli, G. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004299 · Cited by 6
“Still, timing is likely not the only encoding means valid in the CNS. Repetitive spontaneous and evoked neuronal activation patterns evidenced time warping features, namely duration independent activity episodes, in most diverse behavioral conditions [18–20].”
BackgroundLearning-Induced Sequence Reactivation During Sharp-Wave Ripples: A Computational Study
Malerba, P., Tsimring, K., & Bazhenov, M. (2018) · Association for Women in Mathematics series · doi:10.1007/978-3-319-98684-5_11 · Cited by 6
Experience-dependent persistent Arc expression is reduced in the aged hippocampus
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“During the theta rhythm, principal cells in the hippocampus show sparse representations of the environment as observed in place cells [46], while the same cells can show intermittent and synchronous bursts of activity with concurrent LFP activities known as the sharp wave–ripple complex [47,48].”
BackgroundPropagation of Spike Sequences in Neural Networks
Tonnelier, A. (2010) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/100782504 · Cited by 6
“Repetitions of temporally precise sequences of spikes have been observed in many brain areas, variously referred to as ’sequences’ [31], ’temporal modules’ [20], or ’recurring patterns’ [37].”
Großberger, L., Battaglia, F. P., & Vinck, M. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/252791 · Cited by 7
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“Other efforts have focused on computing the likelihood of individual longer sequences occurring either computationally using shuffling techniques (Nadasdy et al., 1999) or theoretically (Lee & Wilson, 2002, 2004; Smith & Smith, 2006).”
BackgroundMethodA unifying account of replay as context-driven memory reactivation
Zhou, Z., Kahana, M. J., & Schapiro, A. C. (2026) · eLife · doi:10.1101/2023.03.22.533833 · Cited by 7
“Early studies showed that replay preserves the correlational structure and the temporal structure of multi-cell spiking patterns that underlie awake experiences [ 13 , 14 , 15 ].”
What Is the Contribution of iEEG as Compared to Other Methods to Cognitive Neuroscience?
Liu, J., & Xue, G. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_8 · Cited by 6
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“During replay, hippocampal place cells that fired during exposure to an environment are orderly reactivated at a subsequent time so that the initial experience is recapitulated over a very brief period of about 50–100 ms (Lee andWilson, 2002; Nadasdy et al., 1999; Skaggs and McNaughton, 1996, but see Quirk andWilson,…”
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Optimizing information processing in neuronal networks beyond critical states
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MFS: A Brain-Inspired Memory Formation System for GAN
Chang, Y., Wang, Y., Peng, J., Dong, Z., Li, H., & Li, W. (2021) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2021.3108706 · Cited by 5
A Variable Clock Underlies Internally Generated Hippocampal Sequences
Deng, X., Chen, S., Sosa, M., Karlsson, M. P., Wei, X.-X., & Frank, L. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1120-21.2022 · Cited by 5
“SWRs were detected on 4, 11, 7, or 5 tetrodes located in from CA1, respectively, by using an aggregated measure of the root mean square (rms) power in the 150–250Hz band across the tetrodes (Nádasdy et al., 1999).”
MethodDiCola, N. M., Lacy, A. L., Bishr, O. J., Kimsey, K. M., Whitney, J. L., Lovett, S. D., Burke, S. N., & Maurer, A. P. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.04.014 · Cited by 5
Accelerated signal propagation speed in human neocortical dendrites
Oláh, G., Lákovics, R., Shapira, S., Leibner, Y., Szűcs, A. L., Csajbók, É., Barzó, P., Molnár, G., Segev, I., & Tamás, G. (2025) · eLife · doi:10.1101/2022.09.30.510270 · Cited by 4
“…al., 2016 ) and single-cell spiking sequences emerging during human memory formation are replayed during successful memory retrieval ( Vaz et al., 2020 ) similar to results pioneered in the hippocampus of rodents ( Nádasdy et al., 1999 ; Skaggs and McNaughton, 1996 ; Wilson and McNaughton, 1994 ).”
Goirand-Lopez, L., Moulinier, M., Vigier, A., Boileau, C., Carleton, A., Muldoon, S. F., Marissal, T., & Crépel, V. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106260 · Cited by 5
“This surrogate data was created by reshuffling the intervals between each calcium transient (1,000 iterations) (Nádasdy et al., 1999).”
MethodAssociative Reactivation of Place–Reward Information in the Hippocampal–Ventral Striatal Circuitry
Lansink, C. S., & Pennartz, C. M. A. (2014) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-1969-7_4 · Cited by 4
“The compression factor we found is comparable to the factor ~7 reported for the medial prefrontal cortex [ 13 ] but considerably lower than the factor 20 found for the hippocampus [ 8 ], but see [ 92 ].”
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The statistical analysis of temporal resolution in the nervous system
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The Role of Sleep in Song Learning Processes in Songbird
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Beyond replay: Introduction to the special issue on hippocampal replay
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Hippocampal ripples coincide with “up-state” and cortical spindles in Retrosplenial CortexPreprint
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“The replay of neural ensembles in the hippocampus (Foster and Wilson, 2006; Nádasdy et al., 1999; Ólafsdóttir et al., 2016; Wilson and McNaughton, 1993), as well as the repeated activation of cortical patterns (Higgins et al., 2021; Peigneux et al., 2004; Tambini and Davachi, 2019) support this…”
Accelerated signal propagation speed in human neocortical microcircuitsPreprint
Oláh, G., Lákovics, R., Shapira, S., Leibner, Y., Szűcs, A. L., Csajbók, É., Barzó, P., Molnár, G., Segev, I., & Tamás, G. (2024) · eLife · doi:10.7554/elife.93781.1 · Cited by 5
A unifying account of replay as context-driven memory reactivationPreprint
Zhou, Z., Kahana, M. J., & Schapiro, A. C. (2024) · eLife · doi:10.7554/elife.99931.1 · Cited by 5
Noradrenergic neurons of the locus coeruleus are phase-locked to cortical updown states during sleep
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Ripple contributions to human memory: making the spiking content count
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“However, animal studies investigating the underlying spiking activity and spiking sequences have produced many of the most important insights into the role of ripples in memory 22–24, 133, 138 .”
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Brain Rhythms Provide a Framework for Neural Syntax
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Self-citationCoherent noise enables probabilistic sequence replay in spiking neuronal networks
Bouhadjar, Y., Wouters, D. J., Diesmann, M., & Tetzlaff, T. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010989 · Cited by 6
Hippocampal Place Cell Sequences Are Impaired in a Rat Model of Fragile X Syndrome
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“…properties of oscillatory patterns in the LFP, speci fi cally sharp wave-ripples and slow gamma oscillations, that normally co-occur with replay events in WT rats (Kudrimoti et al., 1999; Nádasdy et al., 1999; Lee and Wilson, 2002; Davidson et al., 2009; Carr et al., 2012; Bieri et al., 2014).”
The Role of Sleep in Memory Consolidation: Active or Permissive?
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“Functional brain connectivity between brain regions were estimated by computing the cross-correlation in the frequency domain between EEG signals (Bowyer, 2016; Mohammad-Rezazadeh et al., 2016; Nolte et al., 2004).”
MethodInformation Encoding and Reconstruction by Phase Coding of Spikes
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Self-citationNine insights from internet engineering that help us understand brain network communication
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“In Nádasdy et al. (1999), extracellular tetrode recordings were obtained from hippocampal CA1 pyramidal layer neurons in 18 rats during sleep and conditioned wheel running.”
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“found that the neuron activities in hippocampus during sleep matched those of daytime [2] [3].”
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Self-citationComputational Intelligence in Electrophysiology: Trends and Open Problems
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“Another approach is that of template matching where the rastergram of multi neuronal activity is binned to generate a sequence of population vectors (a N-dimensional vector per time bin) [23, 73].”
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“Asymmetrical connection between pyramidal neurons has been suggested to be a neurophysiological basis in sequence learning and memory in the rat HF (Skaggs et al., 1996; Lisman, 1999; Nadásdy et al., 1999; Sato and Yamaguchi, 2003; Wu and Yamaguchi, 2004).”
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Detection of spike patterns using pattern filtering, with applications to sleep replay in birdsong
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Slow-wave sleep : generation and propagation of slow waves, role in long-term plasticity and gating
Chauvette, S. (2013) · Corpus Université Laval (Université Laval) · Cited by 4
How Adversarial REM Dreams May Facilitate Creativity, and Why We Become Aware of Them
Deperrois, N., Petrovici, M., Jordan, J., Huber, L., & Senn, W. (2024) · Clinical and Translational Neuroscience · doi:10.3390/ctn8020021 · Cited by 3
“For instance, it is difficult to assess whether the improvement of skills after a night of sleep is due to the occurrence of a certain dream, or to other physiological features of sleep such as hippocampal replay during NREM sleep [25].”
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Functional neuroimaging of the reciprocal influences between sleep and wakefulness
Jedidi, Z., Rikir, E., Muto, V., Mascetti, L., Kussé, C., Foret, A., Bourdiec, A. S.-L., Vandewalle, G., & Maquet, P. (2011) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-011-1016-4 · Cited by 3
Synchrony and Precise Timing in Complex Neural Networks
Memmesheimer, R.-M., & Timme, M. (2009) · World Scientific lecture notes in complex systems · doi:10.1142/9789812838803_0013 · Cited by 3
“Despite some interesting studies which might support the synfire chain hypothesis [94, 113, 125, 139], there is no key experiment that directly proves – or disproves – the existence of synfire chain anatomy or dynamics.”
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Mushtaq, M., Haq, R. U., Anwar, M. W., Marshall, L., Bazhenov, M., Zia, K., Alam, H., Hertel, L., Awan, A. A., & Martinetz, T. (2021) · Bioengineered · doi:10.1080/21655979.2021.1936894 · Cited by 3
“Previously stored information reactivation is strongly observed during SPWRs in immobile state, slow-wave sleep [10,11] and rarely during searching [12,13].”
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Cellular Mechanisms of Thalamocortical Oscillations in the Sleeping Brain
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Memory Reactivation in Humans (Imaging Studies)
Farthouat, J., & Peigneux, P. (2014) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-1969-7_11 · Cited by 3
Identification of single neurons in a forebrain network
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Reinforcement Learning and Hippocampal Dynamics
Johnson, A. C., & Venditto, S. J. C. (2014) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-1969-7_14 · Cited by 3
Restricted Boltzmann Machine Models of Hippocampal Coding and Neurogenesis
Finnegan, R., Shaw, M., & Becker, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803784-3.00021-4 · Cited by 5
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0001 · Cited by 4
Self-citationRe-encoding of associations by recurrent plasticity increases memory capacity
Medina, D., & Leibold, C. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00013 · Cited by 3
“Examples for such intrinsic activities are the recurring sequences of neuronal activity patterns in the hippocampus (Wilson and McNaughton, 1994; Nadasdy et al., 1999; Lee and Wilson, 2002; Davidson et al., 2009); see Buhry et al. (2011); Wikenheiser and Redish (2012) for review.”
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Synchronous ensembles of hippocampal CA1 pyramidal neurons during novel explorationPreprint
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A unifying account of replay as context-driven memory reactivationPreprint
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Coherent thalamic inputs organize head direction signal in the medial entorhinal cortexPreprint
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Cortico-hippocampal communication by way of parallel parahippocampal-subicular pathways.
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Self-citationAltered patterning of neural activity in a neuropathology
Hoffman, C., Cheng, J., Morales, R., Ji, D., & Dabaghian, Y. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-08538-6 · Cited by 2
“These brisk, endogenous dynamics may last anywhere from a few dozen to a fewhundred ms, which is comparable to the characteristic interval between spikes triggered by the animal’s physical traversal through place fields 61,73–77 , and thus drive the β P and λ P scores higher (Fig.”
Experimentally inducing dreams of remote emotional memory
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“Notably, this mirrors an analogous temporal gradient in the strength of neural-level memory reactivation, which is also generally strongest in early sleep, immediately following the target experience [44, 45].”
Hippocampo–supramammillary coupling across sleep and wake
Caneo, M., Espinosa, N., Lazcano, G., Aguilera, A., Lara-Vásquez, A., & Fuentealba, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-37066-0 · Cited by 2
Human hippocampal ripples prioritize model-based learning
Zhou, X., Wang, X., Hu, X., Wang, H., Zhang, J., Yu, Q., Xu, J., Xiao, Z., He, L., & Liu, Y. (2026) · Neuron · doi:10.1016/j.neuron.2026.03.020 · Cited by 5
Visual Cognitive Adventures of Single Neurons in the Human Medial Temporal Lobe
Fried, I. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9780262027205.003.0008 · Cited by 2
“These replayed sequences exhibit a high temporal compression and can occur in forward and reverse order (Nadasdy et al., 1999; Foster & Wilson, 2006; Diba & Buzsáki, 2007).”
BackgroundGlobal and local nature of cortical slow waves
Alegre-Cortés, J., Mattia, M., Sáez, M., & Reig, R. (2025) · iScience · doi:10.1016/j.isci.2025.113213 · Cited by 2
“26 The cellular and synaptic mechanisms that control the transition among these states have been widely studied in vivo , in vitro and in silico, as well as the possible roles of these oscillations in memory consolidation through their interaction with thalamic spindles, 27,28 or homeostasis.”
Conditions for replay of neuronal assemblies
Cano, G., & Kempter, R. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013844 · Cited by 2
Hippocampal Sequences and the Cognitive Map
Wikenheiser, A. M., & Redish, A. D. (2014) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-1969-7_5 · Cited by 3
Learning in silicon: A neuromorphic model of the hippocampus
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Coordinated Sequence Replays Between the Visual Cortex and Hippocampus
Haggerty, D. C., & Ji, D. (2014) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-1969-7_9 · Cited by 2
“Consistent with previous results [ 43 – 46 ], we found that the hippocampal neuronal pairs with high correlations during RUN also showed high correlations during POST (Fig.”
ResultLearning with incomplete information in the committee machine
Bergmann, U., Kühn, R., & Stamatescu, I.-O. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0345-2 · Cited by 2
Memory, Sleep, and Dreaming: Experiencing
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Hippocampal–Neocortical Dialog
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Bases biológicas da atividade onírica
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Recording Day and Night: Advice for New Investigators in the Sleep and Memory Field
Souza, A. C., Koike, B. D. V., Pedrosa, R., Tort, A. B. L., Queiroz, C. M., & Ribeiro, S. T. G. (2018) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-812028-6.00002-1 · Cited by 3
Metadata
- Authors
- Nádasdy, Z., Hirase, H., Czurkó, A., Csicsvari, J., & Buzsáki, G.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1999-11-01
- Volume
- 19
- Issue
- 21
- Pages
- 9497-9507
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6782894
- OpenAlex
- W1580901834
- Cited by
- 932
- References
- 65
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 46 · 2023: 44 · 2022: 36 · 2021: 34 · 2020: 43 · 2019: 53 · 2018: 36 · 2017: 46 · 2016: 38 · 2015: 40 · 2014: 51 · 2013: 39 · 2012: 29
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH054671, MH54671)
- National Institute of Neurological Disorders and Stroke (R01NS034994, NS-34994)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Keywords
- replay, slow-wave sleep, pyramidal neurons, template matching, memory consolidation, spatial navigation
- MeSH terms
- Animals, Brain Mapping, Evoked Potentials, Hippocampus, Models, Neurological, Monte Carlo Method, Motor Activity, Nerve Net, Neurons, Probability, Sleep, Time Factors, Wakefulness, Rats, Sprague-Dawley, Pyramidal Cells, Rats
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