Download Björn Rasch, Prof. Dr. rer. nat.

Björn Rasch, Prof. Dr. rer. nat.
Division of Cognitive Biopsychology and Methods, University of Fribourg
Rue P-.A. de Faucigny 2; 1701 Fribourg; Switzerland;
( +41 (0)26 300 76 37 * [email protected]
PERSONAL INFORMATION
Born on the 04.01.1975 in Lüneburg, Germany, married
EDUCATION
2011
Vienia Docendi in Psychology (Habilitation), University of Basel, Switzerland
2008
Doctor of Science (Dr. rer. nat., summa cum laude), University of Trier, Germany
2003
Diploma (equivalent to M.Sc.), Psychology, University of Trier, Germany
PROFESSIONAL APPOINTMENTS
2013 – present
Full professor of Cognitive Biopsychology and Methods at the University of Fribourg, Switzerland
2011 - 2013
SNSF Professor of the Swiss National Science Foundation (SNSF) at the University of Zürich, Switzerland
2008 – 2011
Lecturer and Research Scientist, Division of Cognitive Neuroscience (Prof. De Quervain) and Division of
Molecular Psychology (Prof. Papassotiropoulos), University of Basel, Switzerland
2003 – 2008
Research Scientist, Institute for Neuroendocrinology (Prof. Born), University of Lübeck, Germany
FELLOWSHIPS AND AWARDS
2011
SNSF professorship of the Swiss National Science Foundation (SNSF)
2008
2-year post-doc scholarship, Deutsche Forschungsgemeinschaft (DFG)
2007
Young Scientist Award (Fachgruppe „Biologische Psychologie und Neuropsychologie“ der
Deutschen Gesellschaft für Psychologie (DGPs))
2001
1-year Fulbright scholarship for studying in the U.S.A.
POSITIONS OFFERED
2013
Full Professor for Biological and Clinical Psychology, University of Trier, Germany
2011
Team leader position at the RIKEN Brain Science Institute, Tokyo, Japan (tenure-track)
2011
Assistance professor for "Learning and Plasticity in the old Age" at the University of Zürich, Switzerland
SUPERVISED PHD STUDENTS AND POST DOCS
2011 – present
S. Ackermann, M. Cordi, M. Göldi, G. Gvozdanovic, M. Lehmann, M. Lüthi, M. Munz, J. Rihm,
T. Schreiner
TEACHING ACTIVITIES
2013 – present
Lectures on General Psychology I + II and Research Methods in Psychology, regular seminars
2008 – 2013
Participation in the Master-Modul “Cognitive Psychology and Cognitive Neuroscience”, Univ. of Zurich
Regular seminars for psychology students on Memory, Sleep, and neuroscientific methods (fMRI, EEG)
1
INSTITUTIONAL RESPONSIBILITIES
2014 - present
Vice-president of the department of psychology, University of Fribourg, Switzerland
2014 - present
President of the internal ethical review board of the Departement of Psychology, University of Fribourg
AD HOC REVIEWER
Organizations:
German Research Foundation (DFG), Volkswagenstiftung (D), Swiss National Science Foundation
(SNSF), Alberta Univ. (USA), BBSRC (UK), Netherlands Organisation of Scientific Research etc.
Journals:
Science, Nature Neurosci., Neuron, PNAS, J. Neurosci, Biol. Psychiatry, Current Biology, Biol.
Psychology, Neuroimage, Sleep, PlosOne, Psychoneuroendocrinology, etc.
Editor:
Special Issue Guest Editor for Neurobiology of Learning and Memory, and Brain and Language, Review
Editor for Frontiers in Human Neuroscience,
MEMBERSHIPS
Association for Psychological Sciences, Cognitive Neuroscience Society; Deutsche Gesellschaft für
Psychologie (DGPs); Deutsche Gesellschaft für Psychophysiologie und ihre Anwendungen (DGPA), Swiss
Society of Neuroscience; Swiss Society of Sleep Research, Sleep Medicine and Chronobiology; Milton
Erickson Society for Clinical Hypnosis (MEG), Zurich Centre for interdisciplinary Sleep Research (ZiS)
FUNDING (COMPETETIVE, AS PRINCIPLE INVESTIGATOR)
2014
University of Fribourg
CHF:
62.500,-
2012
Subproject in KFSP “Sleep and Health”
CHF:
525.000.-
2011
SNSF professorship (memory reactivation and sleep) CHF:
1.600.000.-
2011
SNSF project (neural correlates of self-control)
CHF:
268.900.-
2009
German Research Foundation (DFG)
EURO:
370.000.-
2009
Freiwillige Akademische Gesellschaft Basel
CHF:
62.000.-
2008
University of Basel
CHF:
50.000.-
PUBLICATIONS
Total: 50 peer reviewed articles, 12 as first, 19 as last/corresponding author; 2 Books; 2 Book Chapters
Cumulative Impact 343 (ResearchGate); h-Index 18 (Web of Science), > 1400 citations, Average citation per article: 32.4
Five most important publications
Rasch, B., Büchel, C., Gais, S., & Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory
consolidation. Science, 315, 1426-1429.
Rasch, B., Pommer, J., Diekelmann, S., & Born, J. (2008). Pharmacological REM sleep suppression paradoxically
improves rather than impairs skill memory. Nature Neuroscience. 12(4). 396-397.
Diekelmann, S., Büchel, C., Born, J. & Rasch, B. (2011). Labile or stable: opposing consequences for memory when
reactivated during waking and sleep. Nature Neuroscience. 14(3):381-6.
Rasch, B. & Born, J. (2013). About sleep’s role in memory. Physiological Reviews 93:681-766.
Schreiner, T. & Rasch, B. (2014). Boosting Vocabulary Learning by Verbal Cueing During Sleep. Cerebral Cortex
(advanced online publication).
2
List of Publications B. Rasch (Mai 2015)
PEER-REVIEWED ARTICLES
2015
Rihm, J. & Rasch, B. (in press). Replay of conditioned stimuli during late REM and stage N2 sleep influences affective
tone rather than emotional memory strength. Neurobiol. Learn. Mem.
Schreiner, T. & Rasch, B. (in press). Cueing vocabulary during daytime wake has no effect on memory. Somnologie.
Cordi, M.; Hirsiger, S.; Merillat, S. & Rasch, B. (2015). Improving sleep and cognition by hypnotic suggestion in the
elderly. Neuropsychologia. 69:176-82.
Kleim B., Wilhelm FH., Temp I., Margraf J., Wiederhold BK. & Rasch B. (2015). Letter to the Editor: Simply
avoiding reactivating fear memory after exposure therapy may help to consolidate fear extinction memory - a
reply. Psychol Med. 45(4):887-8
2014
Ackermann S., Hartmann F., Papassotiropoulos A., de Quervain D.J., & Rasch B. (2014). No Associations between
Interindividual Differences in Sleep Parameters and Episodic Memory Consolidation. Sleep. (advanced online
publication)
Ackermann S. & Rasch B. (2014). Differential Effects of Non-REM and REM Sleep on Memory Consolidation? Curr
Neurol Neurosci Rep. 14(2):430.
Cordi, C., Schlarb, A. & Rasch, B. Deepening sleep by hypnotic suggestion. Sleep. 37(6):1143-52.
Cordi, M., Ackerman, S., Bes, F.W., Hartmann, F., Konrad, B.N., Genzel, L., Pawlowski, M., Steiger, A., Schulz,
H., Rasch, B., Dresler, M. (2014). Lunar cycle effect on sleep and the file drawer problem. Current Biology
24(12): R549-50.
Cordi MJ., Diekelmann S., Born J., Rasch B. (2014). No effect of odor-induced memory reactivation during REM
sleep on declarative memory stability. Front Syst Neurosci. 8:157
Göder, R. Nissen, C., & Rasch, B. (2014). [Sleep, learning and memory: relevance for psychiatry and psychotherapy.]
Nervenarzt 5(1):50-6.
Helversen, B., Karllson, L, Rasch, B. & Rieskamp, J. (2014)Neural Substrates of Similarity and Rule-based Strategies
in Judgment. Frontiers in Human Neuroscience 8:809.
Kleim B, Wilhelm FH, Temp L, Margraf J, Wiederhold BK & Rasch B. (2014). Sleep enhances exposure therapy.
Psychol. Med. 44(7):1511-9.
Luksys G, Ackermann S, Coynel D, Fastenrath M, Gschwind L, Heck A, Rasch B, Spalek K, Vogler C,
Papassotiropoulos A, de Quervain D. (2014). BAIAP2 Is Related to Emotional Modulation of Human Memory
Strength. PLoS One. 2;9(1):e83707.
Rihm, J., Diekelmann, S., Born, J., & Rasch, B. (2014). Reactivating Memories During Sleep by Odors: OdorSpecificity and Associated Changes in Sleep Oscillations. J Cogn Neurosci. 26(8):1806-18.
Schreiner, T. & Rasch, B. (2014). Boosting Vocabulary Learning by Verbal Cueing During Sleep. Cerebral Cortex
(advanced online publication).
2013
Ackermann S, Hartmann F, Papassotiropoulos A, de Quervain DJ & Rasch B. (2013). Associations between Basal
Cortisol Levels and Memory Retrieval in Healthy Young Individuals. J Cogn Neurosci. 25(11):1896-907.
Ackermann S., Heck A., Rasch B., Papassotiropoulos A., de Quervain DJ. (2013) The BclI polymorphism of the
glucocorticoid receptor gene is associated with emotional memory performance in healthy individuals.
Psychoneuroendocrinology 38(7):1203-7.
Bosch OG, Rihm JS, Scheidegger M, Landolt HP, Stämpfli P, Brakowski J, Esposito F, Rasch B, Seifritz E. (2013)
Sleep deprivation increases dorsal nexus connectivity to the dorsolateral prefrontal cortex in humans. Proc Natl
Acad Sci U S A. 26;110(48):19597-602.
Friese, M., Binder, J., Luechinger, R., Boesiger, P. & Rasch, B. (2013). Exerting self control exhausts the prefrontal
cortex. PlosOne 8(4):e60385.
3
Papassotiropoulos A., Stefanova E., Vogler C., Gschwind L., Ackermann S., Spalek K., Rasch B., Heck A., Aerni A.,
Hanser E., Demougin P., Huynh KD., Luechinger R., Klarhöfer M., Novakovic I., Kostic V., Boesiger P.,
Scheffler K., de Quervain DJ. (2013). A genome-wide survey and functional brain imaging study identify
CTNNBL1 as a memory-related gene. Mol Psychiatry 18(2):264.
Rasch, B. & Born, J. (2013). About sleep’s role in memory. Physiological Reviews 93:681-766.
Wascher E, Rasch B, Sänger J, Hoffmann S, Schneider D, Rinkenauer G, Heuer H, Gutberlet I. (2013). Frontal theta
activity reflects distinct aspects of mental fatigue. Biol Psychol. 2;96C:57-65.
Wilhelm, I., Rose, M., Imhof, K.I., Rasch, B., Buchel, C. & Born, J. (2013). The sleeping child outplays the adult's
capacity to convert implicit into explicit knowledge. Nature Neurosci. 16(4):391-3.
2012
Binder, J., de Quervain, D., Friese, M., Luechinger, R., Boesiger, P., Rasch, B. (2012). Emotion suppression reduces
hippocampal activity during successful memory encoding. Neuroimage 63(1):525-32.
Diekelmann S., Biggel S., Rasch B., Born J. (2012) Offline consolidation of memory varies with time in slow wave
sleep and can be accelerated by cuing memory reactivations. Neurobiol Learn Mem. 98(2):103-11.
Ackermann, S., Spalek, K., Rasch, B., Gschwind, L., Coynel D., Fastenrath, M., Papassotiropoulos, A., de Quervain,
D. (2012). Testosterone levels in healthy men are related to amygdala reactivity and memory performance.
Psychoneuroendocrinolgy 37(9):1417-24.
De Quervain, D., Kolassa, T., Ackermann, S., Aerni, A., Boesiger, P., Demougin, P., Elbert, T., Ertl, V., Gschwind,
L., Hadziselimovice, N., Hanser, E., Heck, A., Hieber, P., Huynh, P., Klarhöfer, M.,Luechinger, R., Rasch, B.,
Scheffler, K., Spalek, K., Stippich, C., Vogler, C., Vukojevice, V., Stetak, A. & Papassotiropoulos, P. PKC is
genetically linked to memory capacity in nontraumatized individuals and to traumatic memory and PTSD in
genocide survivors. Proc.Natl.Acad.Sci.U.S.A . 109(22):8746-51.
2011
Rasch, B., Dodt, C., Sayk, F., Mölle, M. & Born, J. (2011). No elevated plasma catecholamine levels during sleep in
newly diagnosed, untreated hypertensives. PlosOne 6(6):e21292
Diekelmann, S., Büchel, C., Born, J. & Rasch, B. (2011). Labile or stable: opposing consequences for memory when
reactivated during waking and sleep. Nature Neuroscience. 14(3):381-6.
Gais, S., Rasch, B., Dahmen, J.C., Sara, S., Born, J. (2011). The Memory Function of Noradrenergic Activity in NonREM Sleep. J.Cogn Neurosci., 23(9):2582-92.
Heck A, Vogler C, Gschwind L, Ackermann S, Auschra B, Spalek K, Rasch B, de Quervain D, Papassotiropoulos A
(2011). Statistical epistasis and functional brain imaging support a role of voltage-gated potassium channels in
human memory. PLoS One. 6(12):e29337.
2010
Rasch, B., Spalek, K., Buholzer, S., Luechinger, R., Boesiger, P., de Quervain, D.J.-F. & Papassotiropoulos, A.
(2010). Aversive stimuli lead to differential amygdala activation and connectivity patterns depending on CatecholO-Methyltransferase Val158Met genotype. Neuroimage. 52(4):1712-9.
Rasch, B., Papassotiropoulos, A. & de Quervain, D. (2010). Imaging genetics of cognitive functions: Focus on episodic
memory. Neuroimage. 53(3), 870-7.
Hallschmid, M., Jauch-Chara, K., Korn, O., Mölle, M., Rasch, B., Born, J., Schultes, B. & Kern, W. (2010).
Euglycemic infusion of insulin detemir compared to human insulin appears to increase direct current brain
potential response and reduces food intake while inducing similar systemic effects. Diabetes. 9, 1101-7.
2009
Rasch, B., Spalek, K., Buholzer, S., Luechinger, R., Boesiger, P., Papassotiropoulos, A., de Quervain, D. (2009). A
genetic variation of the noradrenergic system is related to differential amygdala activation during encoding of
emotional memories. Proc.Natl.Acad.Sci.U.S.A . 106(45). 19191-6.
Rasch, B., Gais, S. & Born, J. (2009). Impaired off-line consolidation of motor memories after combined blockade of
cholinergic receptors during REM sleep-rich sleep. Neuropsychopharmacology. 34(7), 1843-63.
Bly, B.M., Carrion, R.E. & Rasch, B. (2009). Domain-specific learning of grammatical structure in musical and
phonological sequences. Mem Cognit., 1, 10-20.
2008
4
Rasch, B., Pommer, J., Diekelmann, S., & Born, J. (2008). Pharmacological REM sleep suppression paradoxically
improves rather than impairs skill memory. Nature Neuroscience. 12(4). 396-397.
Rasch, B. & Born, J. (2008). Reactivation and Consolidation of Memory During Sleep. Current Directions in
Psychological Science, 17(3), 188-192.
Gais, S., Rasch, B., Wagner, U., & Born, J. (2008). Visual-procedural memory consolidation during sleep blocked by
glutamatergic receptor antagonists. J Neurosci., 28, 5513-8.
2007
Rasch, B., Büchel, C., Gais, S., & Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory
consolidation. Science, 315, 1426-1429.
Rasch, B., Dodt, C., Mölle, M., & Born, J. (2007). Sleep-stage-specific regulation of plasma catecholamine
concentration. Psychoneuroendocrinology, 32(8-10), 884-891.
Rasch, B. & Born, J. (2007). Maintaining Memories by Reactivation. Current Opinion in Neurobiol. 17(6), 698-703
Perras, B., Berkemeier, E., Rasch, B., Fehm, H. L., & Born, J. (2007). PreproTRH((158-183)) fails to affect pituitaryadrenal response to CRH/vasopressin in man: A pilot study. Neuropeptides, 41, 233-238.
2006
Born, J., Rasch, B., & Gais, S. (2006). Sleep to remember. Neuroscientist, 12, 410-424.
Rasch, B., Born, J., & Gais, S. (2006). Combined blockade of cholinergic receptors shifts the brain from stimulus
encoding to memory consolidation. J.Cogn Neurosci., 18, 793-802.
Krug, R., Born, J., & Rasch, B. (2006). A 3-day estrogen treatment improves prefrontal cortex-dependent cognitive
function in postmenopausal women. Psychoneuroendocrinology, 31, 965-975.
Wagner, U., Hallschmid, M., Rasch, B., & Born, J. (2006). Brief sleep after learning keeps emotional memories alive
for years. Biol Psychiatry, 60, 788-790.
Kozhevnikov, M., Motes, M. A., Rasch, B., Blajenkova, O. (2006). Perspective-Taking vs. Mental Rotation
Transformations and How They Predict Spatial Navigation Performance. Applied Cognitive Psychology, 20(3),
397-417.
2002
Levinson, S.C., Kita, S., Haun, D.B. & Rasch, B. (2002). Returning the tables: language affects spatial reasoning.
Cognition. 84(2):155-88.
EDITORIALS / BOOKS / CHAPTERS
Rasch, B. & Born, J. (2015). In search of a role of REM sleep in memory formation. Neurobiol. Learn. Mem.
Rasch, B., Friese, M., Hofmann, W.J. & Naumann, E. (2010): Quantitative Methoden, Band I, 3. Auflage.
Heidelberg: Springer Verlag.
Rasch, B., Friese, M., Hofmann, W.J. & Naumann, E. (2010): Quantitative Methoden, Band II, 3. Auflage.
Heidelberg: Springer Verlag.
Born, J. & Rasch, B. (2005). Psychologie des Schlafs. In: Schulz, H. (Ed.), Kompendium für Schlafmedizin (Kap. II,
9.1 - 9.3). Landsberg/Lech : ecomed.
DISSERTATION
Rasch, B. (2008). Odor-induced memory reactivations during human sleep. University of Trier.
http://ubt.opus.hbz-nrw.de/volltexte/2008/478/
5
Neuron
Perspective
Sleep and the Price of Plasticity:
From Synaptic and Cellular Homeostasis
to Memory Consolidation and Integration
Giulio Tononi1,* and Chiara Cirelli1,*
1Department of Psychiatry, University of Wisconsin, Madison, WI 53719, USA
*Correspondence: [email protected] (G.T.), [email protected] (C.C.)
http://dx.doi.org/10.1016/j.neuron.2013.12.025
Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need
to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis
(SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning
statistical regularities about the current environment requires strengthening connections throughout
the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores
cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of
sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This
Perspective considers the rationale and evidence for SHY and points to open issues related to sleep
and plasticity.
Why we need to sleep seems clear: without sleep, we become
tired, irritable, and our brain functions less well. After a good
night of sleep, brain and body feel refreshed and we are
restored to normal function. However, what exactly is being
restored by sleep has proven harder to explain. Sleep
occupies a large fraction of the day, it occurs from early
development to old age, and it is present in all species
carefully studied so far, from fruit flies to humans. Its hallmark
is a reversible disconnection from the environment, usually
accompanied by immobility. The risks inherent in forgoing
vigilance, and the opportunity costs of not engaging in more
productive behaviors, suggest that allowing the brain to go
periodically ‘‘offline’’ must serve some important function.
Here we review a proposal concerning what this function
might be—the synaptic homeostasis hypothesis or SHY
(Tononi and Cirelli, 2003, 2006). SHY proposes that the
fundamental function of sleep is the restoration of synaptic
homeostasis, which is challenged by synaptic strengthening
triggered by learning during wake and by synaptogenesis
during development (Figure 1). In other words, sleep is ‘‘the
price we pay for plasticity.’’ Increased synaptic strength has
various costs at the cellular and systems level including higher
energy consumption, greater demand for the delivery of
cellular supplies to synapses leading to cellular stress, and
associated changes in support cells such as glia. Increased
synaptic strength also reduces the selectivity of neuronal responses and saturates the ability to learn. By renormalizing
synaptic strength, sleep reduces the burden of plasticity on
neurons and other cells while restoring neuronal selectivity
and the ability to learn, and in doing so enhances signal-tonoise ratios (S/Ns), leading to the consolidation and integration
of memories.
12 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
Synaptic Homeostasis and Sleep Function
Neurobiological and Informational Constraints
SHY was initially motivated by considering neurobiological
and informational constraints faced by neurons in the wake
state, as outlined in the following section.
Neurons Should Fire Sparsely and Selectively. Energetically, a
neuron is faced with a major constraint: firing is more expensive
than not firing and firing strongly (bursting) is especially expensive (Attwell and Gibb, 2005). Informationally, a neuron is a tight
bottleneck: it can receive a very large number of different input
patterns over thousands of synapses, but through its single
axon it produces only a few different outputs. Simplifying a
bit, a neuron’s dilemma is ‘‘to fire or not to fire’’ or ‘‘to burst or
not to burst.’’ Together, these energetic and informational constraints force neurons to fire sparsely and selectively: bursting
only in response to a small subset of inputs while remaining silent
or only firing sporadic spikes in response to a majority of other
inputs (Balduzzi and Tononi, 2013). In line with this requirement
and with theoretical predictions (Barlow, 1985), firing rates are
very low under natural conditions (Haider et al., 2013) and responses to stimuli are sparse, especially in the cerebral cortex
(Barth and Poulet, 2012).
Neurons Should Detect and Communicate Suspicious Coincidences. Since a neuron must fire sparsely, it should choose well
when to do so. A classic idea is that a neuron should fire for ‘‘suspicious coincidences’’—when inputs occur together more
frequently than would be expected by chance (Barlow, 1985).
Suspicious coincidences suggest regularities in the input and
ultimately in the environment, such as the presence and persistence in time of objects, which a neuron should learn to predict.
Importantly, due to sparse firing, excess coincidences of firing
are easier to detect than coincidences of silence (Hashmi
Neuron
Perspective
Figure 1. The Synaptic Homeostasis
Hypothesis
et al., 2013). Thus, a neuron should integrate across its many inputs to best detect suspicious coincidences of firing. Moreover,
it should communicate their detection by firing in response,
assuming that other neurons will also pay attention to firing. A
good strategy to reliably communicate to other neurons would
therefore be to fire most (burst) for the most suspicious coincidences, less so for less suspicious ones, and not at all for all
other inputs. Finally, in order to fire when it detects suspicious
coincidences, a neuron should make sure that the synapses carrying them are strong.
Neurons Should Strengthen Synapses in Wake, When Interacting with the Environment. A neuron cannot allocate high synaptic
strength to input lines carrying suspicious coincidences once
and for all: neurons must remain plastic and appropriately increase synaptic strength to become selective for novel suspicious coincidences and ensure that they can percolate through
the brain. Clearly, this should happen in wake, and especially
when organsims explore their environment and interact with it,
encounter novel situations, and pay attention to salient events.
There are a variety of plasticity mechanisms that can promote
some form of synaptic potentiation during wake and that are
known to occur during exploration (Clem and Barth, 2006), association learning (Gruart et al., 2006), contextual memory formation (Hu et al., 2007), fear conditioning (Matsuo et al., 2008;
Rumpel et al., 2005), visual perceptual learning (Sale et al.,
2011), cue-reward learning (Tye et al., 2008), and avoidance
learning (Whitlock et al., 2006). While there are also forms of
learning ‘‘by depression,’’ including reversal learning in the hippocampus (e.g., Dong et al., 2013), some aspects of fear extinc-
tion in the amygdala (reviewed in Quirk
et al., 2010), and familiarity recognition
in perirhinal cortex (e.g., Cho et al.,
2000), enduring synaptic depression is
associated more with forgetting what
was previously known than with acquiring
new knowledge (Collingridge et al., 2010).
While potentiating synapses in wake
when the organism is interacting with
the environment is essential, doing so in
sleep, when neural activity is disconnected from the environment and the
brain is exposed to its own ‘‘fantasies,’’
may instead be maladaptive. For
example, more than half of nocturnal
awakenings reveal the occurrence of
imaginary scenes or full-fledged dreams,
so it could be dangerous if they gave
rise to new declarative memories (Nir
and Tononi, 2010). Similarly, nondeclarative skills are acquired and refined with
environmental feedback in wake, but if
new learning occurred during sleep
without such feedback, these skills could
easily become corrupted. Indeed, the strengthening of fantasies
is a known problem in neural networks that learn based on a
wake-sleep algorithm in which feedforward (‘‘recognition’’) connections that match feedback (‘‘generative’’) connections are
potentiated in the sleep phase (Hinton et al., 1995).
Neurons Should Renormalize Synapses in Sleep, When They
Can Sample Memories Comprehensively. While neurons should
learn primarily by potentiating synapses in wake, synaptic
strength is a costly resource. One set of reasons is cell biological:
stronger synapses consume more energy, require extra
supplies, and lead to cellular stress (see below). Another reason
is informational and can be termed the plasticity-selectivity
dilemma: when a neuron strengthens additional input lines, a
broader distribution of its input patterns can make it burst,
reducing its ability to capture suspicious coincidences because
it will also begin to fire for chance, spurious coincidences
(Balduzzi and Tononi, 2013; Hashmi et al., 2013). Clearly, as
recognized in many models of learning, neurons must eventually
renormalize total synaptic strength in order to restore cellular
functions as well as selectivity. SHY proposes that renormalization through synaptic depression should happen during sleep.
This is because, when the brain goes offline in sleep, the continuously changing patterns of spontaneous activity allows neurons
to obtain a ‘‘comprehensive’’ sampling of the brain’s overall
knowledge of the environment (Figure 2, bottom)—one acquired
over evolution, development, and a lifetime of learning (Tononi
et al., 1996). During a period of wake, instead, an organism is
faced with the ‘‘current’’ sampling of the environment that is
necessarily limited and biased. For example, consider spending
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 13
Neuron
Perspective
Figure 2. SHY, Wake/Sleep Cycles, and the
Plasticity-Stability Dilemma
Top: during wake the brain interacts with the
environment (grand loop) and samples a limited
number of inputs dictated by current events
(current sampling, here represented by a new acquaintance). High levels of neuromodulators, such
as noradrenaline released by the locus coeruleus
(LC), ensure that suspicious coincidences related
to the current sampling percolate through the brain
and lead to synaptic potentiation. Bottom: during
sleep, when the brain is disconnected from the
environment on both the sensory and motor sides,
spontaneous activity permits a comprehensive
sampling of the brain’s knowledge of the environment, including old memories about people,
places, etc. Low levels of neuromodulators, combined with the synchronous, ON and OFF firing
pattern of many neurons during NREM sleep
events such as slow waves, spindles, and sharpwave ripples, are conducive to synaptic downselection: synapses belonging to the fittest
circuits, those that were strengthened repeatedly
during wake and/or are better integrated with older
memories, are protected and survive. By contrast,
synapses belonging to circuits that were only
rarely activated during wake and/or fit less well
with old memories, are progressively depressed
and eventually eliminated over many wake/sleep
cycles. The green lines in the sleeping brain (right),
taken from Murphy et al. (2009), illustrate the
propagation of slow waves during NREM sleep, as
established using high-density EEG and source
modeling.
a day with a new acquaintance (Figure 2, top). By the evening,
neurons in various brain areas will have learned to recognize
the person’s face, voice, posture, clothes, and many other
aspects by strengthening incoming synapses. But it would not
be a good idea if, to renormalize total synaptic strength, synapses underutilized during that particular waking day were to be
weakened and possibly eliminated—otherwise one would
remember the new acquaintance and forget old friends, a problem known as the plasticity-stability dilemma (Abraham and
Robins, 2005; Grossberg, 1987).
In summary, SHY claims that neurons should achieve some
basic goals with respect to plasticity. (1) New learning should
happen primarily by synaptic potentiation. In this way, firing
that signals suspicious coincidences can percolate throughout
the brain. (2) Synaptic potentiation should occur primarily in
wake, when the organism interacts with its environment, not in
sleep when it is disconnected. In this way, what the organism
learns is controlled by reality and not by fantasy. (3) Renormalization of synaptic strength should happen primarily during sleep,
when the brain is spontaneously active offline, not in wake when
a neuron’s inputs are biased by a particular situation. In this way,
neurons can sample comprehensively the brain’s overall statistical knowledge of its environment.
Heuristic Rules for Neuronal Plasticity in Wake and
Sleep
Learning by Potentiation in Wake. The actual plasticity mechanisms employed by specific neuronal populations are bound to
be complex, variable, and adaptable to local conditions and
firing patterns (Feldman, 2009). However, learning and communicating downstream important events that occur during wake
14 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
can in principle be achieved using a few heuristic rules (Nere
et al., 2012). First, a neuron should pay attention to inputs that
fire strongly, because they could signal the detection of suspicious coincidences by upstream neurons. Furthermore, a strong
input that persists over time could signal the presence of something (like an object) that remains present longer than expected
by chance. Positive correlations between pre- and postsynaptic
spikes, whether over pairs of spikes (spike-timing-dependent
plasticity [STDP]) or as an average, signal that the neuron must
have detected enough suspicious coincidences, by integrating
over its dendritic tree, to make it fire strongly within a restricted
time frame (tens to hundreds of milliseconds), so they should
be rewarded by increasing synaptic strength. Suspicious coincidences in input firing that occur over a restricted dendritic
domain may be especially important (Legenstein and Maass,
2011; Winnubst and Lohmann, 2012), particularly if they involve
both feedforward and feedback inputs. Such coincidences
suggest the closure of a loop between input and output in which
the neuron may have played a causal role (Hashmi et al., 2013).
They also suggest that the feedforward suspicious coincidences
the neuron has captured, presumably originating in the environment, can be matched internally, within the same dendritic
domain, by feedback coincidences generated higher up in the
brain, indicating that bottom-up data fit at least in part with
top-down expectations. This is a sign that the brain can model
internally what it captures externally and vice versa—a good
recipe for increasing the matching between its causal structure
and that of the environment (Hinton et al., 1995; Tononi, 2012).
Finally, in this scheme, a neuron should enable the strengthening
of connections only when it is awake and engaged in situations
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Perspective
worth remembering. This can be signaled globally by neuromodulatory systems that gate plasticity and are active during
wake, especially during salient, unexpected, or rewarding circumstances.
Renormalization by Down-Selection in Sleep. Various synaptic
rules enforcing activity-dependent depression during sleep are
compatible with the renormalization process predicted by
SHY. In all cases, the end result is a competitive ‘‘down-selection’’ whereby after sleep, some synapses become less effective
than others. Computer implementations of down-selection
include: a downscaling rule where all synapses decrease in
strength proportionally, but those that end up below a minimal
threshold become virtually ineffective (Hill et al., 2008); a modified STDP rule by which stronger synapses are depressed less
than weaker ones (Olcese et al., 2010); and a ‘‘protection from
depression’’ rule (Hashmi et al., 2013; Nere et al., 2013). In this
last implementation of down-selection, when a neuron detects
many suspicious coincidences during sleep (thus fires strongly),
rather than potentiating the associated synapses as in the awake
state, it protects them from depression (Figure 2). This competitive down-selection mechanism has the advantage that synapses activated strongly and consistently during sleep survive
mostly unchanged and may actually consolidate, in the classic
sense of becoming more resistant to interference and decay.
By contrast, synapses that are comparatively less activated
are depressed, resulting in the consolidated synapses being
stronger in relative terms. Thus, down-selection ensures the
survival of those circuits that are ‘‘fittest,’’ because they were
strengthened repeatedly during wake or better integrated with
older memories, whereas synapses that were only occasionally
strengthened during wake, or fit less well with old memories,
are depressed and eventually eliminated. The simulations also
show that down-selection during sleep increases S/N and
promotes memory consolidation, gist extraction, and the integration of new memories with established knowledge, while
ensuring that no new memories are formed in the absence of
reality checks (Nere et al., 2013). Finally, it should be noted
that in the special case of a neuron that received all its inputs
from the same source (or from strongly correlated sources),
down-selection would be ineffective because it could not
enforce any competition among synapses. Neurons ‘‘taken
over’’ by a particular source might be relevant for memories
that are extremely stable, such as traumatic ones.
A few cellular mechanisms could explain why during sleep
strongly activated synapses could depress less or not at all.
For instance, high calcium levels can partially or totally block
calcineurin, a phosphatase that promotes synaptic depression
and whose expression is upregulated in sleep (Cirelli et al.,
2004). Another potential mechanism involves the endogenous
inhibitor of CamKII (CamKIIN), which decreases synaptic
strength by directly impairing the binding of CaMKII to the
NMDA receptor (Sanhueza and Lisman, 2013). The alpha isoform
of CaMKIIN is upregulated during sleep (Cirelli et al., 2004), and
its inhibitory function is reduced by high calcium levels (Gouet
et al., 2012). Alternatively Arc/Arg3.1, an activity-induced immediate-early gene that enters spines and mediates receptor internalization (Bramham et al., 2010; Okuno et al., 2012), may be
excluded from the spines that need to be protected, while synap-
ses that are activated in isolation are not protected and depress
progressively in the course of sleep. In sleep, the switch to a
mode of plasticity where synaptic potentiation is prevented
and synapses can at most be protected or depressed in an
activity-dependent manner may be signaled globally by a drop
in the level of neuromodulators, such as noradrenaline, histamine, and serotonin, that are high in wake and low in sleep.
Indeed, the radically altered balance of neuromodulators and
trophins such as brain-derived neurotrophic factor (BDNF) during sleep can reverse the sign of plastic changes compared to
wake, blocking potentiation and promoting depression (Aicardi
et al., 2004; Harley, 1991; Seol et al., 2007).
The schematic scenario described above is indicative of the
general principles that would allow neurons to learn suspicious
coincidences during wake and renormalize synaptic strength
during sleep. Nevertheless, given the variety and complexity of
plasticity mechanisms, the specific synaptic rules followed
by neurons in order to learn during wake and to renormalize
synapses during sleep are likely to differ in different species,
brain structures, neuronal types, and developmental times
(Tononi and Cirelli, 2012). For instance, it is unclear whether
inhibitory connections also need to be renormalized after
wake. It is also unknown whether invertebrates, such as the fruit
fly, or ancient brain structures, such as the brainstem, use the
same mechanisms of renormalization as the vertebrate cortex
or may not even require activity and oscillations in membrane
potentials. Moreover, while SHY unambiguously predicts that
wake should result in a net increase in synaptic strength and
sleep in a net decrease, it does not rule out that some synaptic
depression may also occur in wake and some potentiation in
sleep.
Sleep and Synaptic Homeostasis: The Evidence
In view of the multiplicity of mechanisms of synaptic potentiation,
depression, metaplasticity, homeostatic plasticity, and intrinsic
plasticity, it is natural to assume that neurons have many ways
to keep overall synaptic strength balanced (Kubota et al.,
2009). However, for the reasons outlined above, SHY claims
that such a balance is best achieved through an alternation of
net synaptic potentiation in wake and net depression in sleep.
Over the past few years, the core claim of SHY has been investigated using molecular, electrophysiological, and structural
approaches (Figure 3) that will be discussed in the following
section.
Molecular Evidence. The trafficking of GluA1-containing
AMPA receptors (AMPARs) in and out of the synaptic membrane
is considered a primary mechanism for the occurrence of
synaptic potentiation and depression, respectively (Kessels
and Malinow, 2009). GluA1-containing AMPARs are permeable
to calcium and their expression shows a supralinear relationship
with the area of the postsynaptic density (Shinohara and Hirase,
2009), making them especially powerful in affecting synaptic
strength. Levels of GluA1-containing AMPARs are 30%–40%
higher after wakefulness than after sleep in rats (Vyazovskiy
et al., 2008) and phosphorylation changes of AMPARs, and
of the enzymes CamKII and GSK3b, are also consistent with
net synaptic potentiation during wake and depression during
sleep (Vyazovskiy et al., 2008) (Figure 3A). Similar sleep/wake
changes in AMPAR expression have been found in other studies,
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 15
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Figure 3. Evidence Supporting SHY
(A) Experiments in rats and mice show that the number and
phosphorylation levels of GluA1-AMPARs increase after
wake (data from rats are from Vyazovskiy et al., 2008).
(B, B0 , and B00 ) Electrophysiological analysis of cortical
evoked responses using electrical stimulation (in rats, from
Vyazovskiy et al., 2008) and TMS (in humans, from Huber
et al., 2013) shows increased slope after wake and
decreased slope after sleep. In (B), W0 and W1 indicate
onset and end of 4 hr of wake; S0 and S1 indicate onset
and end of 4 hr of sleep, including at least 2 hr of NREM
sleep. In (B0 ), pink and blue bars indicate a night of sleep
deprivation and a night of recovery sleep, respectively. (B00 )
In vitro analysis of mEPSCs in rats and mice shows
increased frequency and amplitude of mEPSCs after wake
and sleep deprivation (SD) relative to sleep (control). Data
from rats are from Liu et al. (2010).
(C and C0 ) In flies, the number of spines and dendritic
branches in the visual neuron VS1 increase after enriched
wake (ew) and decrease only if flies are allowed to sleep
(from Bushey et al., 2011). (C0 ) Structural studies in
adolescent mice show a net increase in cortical spine
density after wake and sleep deprivation (SD) and a net
decrease after sleep (from Maret et al., 2011).
16 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
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Perspective
for example, the insertion of GluA1-containing AMPAR during
wake (Qin et al., 2005) and their removal during sleep (Lanté
et al., 2011), as well as increases and decreases in a molecular
hallmark of synaptic depression, dephosphorylation of GluA1containing AMPARs at Ser845 (Kessels and Malinow, 2009),
with time spent in sleep and wake respectively (Hinard et al.,
2012).
Electrophysiological Evidence. The slope of the early (monosynaptic) response evoked by electrical stimulation delivered
in vivo is a classical measure of synaptic strength. In rat frontal
cortex, the first negative component of the response evoked
by transcallosal stimulation increases with time spent awake
and decreases with time spent asleep, and the sleep-related
decline correlates with the extent of the decline in slow-wave
activity (Vyazovskiy et al., 2008) (Figure 3B). The slope of the
response evoked in the rat hippocampal CA3 region by electrical
stimulation of the fimbria also declines in the sleep period
following a wake episode (Lubenov and Siapas, 2008). Similarly,
in humans, the slope of the early response evoked in frontal
cortex by transcranial magnetic stimulation (TMS) increases
progressively in the course of 18 hr of continuous wake and
returns to baseline levels after one night of recovery sleep (Huber
et al., 2013) (Figure 3B0 ). These changes in the slope of evoked
responses occurred after several hours of sleep or wake with
the subjects fully awake when postsleep responses were recorded. By contrast, a recent study in head-restrained cats
saw an increase in the cortical response evoked by medial
lemniscus stimulation after sleep (Chauvette et al., 2012).
Notably, the effect was observed after as little as 10 min of sleep
and saturated after two short sleep episodes. While speciesspecific differences may exist, electroencephalogram (EEG)
and intracellular recordings in the report suggest that the membrane potential in the ‘‘awake’’ condition immediately postsleep
was hyperpolarized, implying that the enhanced responses were
most likely due to sleep inertia.
Other experiments measure amplitude and frequency of
miniature excitatory postsynaptic currents (mEPSCs) from slices
of frontal cortex (Figure 3B00 ). Changes in mEPSC frequency
reflect modifications of the presynaptic component of synaptic
transmission, while amplitude changes indicate alterations
in the postsynaptic component. In the cerebral cortex of mice
and rats, both parameters are lower after a few hours of sleep,
higher after a few hours of wake, and decline during recovery
sleep following sleep deprivation (Liu et al., 2010). This suggests
that synaptic efficacy varies between sleep and wake because
of changes at the postsynaptic level, as already indicated
by changes in AMPAR expression (Vyazovskiy et al., 2008),
as well as at the presynaptic level. Consistent with these
findings, the mean firing rates of cortical neurons increase
after prolonged wake (Vyazovskiy et al., 2009), and levels of
glutamate in the rat cortical extrasynaptic space rise progressively during wake and decrease during NREM sleep (Dash
et al., 2009). A study that tested excitatory synapses on hypocretin/orexin neurons of the hypothalamus also found an
increase in both frequency and amplitude of mEPSCs after sleep
deprivation (Rao et al., 2007), suggesting that changes in synaptic efficacy due to sleep/wake may not be restricted to cortical
areas.
Structural Evidence. Structural correlates of synaptic strength
also support SHY. In Drosophila, protein levels of pre- and postsynaptic components are high after wake and decline in the
course of sleep (Gilestro et al., 2009). Moreover, the number or
size of synapses in four different neural circuits increases after
a few hours of wake and decreases only if flies are allowed to
sleep (Bushey et al., 2011; Donlea et al., 2009, 2011). For
instance, in the first giant tangential neuron in the visual system,
the number of dendritic spines increases after 12 hr of wake
spent in an enriched environment and returns to pre-enrichment
levels only if the flies are allowed to sleep (Bushey et al., 2011)
(Figure 3C). In mammals, structural synaptic changes due to
sleep and wake have been studied by repeated two-photon
microscopy in transgenic YFP-H mice. With only a few apical
dendrites of layer V pyramidal neurons expressing yellow
fluorescent protein, spines were counted twice within 12–
16 hr, after a period spent mostly asleep or mostly awake (Maret
et al., 2011) (Figure 3C0 ). In adolescent 1-month-old mice, spines
form and disappear at all times, but spine gain prevails during
wake, resulting in a net increase in spine density, while spine
loss is larger during sleep, resulting in a net spine decrease
(Maret et al., 2011). Another study using younger YFP-H mice
(3 weeks old) also found greater formation of spines and filopodia (possible precursors of mature spines) during the dark
period, when mice are mostly awake, and more elimination of
these protrusions during the light period, when mice are mostly
asleep (Yang and Gan, 2012). These findings confirm that, in
young mice, a few hours of sleep and wake can affect the density
of cortical synapses. By contrast, spine turnover is limited and is
not impacted by sleep and wake in adult mice (Maret et al.,
2011), suggesting that after adolescence synaptic homeostasis
may be mediated primarily by changes in synaptic strength
rather than number.
While the cellular, electrophysiological, and structural
evidence discussed above largely support SHY, it is important
to bear in mind the limitations of these markers. Changes in
evoked responses or firing rates may also be explained by fast
changes in neuronal excitability due to neuromodulators such
as norepinephrine, although synaptic strength and neuronal
excitability are usually coregulated in the same direction
(Cohen-Matsliah et al., 2010; Kim and Linden, 2007). Moreover,
in vitro changes in mEPSCs may not reflect what happens in vivo,
structural changes of synapses do not always reflect changes in
efficacy, and changes in the number and/or phosphorylation
levels of AMPARs may not fully capture their functional status.
Thus, more refined approaches, such as Cre-dependent tagging
of activated circuits, will be needed to establish precisely which
synapses strengthen and weaken during and after a specific
learning task, and whether they mostly do so in wake and in
sleep, respectively. Finally, in most of the studies highlighted,
increases in synaptic strength after wake and their renormalization after sleep occurred in the absence of specific training
paradigms, merely requiring that the experimental subjects
stay awake. Regardless, it should be kept in mind that, even
without any explicit instruction to learn, at the end of a typical
waking day, we can recollect an extraordinary amount of events,
facts, and scenes, including many irrelevant details (Brady et al.,
2008; Standing, 1973).
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 17
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Figure 4. SHY and Slow-Wave Activity
(A) Slow-wave activity (SWA), a quantitative measure of the number and amplitude of slow waves (left), is high in NREM sleep and low in REM sleep and wake
(middle). SWA increases with time spent awake and decreases during sleep, thus reflecting sleep pressure (right).
(B) In rats kept awake for 6 hr by exposure to novel objects, longer times spent exploring result in greater cortical induction of BDNF during wake, as well as in
larger subsequent increases in SWA at sleep onset (from Huber et al., 2007b).
(legend continued on next page)
18 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
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Perspective
Synaptic Homeostasis and Slow-Wave Activity
In mammals and birds, a reliable marker of sleep need is the
amount of slow-wave activity (SWA, 0.5–4.5 Hz) in the EEG of
NREM sleep. As shown by many experimental and modeling
studies, SWA is highest at sleep onset, decreases with the
time spent asleep, increases further if one stays awake longer,
and is reduced by naps (Figure 4A). SWA occurs when, due to
changes in neuromodulation in NREM sleep, cortical neurons
become bistable and undergo a slow oscillation (<1 Hz) in
membrane potential (Steriade et al., 2001). This consists of a depolarized up state, when neurons show sustained firing, and a
hyperpolarized down state, characterized by neuronal silence,
which corresponds to the negative downstroke of EEG slow
waves. Computer simulations show that, for a given level of neuromodulatory and inhibitory tone, the amplitude and slope of
EEG slow waves are related to the number of neurons that enter
an up state or a down state near synchronously. In turn,
synchrony is directly related to the number, strength, and distribution of synaptic connections among them (Esser et al., 2007;
Olcese et al., 2010).
SWA as an Index of Synaptic Homeostasis. An important
corollary of SHY is that, to the extent that SWA reflect the
homeostatic regulation of sleep need, it should reflect changes
in synaptic strength. Work in humans and rodents is consistent
with these predictions. For example, the increase in the slope
of cortical evoked field potentials (an electrophysiological sign
of increased synaptic strength) after a period of wakefulness
correlates with SWA values at the onset of the following sleep
period (Vyazovskiy et al., 2008). Furthermore, rats exposed to
an enriched environment experience a diffuse induction of
BDNF (a marker of synaptic potentiation) and show a widespread increase in SWA during subsequent sleep, which is positively correlated with the amount of the time spent exploring and
with the cortical induction of BDNF (Huber et al., 2007b)
(Figure 4B). By contrast, the increase in sleep SWA after wake
is dampened following noradrenergic lesions, which reduce
levels of BDNF, Arc, and other markers of plasticity (Cirelli
et al., 1996; Cirelli and Tononi, 2000) (Figure 4C).
The link between plasticity, SWA, and sleep is also seen
locally. In rats, SWA increases locally both after learning a
task involving motor cortex (Hanlon et al., 2009) and after
locally infusing BDNF to induce synaptic potentiation (Faraguna
et al., 2008). In humans, learning tasks that involve particular
regions of cortex, i.e., right parietal cortex (Perfetti et al.,
2011), leads to a local increase in sleep SWA and correlates
with postsleep performance improvement (Figure 4D; (Huber
et al., 2004; see also Kattler et al., 1994; Landsness et al.,
2009). Similarly, visual perceptual learning, which depends on
a restricted population of orientation-selective neurons in lateral
occipital cortex, increases the number of slow waves initiated in
these areas (Mascetti et al., 2013b). High-frequency TMS over
motor cortex also leads to a local increase in the amplitude of
EEG responses, indicative of potentiation of premotor circuits.
The magnitude of this potentiation in wake predicts the local increase in SWA during the subsequent sleep episode (Huber
et al., 2007a). By contrast, arm immobilization leads to motor
performance deterioration with a decrease in somatosensory
and motor-evoked responses over contralateral sensorimotor
cortex (indicative of local synaptic depression) and a decrease
in sleep SWA over the same cortical area (Huber et al., 2006)
(Figure 4E). Sustained increases or decreases of cortical excitability induced by a paired associative stimulation protocol also
result in local SWA increases and decreases, respectively
(Huber et al., 2008), although some studies employing slightly
different protocols failed to detect local changes in SWA (for
details, see Hanlon et al., 2011). Overall, these results support
the idea that sleep may be regulated locally (Krueger and Tononi, 2011).
SWA as a Contributor to Synaptic Homeostasis. SHY also
suggests that SWA may not simply reflect changes in synaptic
strength but that the underlying slow oscillations may contribute
directly to synaptic renormalization. One scenario is that burst
firing, which is common in slow-wave sleep during transitions
between intracellular up and down states, may lead to a longlasting depression of excitatory postsynaptic potentials (Czarnecki et al., 2007), mainly via postsynaptic mechanisms. Indeed,
repetitive burst firing without synaptic stimulation, or paired with
synaptic stimulation in a way that mimics in vivo conditions,
leads to long-term depression and removal of AMPARs via
serine/threonine phosphatases and protein kinase C signaling
(Lanté et al., 2011). Another possible mode for SWA to enforce
synaptic renormalization is by decoupling through synchrony
(Lubenov and Siapas, 2008). In recurrent networks with conduction delays, synchronous bursts of activity typical of slowwave sleep would lead to net synaptic depression through
STDP mechanisms. For example, if neurons A and B fire simultaneously and neuron A projects to neuron B, then, due to
conduction delays, the presynaptic spike will arrive after the
postsynaptic spike has occurred, leading to synaptic depression.
(C) After bilateral lesions of the LC, expression of plasticity-related genes during wake is low; during subsequent sleep, SWA is lower than in nonlesioned controls
(from Cirelli et al., 1996; Cirelli and Tononi, 2000).
(D) During wake, subjects learn to adapt to systematic rotations imposed on the perceived cursor trajectory, a task that activates right parietal areas (Ghilardi
et al., 2000); during subsequent NREM sleep, SWA in the same areas shows a local increase, which correlates with postsleep improvements in performance (from
Huber et al., 2004).
(E) After a subject’s arm is immobilized during the day, motor performance in a reaching task deteriorates, and the P45 cortical component of the response
evoked by stimulation of the median nerve (SEP) decreases in contralateral sensorimotor cortex. In sleep postimmobilization, the same area shows a local
decrease in SWA (from Huber et al., 2006).
(F) Control loop for the homeostatic regulation of connection strength and firing rate/synchrony, based on the results of computer simulations of slow-wave sleep
(Olcese et al., 2010). Here connection strength (s) affects firing rates and synchrony (f) via activity mechanisms (A). During slow-wave sleep, plasticity mechanisms
(P) lead to a depression of synaptic strength (ds/dt) that is proportional to f. The resulting integrated value of connection strength (!), in turn, determines the new
value of firing rates and synchrony (f). As an example, strong average connection strength will lead to high firing rates and synchrony that, in turn, will strongly
depress synapses, to bring the system back to baseline values of connection strength. Conversely, when connections are renormalized, activity levels will not be
able to induce significant plastic changes and the system will reach a self-limiting equilibrium point.
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 19
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Perspective
A Control Loop for Synaptic Strength. If stronger synapses
increase SWA and SWA contributes to the decrease of synaptic
strength during sleep, the conditions are in place for implementing a control loop in which synaptic strength is the regulated
variable (Olcese et al., 2010). In this loop (Figure 4F), synapses
are potentiated due to learning in wake, leading to higher
neuronal firing rates and synchrony and thus to high SWA
when entering the sleep mode. On the other hand, strong,
synchronous firing during NREM sleep leads to synaptic depression. In turn, the progressive weakening of synapses reduces
firing rates and synchrony, slowing the process of activitydependent renormalization. Finally, the network reaches an
equilibrium point where synaptic strength is sufficiently low
that firing rates and synchrony are too low to further weaken
connections. Altogether, this control loop ensures that the
decline in synaptic strength and SWA during sleep is exponential
and self-limiting (Olcese et al., 2010), in agreement with experimental data in mammals and birds. Consistent with the existence of a control loop, the suppression of SWA during the first
3 hr of sleep prevents the homeostatic decline of SWA (Dijk
et al., 1987), suggesting that SWA is both a sensor and an
effector in a homeostatic process occurring during sleep.
SWA and the Specificity of Cortical Connections. In addition to
the total amount of synaptic strength, the specificity of connections is another factor that may influence neuronal synchronization and SWA. Computer simulations show that, for the same
total number and strength of synapses, synchronization is higher
if the connectivity among cortical neurons is homogenous or
random (all neurons tend to receive similar inputs) and lower if
the connectivity reflects functional specialization (different
groups of neurons receive different inputs) (Tononi et al., 1994,
1998). As mentioned earlier, learning in wake can reduce selectivity of firing as neurons start responding to a broader distribution of inputs (Balduzzi and Tononi, 2013), which in turn leads to a
reduction of specificity, with more neurons firing in response to
the same inputs. Thus, a reduction of selectivity and specificity
may also contribute to increased synchronization and increased
SWA after prolonged wake. By the same token, the restoration of
selectivity and specificity after sleep-dependent renormalization
should decrease SWA by lessening synchronization.
The relationship between connection specificity and neural
synchronization may be especially important during neural
development, including adolescence, when SWA shows a
remarkable decline (Campbell and Feinberg, 2009). In various
periods of development, after the overall anatomical wiring
patterns have been established, a process of synaptic refinement, often activity dependent, leads to an increase in the specificity of connections not only through synaptic pruning but also
through synaptic redistribution (Sanes and Yamagata, 2009).
Moreover, synapses may be rearranged within distinct dendritic
domains of a single neuron, whereby synapses from correlated
sources become clustered together and those from uncorrelated
sources are eliminated from one dendritic domain and redirected
to another one (Sanes and Yamagata, 2009; Winnubst and
Lohmann, 2012). Characteristically, target cells are initially innervated by several axons from multiple neurons, then lose many
inputs and become innervated more specifically by fewer sources (Ko et al., 2013). Electrophysiological evidence indicates
20 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
that after developmental refinement, if two cortical neurons are
connected by one synapse, they are more likely than average
to be connected by further synapses (Ko et al., 2013; Markram
et al., 1997). Thus, the decrease in SWA during adolescence
may reflect not only a decline in cortical synaptic density but
also an increase in the specificity of neuronal connections and,
by extension, cognitive maturation (Buchmann et al., 2011; de
Vivo et al., 2013).
While we highlight SWA in this Perspective, other mechanisms
of synaptic renormalization may also play a role in synaptic
homeostasis in sleep and should be kept in mind as well. For
example, sharp-wave ripples in slow-wave sleep or rest in CA1
hippocampal neurons could lead to a rescaling of synaptic
strength via antidromic spikes that requires L-type calcium
channel activation and functional gap junctions (Bukalo et al.,
2013). Other sleep events grouped by the onset of the ON period
of the slow oscillation, such as spindles and bursts of gamma
activity, may also be involved in the overall effects of NREM
sleep on plasticity (Rasch and Born, 2013). In general, the switch
from a mode of net synaptic potentiation to one of net synaptic
depression is likely mediated by the drop in the level of many
neuromodulators, such as acetylcholine, norepinephrine, serotonin, histamine, and hypocretin during NREM sleep. Neuromodulators can powerfully affect plasticity, including STDP polarity
(Pawlak et al., 2010). Specifically, changes in cholinergic and
noradrenergic modulation during sleep can shift the STDP curve
to favor depression (Isaac et al., 2009; Seol et al., 2007) and
could in turn promote synaptic renormalization in sleep.
Synaptic Homeostasis and the Cellular Benefits of Sleep
If sleep does in fact enforce the renormalization of synaptic
strength, what are the benefits? As mentioned earlier, if learning
during wake produces a net increase in synaptic strength, there
are consequences both at the cellular and at the systems level.
For an average neuron this means higher energy consumption,
larger synapses, greater need for the delivery of cellular supplies
to thousands of synapses, and cellular stress (Figure 1).
Energy. The human brain accounts for 2% of body mass but
uses up to 25% of the whole-body glucose consumption (Sokoloff, 1960). The average metabolic cost per neuron is not only high
but also fixed, as suggested by the fact that the total glucose use
by the brain is a linear function of the number of its neurons
(Herculano-Houzel, 2011). Synaptic activity as a whole accounts
for most of the brain’s energy use (Attwell and Gibb, 2005) due to
the energetically expensive processes of synaptic signaling,
including the release of neurotransmitter vesicles and their
recycling, action potential initiation and propagation, spiking,
and restoration of Na+ and K+ gradients via the Na+/K+
ATPase pump. Thus, a net increase in synaptic strength necessarily comes at the expense of an increase in energy consumption even for the same level of neural activity.
Moreover, despite the various mechanisms that ensure a tight
balance between excitation and inhibition (Haider et al., 2006)
and regulate excitability through intrinsic conductances (van
Welie et al., 2004) and synaptic scaling (Turrigiano, 2012), synaptic potentiation can lead to increased probability of firing in the
hippocampus (Buzsáki et al., 2002). Moreover, sustained wake
leads to increased firing rates (Kostin et al., 2010; Vyazovskiy
et al., 2009), while during the course of sleep firing decreases
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Perspective
in cortex (Vyazovskiy et al., 2009) and hippocampus (Grosmark
et al., 2012). Thus, if synaptic strengths and firing rates were to
grow without check as a result of wake plasticity, they could
eventually become energetically too expensive. It is well established that the brain’s energy consumption is ‘‘state dependent,’’
being higher in wake than in sleep, especially slow-wave sleep
(Kennedy et al., 1982; Madsen and Vorstrup, 1991). During this
stage, the second-by-second occurrence of hyperpolarized
down states is poised to reduce the energy consumption associated with synaptic activity and make more energy available
for other cellular processes (Cirelli et al., 2004; Mackiewicz
et al., 2007; Mongrain et al., 2010; Vyazovskiy and Harris,
2013). However, few studies have assessed whether the brain’s
energy consumption is also ‘‘history dependent,’’ i.e., whether it
increases in the course of wake and/or decreases in the course
of sleep. The available evidence suggests that this may be the
case, but only when wake is forced beyond its physiological
duration (Braun et al., 1997; Buysse et al., 2004; Shannon
et al., 2013; Vyazovskiy et al., 2004).
Cellular Supplies. Synapses also require many cellular constituents, from mitochondria to synaptic vesicles to various proteins
and lipids synthesized and often delivered over great lengths
(Kleim et al., 2003; McCann et al., 2008). These needs grow
acutely when synaptic strength increases. Indeed, one of the
genes most consistently upregulated in the brain during wake
is the endoplasmic reticulum (ER) chaperone BiP (Hspa5) (Cirelli,
2009). BiP assists in the folding of newly synthesized proteins,
including those produced after learning (Kuhl et al., 1992; Vandenberghe et al., 2005). BiP also assists in the folding of misfolded proteins as part of the unfolded protein response (UPR),
a global ER stress response whose corrective actions aim at
preserving ER functions. For reasons that remain unclear, a
few hours of sleep deprivation are sufficient to trigger the UPR,
whose end result is an overall decrease in protein synthesis
(Naidoo et al., 2005). Thus, the induction of plastic changes
during wake increases the need for protein synthesis, but
when wake is extended beyond its physiological duration, protein synthesis becomes impaired. With the reduced consumption of energy by synaptic transmission during hyperpolarized
down states, slow-wave sleep may represent an elective time
for brain cells to carry out many housekeeping functions,
including protein translation, the replenishment of calcium in
presynaptic stores, the replenishment of glutamate vesicles,
the recycling of membranes, the resting of mitochondria (Cirelli
et al., 2004; Mackiewicz et al., 2007; Mongrain et al., 2010; Vyazovskiy and Harris, 2013), and the clearance of the extracellular
space (Xie et al., 2013).
White Matter and Glia. Finally, imaging studies in humans
show that, as a result of learning, changes in gray and white
matter can occur within a few hours or days even in the adult
brain (Zatorre et al., 2012). Although the underlying cellular
mechanisms are poorly characterized, changes in synaptic
strength, synaptogenesis, and dendritic or axonal sprouting
are often accompanied by astrocytic growth, proliferation of
oligodendrocyte precursor cells, and possibly microvascular
modifications. Whether sleep plays a specific role in the glial
response to learning is unclear but should be explored in future
studies, as many brain transcripts upregulated during sleep are
involved in the synthesis and maintenance of membranes in general and of myelin in particular, and the proliferation of oligodendrocyte precursor cells is facilitated by sleep (Bellesi et al., 2013).
Synaptic Homeostasis and the Memory Benefits of Sleep
In this section, we consider how a process of activity-dependent
synaptic down-selection can also be beneficial for neuronal
communication and memory management (Figure 1), thus accounting for many of the positive effects of sleep on memory.
We then contrast down-selection with ‘‘instructive’’ models of
memory consolidation, according to which sleep benefits memory by potentiating recent memory traces.
Memory and Synaptic Renormalization by DownSelection
As illustrated by different computer models, SHY provides a
parsimonious explanation for several of the positive consequences of sleep on memory processes including acquisition,
consolidation, gist extraction, integration, and smart forgetting.
Acquisition. Restoration of the capacity to acquire new
memories is one of the most evident benefits of sleep. For
example, episodic memory retention is substantially impaired if
the training session follows sleep deprivation, despite no change
in reaction time at training, suggesting a decrease in encoding
ability due to sleep loss (Yoo et al., 2007). Similarly, the encoding
of novel images is impaired after a night of mild sleep disruption,
which decreases SWA without reducing total sleep time (Van Der
Werf et al., 2009). Conversely, a nap in which slow oscillations
were enhanced by transcranial stimulation, relative to sham
stimulation, enhanced the encoding of pictures, word pairs,
and word lists (Antonenko et al., 2013). Synaptic renormalization
provides a straightforward account of these beneficial effects of
sleep, since the desaturation of synaptic weights (Olcese et al.,
2010), the improvement in energy availability, and the reduction
in cellular stress all lead to an improved ability to learn.
Consolidation. Activity-dependent down-selection of synapses can also explain various aspects of memory consolidation.
At first, it may seem implausible that synaptic weakening could
enhance memory, until one considers that synapses supporting
new memories may depress less than synapses supporting
memories that are weak or less integrated with previous
memories (Figure 1). For example, a sequence-learning paradigm representative of nondeclarative tasks that benefit from
sleep was implemented in a large-scale model of the corticothalamic system equipped with a STDP-like down-selection rule
(Olcese et al., 2010). When the model learned a sequence of
activations during wake, the learned sequence was preferentially
reactivated during sleep, and reactivation declined over time, in
line with experimental results (Ji and Wilson, 2007; Kudrimoti
et al., 1999). The simulations showed that, by biasing the
STDP-like plasticity rule toward depression during sleep, weaker
synapses were depressed more than stronger ones, with the
result that S/N increased and learned sequences were better recalled by the model, in agreement with experimental findings.
Similar results were obtained with a downscaling rule under a
threshold of minimal efficacy (Hill et al., 2008) and with a
down-selection rule that protected the synapses that were
most activated (Nere et al., 2013). In summary, different downselection rules implemented in different models consistently
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 21
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yielded an increase in S/N and performance, potentially accounting for the consolidation of procedural memories. It
remains to be determined whether specific down-selection
mechanisms may be engaged in different species, brain circuits,
and developmental periods, and whether different rules may
offer specific advantages.
Activity-dependent down-selection during sleep also accounted for memory consolidation in a model of paired-associate (‘‘declarative’’) learning (Nere et al., 2013). Moreover, the
simulations found that enhancing activation of a particular memory in the down-selection phase results in a selective enhancement of that memory, in line with experimental results showing
the benefits of cuing during sleep (Antony et al., 2012; Bendor
and Wilson, 2012; Diekelmann et al., 2011; Rasch et al., 2007;
Rudoy et al., 2009). These simulations also examined the effects
of further synaptic potentiation in wake and of potentiation during ‘‘reactivation’’ in the sleep mode, followed by downscaling
of connections (Lewis and Durrant, 2011). In both cases, S/N,
performance, and recall showed a decrease rather than the
increase observed with down-selection during sleep. This implies that further potentiation in wake or sleep may result in
‘‘overtraining’’ and saturation of relevant neural circuits, since
both ‘‘signal’’ and ‘‘noise’’ synapses are potentiated. Similar
conclusions have been reached from perceptual learning experiments in humans using the visual texture discrimination task,
one of the best-characterized examples of sleep-dependent
memory consolidation (Karni and Sagi, 1993; Karni et al.,
1994). In this task, perceptual learning is assumed to occur
through synaptic potentiation (Cooke and Bear, 2012) within
the neural circuits specific for the trained background orientation
(Karni and Sagi, 1991). However, performance in wake declines
with overtraining and eventually does not recover even after
sleep, consistent with saturation of both signal and noise synapses and in line with the idea that the benefits provided by sleep
may be due to desaturation (Censor and Sagi, 2008, 2009).
Gist Extraction. Simulations of hierarchically organized networks indicate that down-selection can also account for gist
extraction—a prominent feature of memory that appears to be
facilitated by sleep (Inostroza and Born, 2013; Lewis and Durrant, 2011; Rasch and Born, 2013; Stickgold and Walker,
2013). Gist extraction is related to the brain’s penchant for forming more enduring memories of high-level invariants, such as
faces, places, or even maps, than of low-level details and instances of a particular encounter with the environment. In the
simulations, a hierarchically organized network was trained in
the wake mode with stimuli that shared some invariant features
but differed in specific details (Nere et al., 2013). Learning during
wake led to the strengthening of many connections, most of all
those of neurons in higher cortical areas relating to the invariant
concepts. During sleep, connections in higher areas were protected by strong and frequent reactivations, while synaptic
depression predominantly weakened synapses associated
with details learned by lower cortical areas, in line with the
more frequent origin of sleep slow waves in anterior rather than
posterior cortices (Massimini et al., 2004; Murphy et al., 2009).
A bias for preferential top-down activation during sleep can be
predicted based on multiple factors: (1) the inherent reversal of
the flow of signals from bottom-up to top-down, due to the
22 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
lack of driving input from low areas associated with the sensory
disconnection of sleep; (2) the large number and long time
constant of feedback connections (due to a higher percentage
of NMDARs (Self et al., 2012); (3) the high likelihood that activation of higher areas can produce meaningful activation
patterns that percolate top-down through diverging back connections, in line with the evidence suggesting that cognitive
activity during sleep is more akin to imagination than to perception (Nir and Tononi, 2010); and (4) the low likelihood that random
activations of neurons in lower areas may selectively activate
neurons in higher areas through their specialized convergent
connectivity. Therefore top-down spontaneous activation during
sleep would have a competitive advantage over bottom-up,
random activation of lower areas, which would resemble meaningless ‘‘TV noise’’ and thus would fail to percolate bottom up
through feedforward connections. Conceptually, the process
of preserving the gist and removing the chaff resembles the increase in S/N through which sleep appears to benefit nondeclarative memories. The benefits of sleep for gaining insight of
a hidden rule, enhancing the extraction of second-order inferences, and helping abstraction in language-learning children—
all tasks that are conceptually related to gist extraction (Stickgold and Walker, 2013)—may also be achieved through similar
mechanisms.
Integration. Another prominent feature of memory is that new
material is better remembered if it fits with previously learned
schemas (Bartlett, 1932), that is, if the new memories are integrated or incorporated with an organized body of old memories
(McClelland et al., 1995). Once again, sleep seems to facilitate
this process (Inostroza and Born, 2013; Lewis and Durrant,
2011; Rasch and Born, 2013; Stickgold and Walker, 2013).
Computer simulations confirm that memory integration can be
obtained through down-selection (Nere et al., 2013), whereby
new and old memories that fit well together are coactivated
strongly and repeatedly during sleep and thus are comparatively
protected, while new memories that fit less well with previous
knowledge are less activated and are competitively downselected.
Protection from Interference. Sleep can also benefit declarative memories by sheltering them from interference (Alger
et al., 2012; Ellenbogen et al., 2006; Korman et al., 2007; Sheth
et al., 2012). A simple mechanism by which NREM sleep, like
quiet wake, alcohol, and several drugs, can reduce interference
is by blocking LTP-like potentiation and thus new learning
(Mednick et al., 2011; Wixted, 2004). Another mechanism may
involve the molecular or structural ‘‘stabilization’’ of synapses
tagged during wake, although direct evidence that sleep may
do so is missing. In this context, an interesting possibility is
that learning in wake would promote the early/induction phase
of synaptic potentiation, while sleep would promote the late/
maintenance phase. GluA2-containing AMPARs are strongly
involved in constitutive receptor cycling and synaptic depression, while GluA1-containing AMPARs are linked to synaptic
potentiation (Kessels and Malinow, 2009). According to current
models, the maintenance of synaptic potentiation requires that
a constant amount of GluA2-containing AMPARs is preserved
at the synaptic membrane, perhaps through the formation of
CamKII-NMDA complexes acting as seeds to keep them
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anchored to the plasma membrane (Sanhueza and Lisman,
2013). Because these complexes are large and are made of
many, partly redundant proteins with different lifespan, the turnover of each protein is unlikely to imperil the existence of the
complex and thus of the memory (Sanhueza and Lisman,
2013). Thus, if sleep were to actively maintain previously induced
synaptic potentiation rather than inducing it de novo, it would
likely do so by preventing the removal of synaptic GluA2containing AMPARs, rather than by promoting the new insertion
of GluA1-containing AMPARs. The available evidence, however,
suggests that synaptic expression of GluA2-containing AMPARs
goes in the same direction as that of GluA1-containing AMPARs,
i.e., it is higher in wake than in sleep, although the changes do not
reach significance (Vyazovskiy et al., 2008).
Forgetting. Forgetting has been recognized as an important
mechanism for dealing efficiently with the inevitable accumulation of unimportant details (Wixted, 2004). According to a recent
view, forgetting relies heavily on active decay and could involve
the internalization of GluA2-containing AMPARs during sleep
(Hardt et al., 2013). Indeed, computer simulations show that
active forgetting, if performed offline so as to weaken preferentially memory traces that represent details and are less
integrated with the overall structure of knowledge, is likely to
constitute a major benefit of sleep on memory (Hashmi et al.,
2013), offering a potential solution to the plasticity-stability
dilemma of learning new associations without wiping out previously learned ones (Abraham and Robins, 2005; Grossberg,
1987). The plasticity-stability dilemma is evident in artificial neural networks, where increasing connection strengths to store
new associations can lead to ‘‘catastrophic interference’’
(French, 1999). The brain, despite its large memory capacity, is
probably not immune to such problems, and the potential for
sleep to help with this issue has been recognized before (Crick
and Mitchison, 1995; Robins and McCallum, 1999). Down-selection during sleep provides an efficient and smart means for enforcing an overall renormalization of synaptic strength, thereby
avoiding runaway potentiation and catastrophic interference
(Hashmi et al., 2013).
Matching. Another benefit of down-selection becomes
apparent when considering the systematic alternation between
net synaptic potentiation during wake and depression during
sleep (Hashmi et al., 2013). Most neurons in the brain only
communicate with other neurons and not directly with sensory
inputs and motor outputs. However, high levels of neuromodulators during wake alert neurons that they are connected in a
‘‘grand loop’’ with the environment and learning should be
enabled. Conversely in sleep, low levels of neuromodulators
signal disconnection from the environment, leaving only internal
loops operative, and enforce a bias toward smart, selective
forgetting (Figure 2). Over time, the systematic alternation
between ‘‘connected’’ potentiation and ‘‘disconnected’’ depression should favor the acquisition of activity patterns related to
statistical regularities in the environment that are presumably
adaptive, at the expense of activity patterns that are unrelated
to the environment and are potentially maladaptive. In this
way, sleep can increase the ‘‘matching’’ between the causal
structure of the brain and that of the environment to which it is
adapted. In principle, matching can be assessed by measuring
how much the brain states triggered when interacting with the
environment differ from those triggered when it is exposed to
uncorrelated noise. In a simple model in which changes in
matching could be measured rigorously (Hashmi et al., 2013),
the learning rules for potentiation in wake and down-selection
in sleep led to a progressive increase in matching over repeated
sleep-wake cycles. By contrast, matching decreased if downselection occurred in wake or if synaptic potentiation occurred
during sleep, due to the frequent strengthening of spurious
coincidences not sampled from the environment. This result
highlights a potential problem with the idea that sleep may
help memory through ‘‘pseudorehearsal’’—the systematic ‘‘relearning’’ of both new and old memories by random reactivation
and synaptic potentiation (Robins and McCallum, 1999). By
contrast, activity-dependent down-selection can lead to the
transfer, transformation, and integration of memories, and to
the stimulation of unused circuits, without the pitfalls of spurious
potentations.
An Alternative View of Sleep-Dependent Memory
Consolidation: Replay-Transfer-Potentiation and Active
System Consolidation
An alternative model suggests that sleep benefits memory
consolidation by selectively strengthening certain synaptic
traces. The original replay-transfer-potentiation model (Born
et al., 2006) was inspired by three main sets of observations.
First, in line with the standard system consolidation framework
(McClelland et al., 1995; Squire et al., 2004), the hippocampus—a fast learner—stores memories for a short time before
they are transferred to the cerebral cortex—a slow learner—for
long-term storage. Second, firing patterns established during
learning in wake are replayed in sleep, especially as accelerated
sequences during sharp-wave ripples in NREM sleep, and impressed upon neocortical circuits. This evidence is often tied
together with work suggesting that the dialogue between hippocampus and cortex may reverse in direction between wake and
sleep (Buzsáki, 1998; Chrobak and Buzsáki, 1994), that the
neuromodulatory milieu of sleep may favor outflow from hippocampus in some stages of sleep (Hasselmo, 1999), and that
intense activity in the hippocampus during sleep may impinge
upon cortex and modify the firing of cortical neurons (Logothetis
et al., 2012; Siapas and Wilson, 1998). Third, there is strong
evidence that sleep benefits declarative memory consolidation
(Born et al., 2006; Diekelmann and Born, 2010; Stickgold and
Walker, 2013; Wilhelm et al., 2012). Based on these premises,
it is natural to consider the possibility that hippocampal replay
during sleep may ‘‘transfer’’ memory representations from a
short-term store in the hippocampus to long-term stores in the
cortex. Similarly, it is plausible to infer that the activation of
hippocampal circuits during sharp-wave ripples, followed by
spindles and slow waves in the cortex, may be responsible for
memory enhancements after sleep and ‘‘system consolidation’’
(Born et al., 2006). Finally, one can hypothesize that replay during
sleep leads to an enhancement of memories through synaptic
potentiation in the relevant neural circuits, in a process of ‘‘synaptic consolidation.’’
While the replay-transfer-potentiation model is straightforward and elegant, some of its assumptions are problematic.
Thus, the original idea that memories are transferred from
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 23
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short-term storage in the hippocampus to long-term storage in
the cortex has lost support, in favor of the notion that an episodic
memory trace is always a hippocampal-neocortical ensemble,
where the role of the hippocampal formation is to index and
bind together sparse cortical representations (Winocur and
Moscovitch, 2011). Over time, memories are likely to be reactivated in multiple contexts, forming multiple related traces that
slowly become integrated into a large body of semantic knowledge and lose their episodic character (Winocur and Moscovitch, 2011). There are also indications that neocortical circuits
may not be ‘‘slow learners’’ after all, but may rapidly achieve
system-level consolidation as long as a new memory can be
easily assimilated into a body of related knowledge (Tse et al.,
2011). It should also be noted that in the down-selection model,
the very uniqueness of the hippocampal, episodic component of
memories would make them unsuitable to gist extraction and
more liable to interference from the superposition of new memories, leading to an advantage of the new at the expense of the
old in hippocampal circuits. Conversely, the cortical, semantic
component of such memories would benefit from superposition
and gist extraction, as is the case with nondeclarative memories,
leading to an advantage for the signal at the expense of the noise
in cortical circuits.
Moreover, most of the evidence indicates that, during NREM
sleep, synchronous volleys associated with slow waves percolate from cortex to hippocampus, rather than the other way
around. Recent studies in animals and humans show that
cortical slow waves typically begin in cortex and only later reach
medial temporal lobe structures and the hippocampus (Isomura
et al., 2006; Mölle et al., 2006; Nir et al., 2011). Thus, the interactions between cortex and hippocampus during sleep are most
likely bidirectional (Buhry et al., 2011; Diekelmann and Born,
2010; Ji and Wilson, 2007; Tononi et al., 2006), with up states
in the cortex activating the hippocampus in a feedforward
manner, prompting the hippocampus itself to feedback on the
cortex with sharp-wave ripple complexes.
More recent accounts of how sleep can benefit memory can
be grouped under the general heading of ‘‘active system consolidation’’ models, which have modified and elaborated the standard replay-transfer-potentiation model in several important
ways (Diekelmann and Born, 2010; Inostroza and Born, 2013;
Lewis and Durrant, 2011; Rasch and Born, 2013; Stickgold and
Walker, 2013). First, such models propose that sleep leads to a
system-level transformation of memory representations and
not just to a straightforward transfer from hippocampus to cortex. Moreover, some aspects of the renormalization model,
including the claim that overall synaptic strength decreases
during sleep, have been incorporated in the process of active
system consolidation. For example, it has been proposed that
synapses subject to replay during sleep may first be selectively
potentiated and then globally downscaled (Lewis and Durrant,
2011) or may first be ‘‘tagged’’ for potentiation during NREM
sleep replays in the context of an overall downscaling and then
potentiated during subsequent REM sleep (Rasch and Born,
2013). Active system consolidation models can account for
many experimental data and have inspired numerous experiments (Mascetti et al., 2013a; Rasch and Born, 2013). However,
even in their latest incarnations, such models still differ from the
24 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
down-selection model on a fundamental issue: whether memory
consolidation and integration during sleep are achieved primarily
by ‘‘instruction’’ or by ‘‘selection.’’
Instruction or Selection?
In both the active system consolidation model and the downselection model, spontaneous activity during sleep, especially
slow oscillations, spindle oscillations, and sharp-wave ripples,
trigger plastic processes that ultimately account for the memory
benefits of sleep. There is now evidence that promoting such
oscillations can enhance memory consolidation and disrupting
them can impair it (reviewed in Rasch and Born, 2013). What
remains controversial is the direction of synaptic changes
(potentiation or depression) during sleep and their synaptic and
systems-level consequences. In the active system consolidation
model, ‘‘replays’’ of recent waking activity patterns in NREM
sleep ‘‘instruct’’ learning, determining which connections should
be strengthened selectively or ‘‘tagged’’ for subsequent
strengthening in REM sleep (Diekelmann and Born, 2010; Rasch
and Born, 2013). Such strengthening would explain why sleep
not only enhances declarative memories but also changes their
quality, enabling the integration of newly learned material into
pre-existent schema, the emergence of insight, and even the
formation of false memories. By contrast, in the down-selection
model, spontaneous activity during sleep samples comprehensively the brain’s knowledge basis in a neuromodulatory milieu
that promotes depression. In doing so, spontaneous activity ‘‘selects’’ among pre-existing memory traces those that are stronger
and fit better with the overall organization of memory, protecting
them preferentially and leading to the ‘‘survival of the fittest,’’
without requiring new learning. Of note, according to the
down-selection model, spontaneous ensemble activation of
corticohippocampal circuits during sleep does not need to be
random but may be highly structured, as long as it is comprehensive. For example, slow waves are more global early in the night,
then become more local (Nir et al., 2011), suggesting that consolidation and integration of memory traces may first be achieved
on a larger-scale and then, progressively, in more restricted
circuits. Moreover, slow waves not only have varying sources
of origin and propagation (Massimini et al., 2004; Murphy et al.,
2009; Nir et al., 2011) but typically only involve a subset of brain
areas (Nir et al., 2011). It could be that certain slow waves may be
triggered preferentially by synapses that were recently strengthened during wake, thus priming certain circuits for preferential
consolidation. Similarly, instructions to remember certain material, administered after learning but before sleep, may prime
certain pathways for more frequent sleep-dependent consolidation. Which of these two frameworks—instruction and selection—fits better with the available data?
Replay to Reinforce or Play to Select? Active system consolidation models were initially galvanized by the demonstration of
so-called ‘‘replays’’ or reactivations: patterns of neuronal firing
during sleep that bear some resemblance to patterns of activity
during preceding wake. Replays are especially evident during
hippocampal sharp-wave ripples, but they can be demonstrated
also during ‘‘ON’’ periods in cortex, corresponding to the up
state of the slow oscillation. However, we now know that reactivations occur outside of sleep, i.e., in quiet wakefulness (Davidson et al., 2009; Diba and Buzsáki, 2007; Foster and Wilson,
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2006; Karlsson and Frank, 2009; Kudrimoti et al., 1999), during
initial learning (Singer et al., 2013), and during many states of
cortical activation (Bermudez Contreras et al., 2013). This makes
it difficult to understand why, if replays are important for
rehearsing memories, animals should risk being asleep if they
can do so when awake and monitoring the environment.
An equally serious issue is that replays during sleep are
comparatively infrequent, are not very faithful to the original,
are usually played several times faster, and decline rapidly
during the first hour of sleep (Ji and Wilson, 2007; Kudrimoti
et al., 1999; Nádasdy et al., 1999). Rare, noisy reactivations
would not seem ideal for enhancing memories. Moreover, if
replays strengthen the associated memory traces, why should
replays themselves fade rapidly? Above all, what should one
make of the overwhelming proportion of spontaneous activity
that does not constitute replays? Even during quiet wake, firing
sequences of CA1 hippocampal cells do not only replay previous
events but may instead anticipate (‘‘preplay’’) those that will be
triggered by future interactions with the environment, suggesting
that spontaneous firing patterns can be recruited to make plans
and encode new memories (Dragoi and Tonegawa, 2011; Pfeiffer
and Foster, 2013). In fact, since the brain is spontaneously
active, it can be expected by default to ‘‘replay,’’ ‘‘preplay,’’
and just plain ‘‘play’’ many different combinations from its vast
repertoire of memories (as suggested by dreams), whether old
or recently modified, in wake or in sleep (Gupta et al., 2010).
What is not easy to explain in an instructive model, then, is
how the sleeping brain, disconnected from the environment,
could distinguish the ‘‘right replays’’ from the ‘‘wrong’’ ones
and make sure that only the former are potentiated, thus avoiding the formation of spurious memories. Of note, while enhanced
hippocampal replay could explain why sensory cuing during
sleep enhances memory, the beneficial effects of sensory cuing
during sleep, as well as of precuing through instructions to
remember before sleep, can be explained just as well by
increased down-selection triggered by increased activations
(Nere et al., 2013).
Does Synaptic Potentiation Occur during Sleep? As we have
seen, structural, molecular, and electrophysiological studies
consistently indicate that sleep is accompanied by a net depression of synaptic strength, although this evidence so far does not
rule out the selective potentiation of a subset of synapses. The
case for synaptic potentiation in sleep rests on several grounds.
One is based on the assumption that phasic events such as
hippocampal sharp-wave ripples and correlated cortical spindles (Siapas and Wilson, 1998; Sirota et al., 2003) may provide
conditions conducive to long-term potentiation (e.g., Buzsáki,
1989; Louie and Wilson, 2001; Pennartz et al., 2004), because
they may result in a large influx of calcium inside dendrites
(Sejnowski and Destexhe, 2000; Steriade and Timofeev, 2003).
However, it was recently shown that antidromic spikes produced
during sharp-wave ripples produce an overall downscaling of
synaptic strength through L-type calcium channel activation
(Bukalo et al., 2013). In vitro and in vivo studies show that electrical stimulation near 10 Hz, which spans the spindle range
(7–14 Hz), can result in either synaptic potentiation or depression, depending on the intensity of the stimulation and the
pattern of cortical activity (Rosanova and Ulrich, 2005; Werk
and Chapman, 2003; Werk et al., 2006). Moreover, high-frequency stimulation in hippocampus consistently induces synaptic potentiation during wake and REM sleep but rarely during
NREM sleep (Bramham and Srebro, 1989; Leonard et al.,
1987). Finally, the most ubiquitous and frequent pattern of activity during NREM sleep is burst-pause activity at around 0.8 Hz,
corresponding to the up and down states of the slow oscillation,
which leads to synaptic depression (Lanté et al., 2011; see also
Czarnecki et al., 2007).
Another reason is provided by imaging studies indicating that
the relative activation of several brain areas increases during
postsleep retest but not at encoding (Mascetti et al., 2013a).
These results are interpreted as evidence for the selective potentiation of connections during sleep. Yet, relative changes in fMRI
responses after sleep could also result from a down-selection
process, whereby certain memory traces are protected more
than others from depression, changing the ‘‘synaptic landscape’’
of the brain.
Then there are some molecular studies in rats indicating that
induction of electrical LTP or novel experiences increase cortical
expression of the immediate-early genes zif-268 and Arc during
REM sleep, though not during NREM sleep (Ribeiro et al., 2002,
2007). Reactivation during NREM sleep may set the stage for the
induction of synaptic potentiation during a subsequent REM
sleep episode (Diekelmann and Born, 2010; Rasch and Born,
2013). However, the link between zif-268 and synaptic potentiation remains indirect (Davis et al., 2003; Knapska and Kaczmarek, 2004). Moreover, recent studies show that after early
induction in response to neuronal activation, Arc enters weakly
stimulated synapses and promotes their depression via endocytosis of AMPARs (Okuno et al., 2012) and/or enters the nucleus
to mediate cell-wide synaptic downscaling by repressing the
transcription of the same receptors (Korb et al., 2013). Other
experiments found that active avoidance learning increases the
density of ponto-geniculo-occipital (PGO) waves during postlearning REM sleep, and this increase is correlated with the subsequent consolidation of the task (Datta, 2000). The expression
of Arc, P-CREB, BDNF, and zif-268 also increases in several
brain areas 1–6 hr after avoidance learning (Ulloor and Datta,
2005), but whether the induction of these activity-dependent
genes occurs specifically during REM sleep after training, and
whether it is causally linked to the consolidation of the avoidance
task, remains unclear. Altogether, how REM sleep may
contribute to memory consolidation remains an open issue
(see below).
A final reason comes from developmental studies using
monocular deprivation, which triggers first a decrease in the
response of the deprived eye due to synaptic depression, followed by an increase in the response of the open eye due to
homosynaptic and/or heterosynaptic potentiation (Smith et al.,
2009). In kittens, an increase in the open eye response occurs
during the 6 hr of sleep following eye closing (Frank et al.,
2001). This result and the identification of a narrow window of
1–2 hr, during which the phosphorylation of CamKII and other
molecular markers of synaptic potentiation increases during
sleep (Aton et al., 2009), poses a challenge to the down-selection
model (Frank, 2012). However, while acute monocular deprivation is a powerful paradigm for investigating the occurrence
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and mechanisms of plasticity, it is also nonphysiological. Under
such conditions, wake is accompanied not by the usual net
potentiation but by massive synaptic depression, and it is followed by a 40% decrease in slow waves during subsequent
sleep (Miyamoto et al., 2003). What these experiments show,
then, is that under special conditions, synaptic potentiation
can be induced during sleep, consistent with previous evidence
that LTP induction during NREM sleep is difficult but not impossible (Bramham and Srebro, 1989; Leonard et al., 1987). However, these findings say less about the role played by sleep under
physiological conditions.
Are New Associations Formed during Sleep? If replays and,
more generally, neural activity during sleep were able to potentiate synapses and transform memories, they should also be
able, under the right conditions, to promote the learning of new
associations. However, a consistent feature of declarative memory consolidation is that, after acquisition, declarative memories
do not improve in absolute terms but always deteriorate. Thus,
the positive effect of sleep on retention is that it slows down
forgetting of certain memories. This observation is clearly more
in line with relative down-selection rather than with absolute
potentiation. Procedural memories do get better in absolute
terms, due either to a net increase in speed of performance, as
in visual discrimination learning, or in accuracy, as in visuomotor
learning, both of which depend on slow-wave sleep (Aeschbach
et al., 2008; Landsness et al., 2009). Yet even in these cases,
computer simulations show that an absolute increase in S/N
can be obtained through down-selection (Hill et al., 2008; Nere
et al., 2013; Olcese et al., 2010).
Three other cases suggesting that sleep may positively
strengthen memory traces and create new associations are
insight, false memories, and sleep learning. The emergence of
insight after sleep was shown using a modified version of the
number reduction task, which subjects can solve slowly, by
applying the instructions they are given at the onset of training,
or quickly, if they realize that there is a hidden rule to reach the
final solution. Subjects who slept were twice more likely to gain
insight of the hidden rule than those who stayed awake (Wagner
et al., 2004), suggesting that sleep may have created new associations that may have led to insight. However, fMRI data indicate that insight solutions activate a specific brain region, the
anterior superior temporal gyrus, more than noninsight solutions
and that the same area is already activated during the initial solving efforts (Jung-Beeman et al., 2004). Thus, it may be that sleep,
rather than creating an insight solution from scratch, simply lets it
emerge more clearly after removing the ‘‘noise’’ around it, as
suggested by the simulations of gist extraction discussed earlier
in this Perspective.
False recall is classically tested using the Deese-RoedigerMcDermott paradigm, in which memorizing a list of related
words (e.g., bed, rest, tired, dream, snooze, nap), elicits high
recall of a ‘‘lure,’’ a word that is semantically associated to the
list of studied words but is never presented at training (e.g.,
sleep). False memories have been shown to increase after sleep
relative to wake in some studies (Darsaud et al., 2011; Payne
et al., 2009), but not in others (Diekelmann et al., 2008; Fenn
et al., 2009). Even when sleep increased false recall, however,
the false memories were already present at the end of training.
26 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
Once again, it seems that sleep did not create new false memories from scratch but at most slowed down their forgetting
(Fenn et al., 2009; Payne et al., 2009).
As for sleep learning, well-controlled studies have failed to
show the transfer of any learning for declarative material from
sleep to the following wake (Roth et al., 1988; Wyatt et al.,
1994). If the EEG is monitored carefully, there is virtually no recall
of verbal material presented during sleep (Simon and Emmons,
1956) unless subjects are awakened (Koukkou and Lehmann,
1968; Portnoff et al., 1966). On the other hand, some forms of
nondeclarative memory may be acquired during sleep. For
example, subjects could learn a simple conditioned response
(an EEG K complex triggered by a tone associated with a shock)
and the learned response could be transferred to waking (Beh
and Barratt, 1965). Also, in a recent study in which the sleep
EEG was carefully monitored (Arzi et al., 2012), subjects did learn
to inhale larger volumes when conditioned with tones paired with
pleasant odors. However, simple conditioning is quite different
from declarative learning (Stickgold, 2012) or even skill learning.
Moreover, we know now that during EEG-defined sleep, individual brain regions can be in a wake-like state (Nir et al., 2011).
Thus, it is possible that the conditioning procedures may have
induced a local wake-like activation that could not be detected
with traditional EEG.
In summary, while a definitive assessment is still premature,
there is no clear evidence that, under normal conditions, sleep
can lead to new learning or promote synaptic potentiation, nor
that the sleeping brain can distinguish between ‘‘replays’’ that
should be potentiated and ‘‘plays’’ that should not.
Some Open Issues
In the future, systematic optogenetic stimulation of multiple
inputs to a given neuron, simultaneously with calcium imaging
in vivo, may establish more directly what happens to individual
synapses during wake and sleep and adjudicate between the
active system consolidation and down-selection models. Moreover, other important features of sleep will have to be addressed,
including the role of REM sleep, the effects of sleep deprivation,
and the function of sleep during development, as will be briefly
discussed below.
REM Sleep. While some of the evidence that wake leads to
an increase in synaptic strength and sleep to its homeostatic
renormalization points to a specific role for NREM sleep, many
of the findings suggestive of renormalization were obtained in
relation to total sleep, not just NREM sleep. Moreover, synaptic
homeostasis occurs in invertebrates such as flies, where there is
so far no indication for a distinction between different sleep
stages. It is thus natural to ask how REM sleep may or may not
fit with the hypothesized core function of sleep, and whether
the regular alternation of NREM/REM periods in most mammals
and birds may serve a particular function (Giuditta et al., 1995).
These are classic questions that have implications for both the
ontogeny and the phylogeny of REM sleep (Jouvet, 1998).
It has been argued that REM sleep may not serve an essential
function, at least with respect to memory, since it can be largely
suppressed for months by antidepressant treatment, or even
permanently by brainstem lesions, without obvious ill effects
(Siegel, 2001). On the other hand, REM sleep can have memory
benefits (Graves et al., 2001; Karni et al., 1994), and the PGO
Neuron
Perspective
waves, phasic events that are triggered by burst firing in the pons
but extend to the forebrain, could be one of the underlying mechanisms (Datta, 2000). In rodents, REM sleep is also characterized
by the occurrence of regular theta oscillations in the
hippocampus. Theta oscillations also occur during active exploration in wake, travel across the hippocampal formation (Lubenov and Siapas, 2009), and can modulate plasticity in complex
ways (Hölscher et al., 1997; Poe et al., 2010). An intriguing
possibility is that REM sleep may promote the insertion of
AMPARs in the synaptic sites that are still effective after renormalization during NREM sleep, thus favoring their consolidation
(Tononi and Cirelli, 2003). Similarly, it may potentiate synapses
that were ‘‘tagged’’ by replays during NREM sleep (Rasch and
Born, 2013). It may also stimulate unused synapses, another
possible function that has been repeatedly attributed to sleep
in general (Kavanau, 1997; Krueger and Obál, 1993, 2003), or
prompt the formation of new synaptic contacts to refresh the
repertoire of circuits available for the acquisition and selection
of new memories.
An alternative possibility is suggested by the observation that
intense spontaneous activity can lead to the cleansing of uncorrelated synapses and to the relative consolidation of correlated
ones (Cohen-Cory, 2002; Zhou et al., 2003). In this view, REM
sleep could lead, with different means and perhaps for different
brain structures, to results similar to the ones postulated here for
NREM sleep in the cerebral cortex of mammals and birds (Tononi
and Cirelli, 2003). In a recent study (Grosmark et al., 2012), firing
rates of hippocampal CA1 neurons decreased across sleep, as
they do in cortex (Vyazovskiy et al., 2009) but did so in REM sleep
as a function of REM theta power and not, as they do in cortex, in
NREM sleep as a function of SWA (Vyazovskiy et al., 2009). If
these progressive changes in firing rate are indicative of changes
of neuronal excitability and net synaptic strength, they would
suggest that synaptic renormalization is brought about by SWA
during NREM in the cortex and by theta activity during REM
sleep in the hippocampus. In this regard, it is notable that hippocampal cells lack the slow oscillation. Unlike cortical cells, hippocampal granule cells and CA3 and CA1 pyramidal cells do
not show OFF states and are not bistable (Isomura et al.,
2006). Considering the likely role of the slow oscillation in promoting synaptic depression during sleep, it may be that synaptic
renormalization in the hippocampus uses different mechanisms,
tied to theta/gamma activities, which are its dominant oscillatory
modes. Indeed, there are several indications that the phase of
theta activity can influence the direction of plasticity (Hölscher
et al., 1997; Poe et al., 2010), and the dynamics of theta-gamma
coordination are different in REM sleep and wake (Montgomery
et al., 2008). Finally, as in the cortex, the levels of neuromodulators such as noradrenaline, serotonin, and histamine are high in
wake and low in REM sleep, potentially affecting the direction of
plastic changes. In summary, while REM sleep could contribute
to memory in many intriguing ways, we still do not know for sure if
it is even necessary, and if so how it would perform its functions.
Sleep Deprivation and Local Sleep in Wake. Acutely extending
wake or chronically curtailing sleep impairs many cognitive functions. The underlying cellular mechanisms remain unclear, but
the recent identification of ‘‘local sleep’’ during wake offers
some clues (Vyazovskiy et al., 2011). Multiarray recordings in
rats show that the longer an animal stays awake, the more its
cortical neurons show brief periods of silence that are essentially
indistinguishable from the OFF periods associated with the slow
oscillations of sleep. These OFF periods are local in that they
occur at different times in different brain regions and are associated with a local EEG slow/theta wave (2–6 Hz). Since local
OFF periods in wake are remarkably similar to sleep OFF
periods, they may also result from increased neuronal bistability
caused by an increased drive toward hyperpolarization. In turn,
hyperpolarization could be a local consequence of synaptic
overload caused by intense wake plasticity leading to an imbalance between energy supply and demand, possibly signaled by
a local increase in extracellular adenosine (Brambilla et al., 2005).
At present, it is unknown whether local OFF periods in wake can
carry out some of the restorative functions of sleep, including
synaptic homeostasis. However, the occurrence of local OFF
periods raises interesting new questions. For example, if local
sleep in wake occurred in hypothalamic and brainstem neurons
that exert a central control on arousal, it could help explain the
increased sleepiness and global deficits in arousal and attention
after sleep deprivation, especially for simple, boring tasks (Killgore, 2010; Lim and Dinges, 2010). Intriguingly, overall performance in sleep-deprived subjects is highly unstable, oscillating
back and forth from normal levels to catastrophic mistakes
(Doran et al., 2001; Zhou et al., 2011), just as would be expected
given the stochastic, all-or-none occurrence of local sleep. In
addition, the occurrence of local sleep at times in subcortical,
arousal-promoting systems, and at other times in specific
cortical areas, could explain the occasional dissociation between overall vigilance and specific cognitive functions under
conditions of sleep deprivation (Blatter et al., 2005; Sagaspe
et al., 2007; Sandberg et al., 2011).
Development. If sleep serves synaptic homeostasis, then it
should do so even more prominently during development (Roffwarg et al., 1966). Childhood and adolescence are times of
concentrated learning, which in itself would make sleep particularly important. Moreover, development is characterized by
intense synaptic remodeling, with massive synaptogenesis
accompanied by massive synaptic pruning. The increase in the
number of synapses during early development is explosive and
is likely to pose a risk of synaptic overload and associated
cellular burdens for both neurons and glia. Thus, sleep may be
essential for maintaining homeostasis not just in the strength
but also in the number and distribution of synapses. For the usual
reasons, such rebalancing is best achieved offline, when neurons can sample most of their inputs in a comprehensive
manner. It is worth remarking that, during the initial, experience-independent phases of synaptic formation and refinement
(Sanes and Yamagata, 2009), spontaneous activity during sleep
might serve to restore synaptic homeostasis also in the positive
direction, to avoid the risk that a neuron may end up connected
just to a few sources.
As was mentioned above, in adolescent mice, wake is associated with a net increase in the number of synapses and sleep
with a net decrease, although the total number of synapses
does not change appreciably over 24 hr (Maret et al., 2011;
Yang and Gan, 2012). Even without changes in number, the addition and survival of some synapses, and the elimination of others,
Neuron 81, January 8, 2014 ª2014 Elsevier Inc. 27
Neuron
Perspective
can enforce an activity-dependent process of synaptic rearrangement that increases the specificity of connections. For
example, shared inputs may be redistributed to segregated
neurons or to distinct dendritic domains within a neuron (Ko
et al., 2013; Sanes and Yamagata, 2009; Winnubst and
Lohmann, 2012). Cycles of synaptogenesis or synaptic strengthening in wake, followed by down-selection in sleep, may play a
role in this process and enforce competition for ‘‘survival of the
fittest.’’
By the same token, if down-selection were impaired during
developmental critical periods when a progressive weakening
of short-range connections occurs in association with the
strengthening of long-range connections (Uddin et al., 2010),
there could be irreversible consequences on the wiring and
function of neural circuits. For example, if circuits involving
nearby neurons were activated more frequently than those
involving neurons in a distant cortical area, the former would
be consolidated, while the latter would be lost permanently. Increases in short-distance connections at the expense of longdistance ones have been reported in autism (Zikopoulos and
Barbas, 2010), a developmental disorder with prominent sleep
abnormalities (Reynolds and Malow, 2011), though the causeeffect relationships are not known. In the future, by mapping
the axonal projections of specific cell types, it should be possible
to determine whether sleep deprivation or restriction in critical
periods during development may alter the refinement of cortical
and other connections. If this were the case, sleep loss early in
life would lead not only to impaired performance but also to a
permanent miswiring of brain circuits.
Conclusions and Caveats
So far, direct experimental evidence from structural, molecular,
and electrophysiological studies in a variety of species is broadly
consistent with the core idea behind SHY—that normal sleep
allows the brain to reestablish synaptic and cellular homeostasis
challenged by plastic changes occurring during normal wake.
The wealth of data about how sleep benefits learning and memory are also compatible with SHY, though other interpretations
are certainly possible. Finally, SHY offers a parsimonious rationale for why the brain needs sleep: to renormalize synaptic
strength based on a comprehensive sampling of its overall
knowledge of the environment, rather than being biased by the
particular inputs of a particular waking day. It is important to
emphasize that SHY is a hypothesis about sleep and plasticity
under natural conditions, not about which plastic changes may
be induced under nonphysiological conditions. Moreover, SHY
does not endorse a specific mechanism for potentiation during
wake and depression during sleep.
While we have discussed the strengths of SHY, there are
many ways in which SHY may turn out to be wholly or at least
partly wrong. A major way is if synaptic homeostasis can be
accomplished sufficiently well in wake. For example, it could
be that at a given time only a small subset of brain circuits are
engaged by behavior, and all other circuits are effectively offline
even in wake. Conceivably, such offline circuits could renormalize synaptic strength even while the organism is behaving. What
is less easily conceivable is how the brain could determine and
control which circuits are being engaged by behavior, and thus
28 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
expected to potentiate, and which would be offline, and thus
expected to depress. Taken as a whole, SHY provides a possible
and testable explanation for why sleep, a pervasive behavioral
state of environmental disconnection and opportunity cost,
may be universally needed to address the plasticity-selectivity
and plasticity-stability dilemmas faced by the brain.
ACKNOWLEDGMENTS
This work was supported by NIMH (1R01MH091326 and 1R01MH099231 to
G.T. and C.C.). We thank present and past members of the laboratory and
many colleagues for helpful discussions.
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34 Neuron 81, January 8, 2014 ª2014 Elsevier Inc.
Cerebral Cortex
doi:10.1093/cercor/bhu139
Cerebral Cortex Advance Access published June 23, 2014
Boosting Vocabulary Learning by Verbal Cueing During Sleep
Thomas Schreiner1 and Björn Rasch1,2,3
1
University of Zurich, Institute of Psychology, Zurich, Switzerland, 2Zurich Center for Interdisciplinary Sleep Research (ZiS),
Zurich, Switzerland and 3Department of Psychology, University of Fribourg, Fribourg, Switzerland
Address correspondence to Björn Rasch, Division of Cognitive Biopsychology and Methods, Department of Psychology, University of Fribourg,
Rue P.-A.-Faucigny 2, CH-1701 Fribourg, Switzerland. Email: [email protected]
Keywords: high-density EEG, language, sleep, targeted memory
reactivations, vocabulary learning
Introduction
Language acquisition is a quintessential human trait and fundamental for every-day communication (Pinker 2000). Learning a new language depends essentially on the learning of new
vocabulary, both for learning the native language as an infant
as well as during acquisition of foreign languages in school
children and adults (Shatz 2001). It has been suggested that
sleep may play an important role in language learning (Davis
and Gaskell 2009; Margoliash 2010; Margoliash and Schmidt
2010) possibly due to its beneficial role on memory consolidation (Rasch and Born 2013). Sleep appears to facilitate
memory for abstract relations of words of an artificial language
in infants (Gómez et al. 2006) and benefits the integration of
newly learned words into pre-existing knowledge in both
school children and adults (Dumay and Gaskell 2007; Henderson et al. 2012). More specifically, Gais et al. (2006) demonstrated that the ability of high school students to remember
vocabulary of a foreign language was enhanced when learning
was followed by sleep when compared with wakefulness.
According to the active system consolidation hypothesis, the
beneficial role of sleep on language acquisition is due to a spontaneous and repeated reactivation of newly acquired information during subsequent non-rapid eye movement (NonREM)
© The Author 2014. Published by Oxford University Press. All rights reserved.
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sleep, promoting memory stabilization and integration (Diekelmann and Born 2010; Stickgold and Walker 2013; Genzel et al.
2014). In support of the hypothesis, replay activity during sleep
has been consistently reported in memory-related brain structures in rodents and humans, particularly in the hippocampus
(Pavlides and Winson 1989; Wilson and McNaughton 1994;
Peyrache et al. 2009; O’Neill et al. 2010). In animal models of
language learning, reactivation of song patterns during sleep in
birds is assumed to be critical for song learning during development (Dave and Marholiash 2000), although mechanisms of
memory consolidation during sleep may differ between
mammals and birds, particularly with respect to system consolidation (Rattenborg et al. 2011). Furthermore, a series of recent
studies has shown that experimentally inducing reactivations
during NonREM sleep by using associated memory cues benefits memory consolidation using odors (Rasch et al. 2007; Diekelmann et al. 2011; Ritter et al. 2012; Rihm et al. 2014), sounds
(Rudoy et al. 2009; Dongen et al. 2012), or even melodies
(Antony et al. 2012; Schönauer et al. 2013), including the successful cueing of hippocampal place cells during sleep in
rodents (Bendor and Wilson 2012). In spite of the increasing
evidence for the beneficial role of cueing during sleep on
various memory processes (e.g., Oudiette and Paller (2013)), it
remains an open question whether words can also be used as
memory cues during sleep.
Based on studies using event-related potentials (ERPs), it
has been suggested that the capacity to establish neural representations of stimuli in sensory memory during sleep is preserved (for a review, see Atienza et al. (2001)). For example,
previous studies have shown that several ERP components
(such as the auditory N1, the mismatch negativity, the P3a and
2 sleep-specific components, the N350 and the N550) react to a
variable degree to different features of the stimuli presented
during sleep, such as frequency and significance (e.g., the subjects’ own name) (Brualla et al. 1998; Pratt et al. 1999; Perrin
et al. 2002). However, it is still unknown whether processing
of complex verbal cues during sleep is indeed capable of reactivating associated memories (e.g., the previously learned
translation of the foreign word), thereby benefiting the consolidation of foreign vocabulary. Furthermore, it is still unclear
whether cueing during sleep is purely beneficial or whether it is
associated with “costs” by disturbing ongoing consolidation processes of uncued memories. Finally, the underlying event-related
and oscillatory processes of successful reactivations during sleep
are basically unknown.
In this study, we directly tested the hypothesis that verbal
cueing during postlearning sleep enhances acquisition of
foreign vocabulary. We hypothesized that cueing Dutch words
specifically improves memory for cued words when compared
with uncued words without disturbing consolidation of uncued
words. Furthermore, we predict that the improving effect of
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Reactivating memories during sleep by re-exposure to associated
memory cues (e.g., odors or sounds) improves memory consolidation. Here, we tested for the first time whether verbal cueing during
sleep can improve vocabulary learning. We cued prior learned Dutch
words either during non-rapid eye movement sleep (NonREM) or
during active or passive waking. Re-exposure to Dutch words during
sleep improved later memory for the German translation of the cued
words when compared with uncued words. Recall of uncued words
was similar to an additional group receiving no verbal cues during
sleep. Furthermore, verbal cueing failed to improve memory during
active and passive waking. High-density electroencephalographic recordings revealed that successful verbal cueing during NonREM
sleep is associated with a pronounced frontal negativity in eventrelated potentials, a higher frequency of frontal slow waves as well
as a cueing-related increase in right frontal and left parietal oscillatory theta power. Our results indicate that verbal cues presented
during NonREM sleep reactivate associated memories, and facilitate
later recall of foreign vocabulary without impairing ongoing consolidation processes. Likewise, our oscillatory analysis suggests that
both sleep-specific slow waves as well as theta oscillations (typically
associated with successful memory encoding during wakefulness)
might be involved in strengthening memories by cueing during sleep.
cueing is sleep-specific and does not occur after cueing during
waking. In addition, we tested the hypothesis that event-related
and oscillatory activity associated with cueing during sleep is
predictive for cueing-related gains in vocabulary by recording
high-density electroencephalography (EEG) during sleep.
Materials and Methods
Design and Procedure
Participants entered the laboratory at 21.00 h. The session started with
the application of the electrodes for standard polysomnography,
Vocabulary Learning Task
The vocabulary learning task consisted of 120 Dutch words and their
German translation, randomly presented in 3 learning rounds (word
pairs are listed in the Supplementary Table 1). Dutch words were presented aurally (duration range 400–650 ms) via loudspeakers (70 dB
sound pressure level). In the first learning round, each Dutch word
was followed by a fixation cross (500 ms) and subsequently by a visual
presentation of its German translation (2000 ms). The intertrial interval
between consecutive word pairs was 2000–2200 ms. The subjects were
instructed to memorize as many word pairs as possible. In a second
round, the Dutch words were presented again followed by a question
mark (ranging up to 7 s in duration). The participants were instructed
to vocalize the correct German word or to say, “next” (German translation: “weiter”). Afterward, the correct German translation was shown
again for 2000 ms, irrespective of the correctness of the given answer.
In the third learning round, the cued recall procedure was repeated
without any feedback of the correct German translation. Recall performance of the third round (without feedback) was taken as preretention learning performance. In the third round, participants recalled on
average 60.88 ± 1.1 words (range 40–82 words) of the 120 words correctly, indicating an ideal medium task difficulty (recall performance
Figure 1. Experimental procedure. (a and b) Participants studied 120 Dutch–German word pairs in the evening. Afterward, participants of the main and the control sleep groups
slept for 3 h, whereas 2 other groups stayed awake. During the retention interval, 90 Dutch words (30 prior remembered, 30 prior not remembered and 30 new words) were
repeatedly presented again. Cueing of vocabulary occurred during NonREM sleep, during performance of a working memory task, or during rest. The control sleep group did not
receive any cues during sleep. After the retention interval, participants were tested on the German translation of the Dutch words using a cued recall procedure.
2 Cueing Vocabulary During Sleep
•
Schreiner and Rasch
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Subjects
A total of 68 healthy, right-handed subjects (32 female, mean age =
24.61 ± 0.6) with German mother tongue and without Dutch language
skills participated in the study. Seventeen subjects participated in each
of the 4 experimental groups (e.g., main sleep group, control sleep
group, active waking, and passive waking group). Four subjects had to
be excluded from both sleep groups due to sleeping problems, resulting in 15 participants in each sleep group (main sleep group: 8 female,
mean age = 25.1 ± 1.17 years; control sleep group: 8 female, mean age
= 23.87 ± 0.68), 17 subjects in the active waking group (7 female, mean
age = 24.7 ± 1.11 years), and 17 subjects in the passive waking group
(8 female, mean age = 23.9 ± 0.97 years). Age and gender distribution
did not differ between the experimental groups (both P > 0.75).
None of the participants were taking any medication at the time of
the experiment and none had a history of any neurological or psychiatric
disorders. All subjects reported a normal sleep-wake cycle and none had
been on a night shift for at least 8 weeks before the experiment. Only
subjects with a normal working memory capacity (i.e., minimum
OSPAN score of 20, see task description, page 4) were recruited, due to
the potential impact of working memory capacity on sleep-dependent
declarative memory consolidation (Fenn and Hambrick 2012). On experimental days, subjects were instructed to get up at 7.00 h and were
not allowed to take in caffeine and alcohol or to nap during daytime.
The study was approved by the ethics committee of the Department
of Psychology, University of Zurich, and all subjects gave written informed consent prior to participating. After completing the whole experiment, participants received 120 swiss francs (CHF) (sleep groups)
or 100 CHF (wake groups), respectively.
including electroencephalographic (EEG; 128 channels, Electrical Geodesic, Inc.), electromyographic (EMG), and electrocardiographic (ECG)
recordings. Prior to the experiment, participants of the sleep group
spent an adaptation night in the sleep laboratory.
In all 4 experimental groups, the learning phase started at ∼22.00 h
with the vocabulary learning task (Dutch–German word pairs, for a detailed description see Vocabulary Learning Task section). After completing the learning task, participants of both sleep groups went to bed
at 23.00 h and were allowed to sleep for 3 h, whereas participants in
the 2 wake control groups stayed awake (see Fig. 1, for an overview of
the procedure). During the 3-h retention interval, a selection of the
prior learned Dutch words was presented again during sleep stages N2
and N3 (slow wave sleep, SWS) in the cueing sleep group and during
active or passive waking in the wake control groups for a total duration
of 90 min (see below for a detailed description of the reactivation
phase). In the control sleep group, the same procedure was administered but the selected Dutch words were not replayed during sleep. At
∼2.00 h, subjects of both sleep groups were awakened from sleep
stage 1 or 2 and at ∼2.15 h, recall of the vocabulary was tested in all experimental groups.
Table 1
Overview of memory performance
Cued
Uncued
t
P
29.87 ± 0.09
31.40 ± 0.16
+1.53 ± 0.79
105.15 ± 2.64
33.20 ± 2.54
31.33 ± 2.17
−1.87 ± 0.70
95.43 ± 2.07
−1.29
0.04
3.52
3.43
52.40 ± 0.98
87.33 ± 1.62
2.32 ± 0.15
51.20 ± 1.57
85.33 ± 2.62
2.32 ± 0.17
1.33
0.80
0.00
0.99
30
28.07 ± 0.71
−1.93 ± 0.71
93.55 ± 2.37
31.93 ± 1.84
29.27 ± 1.66
−2.66 ± 0.89
92.80 ± 3.10
−1.04
−0.77
0.79
0.24
0.31
0.45
0.44
0.81
50 ± 1.24
83.33 ± 2.07
2.01 ± 0.13
50.60 ± 1.55
84.33 ± 2.59
2.09 ± 0.16
−0.64
0.53
−0.93
0.36
30.06 ± 0.10
25.71 ± 0.83
−4.35 ± 0.84
85.53 ± 2.81
30.59 ± 2.7
26.12 ± 2.5
−4.47 ± 0.63
84.21 ± 2.16
−0.19
−0.19
0.12
0.56
0.89
0.85
0.90
0.58
50.29 ± 1.05
83.83 ± 1.75
1.44 ± 0.15
49.35 ± 1.55
82.25 ± 2.59
1.39 ± 0.17
0.79
0.43
0.65
0.52
30.35 ± 0.14
24.24 ± 1.14
−6.11 ± 1.41
79.86 ± 4.58
27.82 ± 1.75
22.82 ± 1.78
−5.00 ± 0.59
81.25 ± 2.09
1.46
1.17
−0.79
−0.35
0.16
0.25
0.44
0.74
46.53 ± 1.83
77.54 ± 3.06
1.13 ± 0.17
43.71 ± 1.85
72.84 ± 3.08
0.95 ± 0.17
2.88
0.01*
2.41
0.02*
0.22
0.97
0.003**
0.004**
Data are means ± SEM; Numbers indicate absolute or relative values of correctly recalled or
recognized words that where presented during the retention interval (cued words, 60 in total) or
not (uncued words, 60 in total). For cued recall testing, number of correctly recalled words during
the learning phase before and the retrieval phase after the retention interval are indicated. Change
(% Change) refers to the absolute (relative) difference in performance between learning and
retrieval phases. Hits (% Hits) refers to the absolute (relative) number of correctly recognized
words as “old” (since % Hits = Hits × 100/60, statistics are redundant). The sensitivity measure
d′ reflects recognition performance according to signal detection theory based on the proportion of
Hits and False Alarms (Macmillan and Creelman 2005). *P < 0.05; **P < 0.01.
50.41%) without any danger of ceiling or floor effects. We observed no
difference in preretention memory performance between the 4 experimental groups (main effect of “condition”: F3,60 = 0.86; P = 0.46), no difference in presleep memory performance between later cued and
uncued words (main effect “cueing”: F1,60 = 0.001; P = 0.96) and no
interaction between condition and cueing (F3,60 = 0.41; P = 0.74; see
Table 1 for descriptive statistics).
Reactivation of Vocabulary
In the reactivation phase during the 3-h retention interval, Dutch
words were presented aurally without the German translation. The
presentation occurred via loudspeakers (50-dB sound pressure level).
Of the 120 words learned before the retention interval, 60 words
were cued and 60 were not cued during the subsequent retention
interval. The 60 cued words consisted of 30 words that participants
remembered during the preretention learning phase (cued hits), and
30 words that participants did not remember before the retention interval (cued misses). The words were individually and randomly chosen
for each participant using an automatic MATLAB algorithm. In addition, 30 new words were presented during the retention interval that
had not been included in the preretention learning list, serving as
Main sleep group
Duration (min)
N1
7.76 ± 1.66
N2
93.16 ± 5.93
SWS
62.26 ± 5.8
REM
22.13 ± 3.18
WASO
4.66 ± 1.71
Duration (%)
N1
4.02 ± 0.84
N2
48.70 ± 2.64
SWS
33.11 ± 3.26
REM
11.38 ± 1.59
WASO
2.35 ± 0.82
Number of reactivations
N2
442.86 ± 40.68
SWS
508.80 ± 54.42
Control sleep group
P
5.20 ± 1.46
100.27 ± 4.71
57.93 ± 5.37
22.07 ± 2.73
0.37 ± 0.14
0.16
0.71
0.94
0.37
0.03
2.72 ± 0.70
53.73 ± 2.95
31.13 ± 2.95
11.65 ± 1.36
0.002 ± 0.00
0.31
0.25
0.72
0.89
0.01
–
–
Data are means ± SEM. N1, N2: NonREM sleep stages N1 and N2; SWS, slow wave sleep/N3;
REM, rapid-eye movement sleep; WASO, wake after sleep onset.
control stimuli. Thus, in total, 90 Dutch words were presented during
the retention interval. Presentation occurred every 2.800–3.200 ms in a
randomized order for a total of 90 min, resulting in 10–11 exposures to
each word (see Table 2). The rational of repeated cueing during sleep
was derived from previous studies using olfactory cues which were repeated several times successfully induces memory reactivation during
sleep (Rasch et al. 2007; Diekelmann et al. 2011; Rihm et al. 2014). Furthermore, we aimed at obtaining a sufficient number of trials for detailed EEG analysis. In the main sleep group, exposure to Dutch words
occurred during sleep stages 2 and SWS. Sleep was continuously monitored by the experimenter, and the stimulation was interrupted whenever polysomnographic signs of REM sleep, arousal, or awakenings
occurred. On average, the presentation of Dutch words during sleep
was interrupted 5.2 ± 0.5 times. In the control sleep group, Dutch
words were also classified as “cued” and “uncued” words using the
same procedure as in the main experiment, but the verbal cues were
not administered during sleep. In the active waking group, cueing of
Dutch words occurred during performance on a computerized n-back
task. The 3-h wake retention interval was divided into 30-min periods.
In the first, third, and fifth 30-min period, participants performed on
the n-back task (including a total of 27 67-s blocks of 0-back, 1-back,
and 2-back blocks, in a randomized order, for more details see task description). Subjects were instructed to focus on the task and were given
feedback on accuracy after each 30-min period. While subjects accomplished the n-back task, Dutch words were played in the same manner
as in the sleep group, resulting in a total exposure time of 90 min.
Between the 3 blocks of word reactivation, subjects completed questionnaires and played an online computer game (Bubble shooter). In
the passive waking group, Dutch words were played during passive
waking of the participants, allowing full attention on the replayed
Dutch words. Participants were re-exposed to the Dutch words in the
first, third, and fifth 30-min period of the 3-h retention interval. They
were instructed that they would hear some of the Dutch words again
and should attentively listen to the words. In the remaining 30-min
periods, the participants performed on the n-back task and filled out
questionnaires, without any auditory stimulation.
Recall of Vocabulary after the Retention Interval
During the recall phase, the Dutch words were presented aurally in a
randomized order. In addition to the 120 words included in the preretention learning list, the 30 control words from the reactivation phase
and 30 entirely new words were tested. After listening to the word, participants had to indicate whether the word was old ( part of the learning material) or new. If the current word was recognized as old, they
were asked to give the German translation.
As index of memory recall of German translations across the retention interval, we calculated the relative difference between the number
of correctly recalled words before and after the retention interval, with
the preretention memory performance set to 100%. For recognition
Cerebral Cortex 3
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Main sleep group
Cued recall
Learning
Retrieval
Change
% Change
Recognition
Hits
% Hits
d′
Control sleep group
Cued recall
Learning
Retrieval
Change
% Change
Recognition
Hits
% Hits
d′
Active waking group
Cued recall
Learning
Retrieval
Change
% Change
Recognition
Hits
% Hits
d′
Passive waking group
Cued recall
Learning
Retrieval
Change
% Change
Recognition
Hits
% Hits
d′
Table 2
Sleep and reactivation parameter
memory of Dutch words, we calculated the sensitivity index d′ [i.e.,
z(Hits) – z(False Alarms)] according to signal detection theory. Proportions of 0 and 1 were replaced by 1/2N and 1-1/2N, respectively, with
N representing the number of trials in each proportion (i.e., N = 60,
see Macmillan and Creelman (2005)). The memory indices for
cued recall and recognition were calculated separately for cued and
uncued words.
OSPAN Task
The OSPAN task was administered to assess the subjects’ working
memory capacity (Unsworth et al. 2005). Each trial included an equation succeeded by a letter. The subjects had to indicate if the answer to
a given equation was correct and had to remember the letter afterwards. Every 3–6 trials, 12 letters appeared on the screen and subjects
had to select those that had been shown before.
Sleep EEG
Sleep was recorded by standard polysomnography including EEG,
EMG, and ECG recordings. EEG was recorded using a high-density
128-channel Geodesic Sensor Net (Electrical Geodesics, Eugene, OR,
USA). High-density EEG was used to obtain a reliable estimation of
possible topographical distributions to the reactivation-related effects.
Impedances were kept below 50 kΩ. Voltage was sampled at 500 Hz
and initially referenced to the vertex electrode (Cz). Additionally to the
online identification of sleep stages, polysomnographic recordings
were scored offline by 3 independent raters according to standard criteria (Iber et al. 2007). In order to exclude the possibility of sleep
onsets in the waking groups, EEG of the waking reactivation phase
was also scored offline.
Event-Related Potentials
Offline EEG analysis was realized using Brain Vision Analyzer software
(version: 2.0; Brain Products, Gilching, Germany). Data were rereferenced to averaged mastoids, low-pass filtered with a cutoff frequency of 30 Hz (roll-off 24 dB per octave), and high-pass filtered with
a cutoff frequency of 0.1 Hz (roll-off 12 dB per octave). The EEG data
were epoched into 1700 ms segments beginning 200 ms before stimulus onset. The 200-ms interval preceding stimulus onset served as baseline and was used for baseline correction. Epochs were categorized
based on performance between pre- and postsleep tests yielding the
following categories of ERPs: first, we analyzed ERPs for later remembered when compared with later forgotten cued words. In addition,
we separated later remembered words in “Gains” (i.e., cued Dutch
words not remembered before sleep but correctly recalled after sleep)
and “HitHit” words (i.e., cued Dutch words remembered before and
after sleep). Later forgotten words were separated in “Losses” (i.e.,
cued words correctly retrieved before sleep but not remembered after
sleep) and “MissMiss” words (i.e., cued Dutch words not remembered
before and after sleep). The control stimuli presented during the retention interval entered the category “Control.”
Signal averaging was carried out separately per subject and per condition and grand averages of all conditions were calculated. For statistical
analysis, average EEG amplitudes measured over the interval from 800
to 1.100 ms after stimulus onset were compared. To protect against error
inflation due to multiple testing of multiple electrodes, we used a false
discovery rate of P < 0.05. For illustration of the results, we present the
ERP of the electrode with the highest significance (for sleep stagespecific ERP analyses, see Supplementary Results and Fig. 2).
4 Cueing Vocabulary During Sleep
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Analysis of Power Changes
We analyzed average power differences between Gains and Losses
using a fast Frequency Transformation implemented in Brain Vision
Analyzer with a Hanning Window of 10% during the 2.5 s after each
word. Power values were analyzed for slow spindle activity (11–13 Hz)
and fast spindle activity (13–15 Hz), as these frequency bands have
been implicated in processes of memory consolidation (Antony et al.
2012; Fuentemilla et al. 2013; Rasch and Born 2013; Cairney et al.
2014). Frequency bands corresponding to slow wave activity (0.5–4
Hz) were not measured because of the limited number of possible
cycles in the short trial length and border effects.
Theta oscillations (5–7 Hz) were analyzed using a Continuous
Wavelet Transformation as implemented in Brain Vision Analyzer
(complex Morlet waveform, frequency range from 5 to 7 Hz in 10 logarithmic steps, Morlet parameter c = 7). In order to avoid edge effects,
the trials entering the wavelet transform were segmented from −0.7 to
1.9 s with respect to stimulus presentation. An interval of 0.4 s at the
beginning and the end of the trials was discarded afterward. A total of
both induced and evoked activity was calculated by performing the
wavelet analysis on single trials, after normalization with respect to
the prestimulus time window from −300 to −100 ms (for the results of
the total theta power calculation see Supplementary Fig. 1). Subsequently, the resulting single-trial frequency spectra were averaged. This
procedure provides the overall power of a given frequency range. In
order to obtain the induced power, which is thought to play a role in
binding distributed cortical representations (Düzel et al. 2005), we subtracted the theta effects of the average ERP (evoked power) from each
single trial before calculating the time–frequency analysis and averaging
the single trials. Statistical analysis was performed for a time window of
700–900 ms after stimulus onset. Additionally, the same procedure was
performed for slow spindles (11–13 Hz) and fast spindles (13–15 Hz),
due to their assumed involvement in processes of sleep-dependent
memory consolidation (for sleep stage-specific oscillatory analyses, see
Supplementary Results and Fig. 3). As with the calculation of average
oscillatory activity, frequency bands corresponding to slow wave activity (0.5–4 Hz) were not measured because of the limited number of possible cycles in the short trial length and border effects.
Statistical Analysis
Data were analyzed using repeated-measures analyses of variance
(ANOVA). Where appropriate, significant interactions were further
evaluated with Fisher’s least significant difference post hoc tests. The
level of significance was set to P = 0.05.
Results
Effects of Verbal Cueing on Memory for Dutch
Vocabulary
As expected, re-exposure to Dutch words improved later
memory for the German translation of the cued words, when
cueing occurred during sleep. Participants correctly recalled
105.14 ± 2.64% of the cued words, whereas only 95.43 ± 2.07%
of the uncued words were remembered after sleep, with
memory performance before sleep set to 100% (Fig. 2, see
Table 1 for absolute values). The improvement of almost 10%
points of vocabulary learning by cueing during sleep when
compared with uncued words was highly significant (t14 = 3.43;
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n-Back Test
Subjects of both waking groups accomplished intermixed 0-, 1-, and
2-back versions of the n-back working memory task (Gevins and
Smith 2000). In this task, different letters appear successively in the
center of the screen. In the 0-back version, subjects had to press a key
whenever the letter “x” appeared on the screen. In the 1-back version,
subjects had to respond to a letter repetition (h-f-f-k), while the 2-back
version requires subjects to respond to a letter repetition with one
intervening letter (h-f-s-f ).
Slow Oscillations Analysis
Artifact-free EEG data, ranging from −300 to 1500 ms with respect to
the gain and loss trials, were low-pass filtered at 30 Hz and band-pass
filtered between 0.5 and 4.0 Hz (stopband 0.1 and 10 Hz) using a Chebyshev Type II filter (MATLAB, The Math Works, Inc., Natick, MA,
USA). Slow oscillations were then identified visually at electrode site Fz
as well as electrode sites F3 and F4 as waves of a total duration >500
ms and a minimal amplitude of 75 µV, starting in a time window
between 0 and 800 ms poststimulus.
P = 0.004). In fact, cueing during sleep even induced a 5% increase in memory for cued Dutch words above presleep performance levels, and this increase reached a statistical trend
(+5.14 ± 2.64%; P = 0.072, one-sample t-test, two-sided). In contrast, German translations of uncued Dutch words were significantly more forgotten when compared with recall performance
before sleep (−4.75 ± 2.07%; P = 0.045). Thus, reactivation of vocabulary during sleep did not only prevent forgetting of
German translations, but showed a trend of improving memory
beyond baseline levels. On the individual level, 12 of 15 participants benefited from cueing (range +1 to +11 words, for the absolute difference between cued and uncued words), whereas 3
participants did not (range 0 to −1 words).
To test whether the observed benefits of cueing during
sleep disturbed the consolidation of uncued words or not, we
conducted an independent control experiment without presenting any verbal cues during sleep after learning (sleep
control group). After learning, words were also classified as
cued and uncued words using the same algorithm as in the
main experiment (see Materials and Methods), but no verbal
cues were replayed during sleep. As expected, recall of words
classified as cued and uncued did not differ (93.55 ± 2.37 vs.
92.80 ± 3.10%; t14 = 0.24; P = 0.81). More importantly, memory
performance in the sleep control group after sleeping without
any verbal cues was highly comparable with the recall performance for uncued words observed in the main experiment
with verbal cues during sleep (93.55 ± 2.37 vs. 95.43 ± 2.07%;
t14 = 0.71; P = 0.48), and was significantly lower when compared with memory for cued words (92.80 ± 3.10 vs.
105.14 ± 2.64%; t14 = 3.26; P = 0.003, Fig. 2, see Table 1, for absolute values).
In the 2 waking groups, cueing did not reveal any beneficial
effect on memory for Dutch vocabulary, neither in the active
waking group (85.53 ± 2.8 vs. 84.2 ± 2.16%, for cued and
uncued words, respectively; t16 = 0.56; P = 0.58) nor in the
passive waking group (79.86 ± 4.58 vs. 81.25 ± 2.09%), for
cued and uncued words, respectively, t16 = −0.35, P = 0.74; see
Table 1 for absolute values). Thus, even with the availability of
Sleep and Cueing
The beneficial effect of cueing on memory during NonREM
sleep cannot be explained by general alterations in sleep as the
effect was specific for cued when compared with uncued
words, while the general improving effect of sleep on memory
was present for both word categories. Sleep architecture was
not altered by cueing, as sleep parameters recorded in the
main sleep group did not differ from those of the control sleep
group (see Table 2). In addition, we did not observe any increases in alpha power 1000 ms before (indicative of brief awakenings (Rudoy et al. 2009)) and after the auditory stimulation
at electrode site Oz, excluding that cueing of words induced
short lasting arousal responses (alpha power before
(2.12 ± 0.41 μV) and after the auditory cue (2.01 ± 0.5 μV), respectively, t14 = 0.31, P = 0.75). Still participants of the main
sleep group spent more time awake then subjects of the
control sleep group (4.66 vs. 0.55 min; t14 = 2.86, P = 0.013), indicating that auditory cueing slightly interrupted sleep. Note
that auditory presentation of words was stop whenever signs
of arousal or awakenings were detected. Importantly, performance levels of uncued words in the main sleep group and in
the sleep group without cueing were almost identical, indicating that increases in wake time did not impair ongoing and
spontaneous processes of memory consolidation.
Cerebral Cortex 5
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Figure 2. Behavioral results. In the main sleep group, memory for cued word pairs
(black bar) was significantly improved when compared with uncued pairs (white bar).
Recall of uncued word pairs in the main sleep group was comparable with recall
performance of word pairs in the control sleep group, which did not receive any cues
during sleep. No enhancing effects of cueing on later memory retrieval occurred in
both waking control groups. Retrieval performance is indicated as percentage of
recalled German translations with performance before sleep set to 100%. Values are
mean ± SEM. **P ≤ 0.01.
attentive processing resources in the passive waking group, reexposure to Dutch words during waking failed to improve
memory for the German translations.
In addition to sleep-specific improvement by cueing, recall
of German translation was generally better in the 2 sleep
groups when compared with the 2 waking control groups, reflecting the well-known beneficial effect of retention intervals
filled with sleep when compared with waking on memory consolidation (main effect condition; F3,60 = 13.06; P < 0.001; see
Fig. 2). Post hoc tests revealed that recall performance in both
sleep groups independent of cueing was better when compared with the active waking and the passive waking group
(t62 = 5.61; P < 0.001).
While cueing during sleep improved memory for German
translation of Dutch words as tested by cued recall, we observed no sleep-specific benefit of cueing on recognition of
Dutch words. The interaction remained nonsignificant
(F3,60 = 1.35; P = 0.15). However, sleep improved recognition
of Dutch words independently of cueing (main effect condition; F2,46 = 15.87, P < 0.001): both sleep groups showed a significantly higher recognition performance (main sleep group:
d′ = 2.32 ± 0.13; sleep control group: d′ = 2.04 ± 0.14) when
compared with the active waking group (d′ = 1.42 ± 0.16) and
the passive waking group (d′ = 1.05 ± 0.16; all P < 0.001), while
neither the 2 waking groups (P = 0.10) nor the 2 sleep groups
(P = 0.68) differed significantly among each other. In fact, recognition of cued and uncued Dutch words was basically identical in the main sleep group (see Table 1), safely excluding that
recognition testing prior to cued recall might have confounded
the reported beneficial effect of cueing during sleep as tested
by cued recall. While cueing also did not affect recognition in
the active waking group, cued words were better recognized in
the passive waking group in an exploratory analysis, possibly
reflecting the fact that the participants in the latter group attended the cued Dutch words during the retention interval
(see Table 1).
We did not observe any significant associations between the
memory advantage induced by cueing (i.e., by subtracting
memory for cued minus uncued words (Antony et al. 2012))
and the relative time spent in a certain sleep stage (N1: r = 0.18,
P = 0.50; N2: r = −0.360, P = 0.18; SWS: r = 0.18, P = 0.51; REM:
r = 0.24, P = 0.93). Cueing was monitored online and was
restricted to sleep stages N2 and SWS. The total number of
cueings did not differ between N2 and SWS (Table 2), and we
did not observe any significant association between the
memory advantage induced by cueing and number of cueings
in N2 or SWS (N2: r = −0.39, P = 0.14; SWS: r = 0.1, P = 0.72; for
a more detailed description and analysis see Supplementary
Table 2 and Results). Additionally, EEG offline scoring of the
waking groups revealed no signs of sleep onsets, indicating
that the subjects of both waking groups were awake throughout the reactivation phase.
6 Cueing Vocabulary During Sleep
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Neural Correlates of Cueing During Sleep
In order to characterize the process of cueing on a neural
basis, we analyzed ERPs and oscillatory responses to vocabulary cues during sleep. First, we analyzed ERPs for later remembered when compared with later forgotten cued words.
In addition, we separated later remembered words in Gains (i.
e., cued Dutch words not remembered before sleep but correctly recalled after sleep) and HitHit words (i.e., cued Dutch
words remembered before and after sleep). Later forgotten
words were separated in Losses (i.e., cued words correctly retrieved before sleep but not remembered after sleep) and MissMiss words (i.e., cued Dutch words not remembered before
and after sleep). Please note that the categories Gains and
Losses reflect a clear behavioral change after cueing, therefore
best representing the neural pattern associated with processes
underlying successful versus unsuccessful cueing for later
memory retrieval. In contrast, neural correlates of HitHit and
MissMiss words are more difficult to interpret, as cueing during
sleep might be ineffective for sufficiently strong memory traces
(cases of HitHit) or nonexisting associations (cases of “LossLoss”) after encoding before sleep (for the behavioral analysis
of Gains and Losses please see Supplementary Results and
Table 2).
Remarkably, the EEG analysis of the average ERP amplitudes
in the main sleep group clearly revealed a more pronounced
negativity for subsequently remembered versus subsequently
forgotten cued words at electrode site Fz (t14 = −2.85, P = 0.013).
We further explored this difference by separately analyzing
Gains and “HitHits” as well as Losses and MissMiss. Similar to
the previous analysis, the difference between the ERP responses
associated with HitHits when compared with “MissMisses”
was significant (t14 = 2.45, P = 0.028). More importantly, we
observed the largest negative amplitude associated with
cueing of “Gain” words. Neural correlates of Gains represent a
memory gain induced by cueing during sleep (i.e., successful
verbal cueing during sleep), and the amplitude was significantly increased when compared with all other word categories at
electrode site Fz in a time interval from 800 to 1100 ms after
word onset (F6,84 = 4.52, P = 0.001), all pairwise post hoc tests
P < 0.04, see Fig. 3a,b). As Losses are the most suitable control
category for Gains (i.e., behavioral change in memory induced
by cueing, relatively similar number of occurrences, etc.), we
focused on the comparison between Gains and Losses in all
subsequent analyses.
The analysis of all electrode revealed that the amplitude difference between Gains and Losses had a stable fronto-central
distribution (see Fig. 3c) comparable with distributions of subsequent memory effects observed during waking (WerkleBergner et al. 2006). Furthermore, in a single-trial analysis, we
counted the number of clearly identifiable slow waves (negative amplitude >75 μV with a duration of >500 ms starting in a
time window 0–800 ms poststimulus, see Materials and
Methods) that followed cueing of Gain words when compared
with Losses during sleep. This analysis revealed, that Gains
were significantly more often followed by slow oscillations
(31.09 ± 3.6% of all cueing trials of Gains) when compared
with Losses (18.48 ± 3.4% cueing trials of Losses; t14 = 5.35,
P < 0.001). This result was found at electrode site Fz, as well as
F3 and F4 indicating a stable frontal distribution of this effect.
This result is compatible with the assumption that the presence
of a slow oscillation after the presentation of a Dutch word
during sleep plays an important role for successfully stabilizing
the associated memory trace, reactivated by the memory cue
presented during sleep. As both slow oscillations and sleep
spindles are critically involved in processes of memory consolidation during sleep (Rasch and Born 2013), we also analyzed
possible differences in average oscillatory power between
Gains and Losses for slow spindles (11–13 Hz) and fast
spindle activity (13–15 Hz). However, we did not observe
any difference between Gains and Losses in this analysis (all
P > 0.10).
We further explored difference between Gains and Losses
in time–frequency space. We controlled for a possible contribution of the evoked brain response by subtracting the
average ERP (evoked power) from each single trial before calculating the time–frequency analysis (induced power) (Klimesch et al. 1998). In contrast to our expectations, the time–
frequency analysis revealed no significant increase in oscillatory power in the spindle band related to Gains versus
Losses, neither in the fast spindle band (13–15 Hz) nor in the
slow spindle band (11–13 Hz). However, sleep stage-specific
analyses revealed a significant increase in slow spindle power
during SWS (but not during stage N2) in a time window 600–
800 ms after the cue (P < 0.05, for details see Supplementary
Results and Fig. 3). Please note that the analysis of power
changes in the slow oscillations/delta band was not possible
due to the relatively small intertrial interval between verbal
cues.
Finally, we also analyzed power changes for the theta band.
Theta activity is prevalently linked to successful memory encoding during waking (Nyhus and Curran 2010) and poststimulus
increases in induced theta power have been specifically linked
to processes of recollection (Düzel et al. 2005). Interestingly,
induced theta power associated with verbal cueing during
sleep differed significantly between conditions (F4,56 = 7.38,
P = 0.002). Gains were associated with an increase in induced
theta power in a time window of 700–900 ms after stimulus
onset. The increase in induced theta power was particularly
strong in right frontal as well as left parietal electrodes (e.g.,
electrode FC6: t14 = 3.68; P = 0.009), strongly suggesting that a
transient increase in theta power is critical for successful cueing
during sleep (see Fig. 3d–f; see Supplementary Fig. 1 for total
power changes). Interestingly, increases in theta activity for
Gains when compared with Losses were more pronounced
during stage 2 sleep, but were also reliably observed during
SWS (see Supplementary Results and Fig. 3).
Discussion
Our findings show for the first time that cueing prior learned
foreign vocabulary during sleep improves later recall. Furthermore, memory performance for uncued words in the main
sleep group resembled memory performance of participants
who did not receive any verbal cues during sleep, suggesting
that cueing led to a real gain in memory performance. In addition, successful cueing during sleep, which resulted in later
memory gains during retrieval testing, was associated with an
increased late negativity and increased theta activity during
NonREM sleep.
The beneficial effect of cueing during sleep is consistent with
the active system consolidation hypothesis, which assumes that
spontaneous memory reactivations during sleep are critical for
the enhancing effect of sleep on memory consolidation. In fact,
recent studies have successfully used memory-associated odors,
sounds, or melodies (Rasch et al. 2007; Rudoy et al. 2009;
Antony et al. 2012) to cue and strengthen memories during
sleep. Here, we go an important step beyond these previous
results by showing that also complex stimuli like foreign vocabulary can be successfully used to reactivate memories during
sleep, leading to an enhanced memory for vocabulary the next
day. Importantly, our results are highly relevant for vocabulary
learning in an educational setting, because our procedure of reactivating foreign vocabulary could be easily applied to these
every-day learning contexts. However, as retrieval was tested in
the night after only a few hours of sleep in the current study,
future studies should test the memory-improving effects of
cueing during sleep the next day or after several days. In addition, it still needs to be determined whether or not the beneficial effects of cueing during sleep are possibly accompanied by
any detrimental effects on sleep-dependent memory consolidation of other material learned during the day. Finally, future
studies need to examine whether cueing of vocabulary during
sleep indeed facilitates foreign language learning.
In our experiment, we explicitly chose Dutch as a foreign
language to achieve sufficiently few learning trials required for
our analysis. Due to the close relation of Dutch to German or
English, German-speaking participants could more easily learn
the vocabulary and might even be able to correctly guess the
meaning of some words. However, guesses cannot explain our
reported improved effect of cueing during sleep, as words
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Figure 3. Electrophysiological results. ERPs and oscillatory theta power recorded during cueing in the sleep group were computed for words, for which cueing during sleep led to a
change in memory performance. “Gains” reflect cued words not remembered in the presleep test but correctly recalled in the postsleep test. “Losses” refer to cued words
remembered in the presleep test but not in the postsleep test. Words remembered before and after the retention interval were labeled “HitHit” and words not remembered both
before and after the retention interval were labeled “MissMiss.” The new 30 Dutch words formed the “Control” condition. (a and b) Successful cueing was associated with a more
pronounced negativity at frontal electrode sites (representative electrode Fz). The rectangle illustrates the time window used for waveform quantification. (c) Scalp map representing
the topographical distribution for the difference between “Gains” and “Losses” in the time window between 800 and 1100 ms, indicating a pronounced frontal distribution (all
electrodes entered the analysis; black dots indicate significant electrodes at P < 0.05, false discovery rate) corrected for multiple comparisons). The following electrodes were
significant: E4, E5, E6, E11, E12, E13, E16, E19, E20, E23, E24, E28, E29, E35, E112 (see Supplementary Fig. 2 for the exact electrode positions). (d and e) Induced theta power for
the difference between “Gains” and “Losses” (electrode FC6), indicating a distinct increase in induced theta power associated with successful cueing. (f ) Scalp map depicting the
distribution of theta power increase for “Gains” relative to “Losses” in the time window between 700 and 900 ms. The following electrodes were significant: E53, E60, E61, E62,
E111, E117 (FC6), E118). **P ≤ 0.01.
8 Cueing Vocabulary During Sleep
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Schreiner and Rasch
serve the same function as replay during NonREM sleep, as inducing reactivation during this behavioral state does not
improve memory at least in humans. The lack of a memory
effect by cueing during wakefulness is well in line with recent
findings emphasizing the critical role of active and effortful retrieval to strengthen memories during wakefulness, whereas
pure repeated study of words (without active retrieval testing)
is not sufficient to improve memory (Karpicke and Roediger
2008). Please note that cued words were played rather fast in
our study (one word every 3 s), possibly not leaving enough
time for active retrieval attempts.
Still our results concerning the sleep specificity and the lack
of beneficial effects of cueing in the waking groups should be interpreted with caution, because reactivation in both wake
groups occurred during the night (11.00–02.00 AM) to exclude
circadian factors on learning and retrieval. Thus, tiredness by
partial sleep deprivation might have influenced the effects of
cueing on memory performance. However, young participants
(and particularly students) are typically quite used to stay up
until 2.00 AM on weekends, so we consider the possible impact
of tiredness on memory performance in the wake groups to be
rather small. Furthermore, even if testing participants in the
afternoon would result in a beneficial effect of cueing on
memory, one could speculate that the underlying processes of
this advantage are different from those acting during sleep:
partial sleep deprivation mostly affects prefrontal functions like
attention, working memory and possibly also task-related motivation. These processes are apparently not relevant for the benefits of cueing during sleep. One might hypothesize that cueing
during sleep appears to benefit memory consolidation in an
automatic, effortless und involuntary way, whereas benefits of
cueing during wakefulness might possibly depend on the availability of attentional resources, high motivation, and active reencoding of cued words. In contrast to this hypothesis, a recent
study demonstrated beneficial effects of cueing in the afternoon
during performance of a working memory task (Oudiette et al.
2013), possibly suggesting that cueing during wakefulness might
improve memory even in the absence of attentional resources.
Thus, an alternative explanation could be that the beneficial
effects of cueing during wakefulness depend on an optimal circadian time, and that cues delivered during wakefulness at nighttime cannot be successfully processed as the brain is already
overloaded by information encoded during prolonged prior
wakefulness. As the memory mechanisms underlying cueing
during wakefulness are still unclear, further investigation regarding the sleep specificity of cueing benefits are clearly needed.
In contrast to previous reactivation studies, we administered
reactivation cues during both N2 sleep and SWS instead of restricting reactivation to SWS. The rational for including N2 sleep
was that 1) reactivation studies in rats do not differentiate
between N2 sleep and SWS and 2) no previous reactivation
study in humans has explicitly tested the effects of reactivation
during N2 sleep on memory. Thus, we included N2 to obtain
more time for repeated reactivation of Dutch words. In our
view, early N2 sleep and SWS differ rather quantitatively (with
respect to the occurrence of slow oscillations) than qualitatively,
and our results suggest that cueing during N2 sleep might have
at least no detrimental effects or even support memory consolidation during sleep.
In accordance to the active system consolidation, which
assumes a critical role of slow oscillatory activity in synchronizing
hippocampal memory reactivations with thalamo-cortical spindle
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were randomly assigned to the cued and uncued conditions.
Furthermore, we can exclude that cueing simply increased perceptual fluency (Jacoby and Dallas 1981), because mere exposure to the words during waking similarly increases perceptual
fluency and had no effect on memory for the vocabulary in our
study. Still, the degree of prior knowledge of related languages,
learning difficulty, and memory strength during encoding might
be important factors determining the effectiveness of cueing
during sleep, requiring further examination. Most importantly,
the close relationship of the languages Dutch and German
might have considerably affected the successful effect of cueing
during sleep in our study. Thus, replicating our results with
more distant languages is necessary to generalize our findings.
In contrast to the beneficial effect of cueing during sleep on
recall of German translations, recognition of Dutch words was
not affected by cueing during sleep. This result suggests that
cueing during sleep specifically strengthens the association
between the Dutch words and the German translations in
memory, thereby facilitating later recall. However, recognition
was only tested once (and not before and after the retention
interval), which might have reduced the sensitivity of this test
for possible beneficial effects of cueing during sleep on
memory consolidation. Importantly, the null effect on recognition safely excludes that the reported beneficial effect of cueing
during sleep on later recall might be confounded by prior recognition testing or higher familiarity with the cued words. Interestingly, sleep in general (independent of cueing) improved both
recognition of Dutch words and recall of German translations,
suggesting a broader role of sleep in memory consolidation
when compared with experimental cueing during sleep.
Moreover, our results provide first evidence that the beneficial effects of cueing during sleep exceed the normal consolidation effects of sleep on memory, since recall of uncued words in
the main sleep group was almost identical to memory performance of sleeping control participants who did not receive any
cues during sleep. Thus, verbal cueing during sleep appears to
benefit later recall of cued memory associations without disturbing ongoing consolidation processes during sleep. Hence, from
a behavioral level, it appears as if the beneficial effect of cueing
during sleep on memory occurs without any obvious costs.
However, future studies in animal models or using intracranial
recordings might additionally examine, in order to get a more
comprehensive view, whether verbal cueing during sleep does
not interfere with ongoing reactivation and consolidation processes also on the neural level. In contrast to our finding for
verbal cues, others (Antony et al. 2012; Schönauer et al. 2013)
reported some evidence for costs of cueing of procedural memories during sleep, as performance on the uncued sequence
after receiving cues during sleep was lower when compared
with performance in a separate group which did not receive any
cues during sleep. Also here, future studies need to determine
the mechanisms underlying a potential biasing of consolidation
processes of cueing procedural memories during sleep when
compared with the benefits of verbal cueing during sleep.
In the wake groups, the lack of beneficial memory effects by
cueing was independent of the availability of attentional resources: both unattended cueing (active wake group) as well
as attended cueing ( passive wake group) during wakefulness
failed to improve later retrieval of cued words. Thus, even
though several rodent studies have reported the existence of
spontaneous replay activity during periods of quiet ( passive)
waking (Gerrard et al. 1986; Kudrimoti et al. 1999), it may not
In general, the results reported here also indicate that
complex auditory cues like foreign vocabulary are indeed
capable of reactivating associated memories during sleep, suggesting that some processing of the presented words is preserved during sleep (at least to some extent). Similarly,
previous studies presenting verbal material during sleep have
suggested a preserved capacity to discriminate semantic incongruency as well as the participants own name from other
names during sleep (Brualla et al. 1998; Perrin et al. 1999; Pratt
et al. 1999; Ibáñez et al. 2006). The successful reactivation of
memories during NonREM sleep was accompanied by an increased negativity over frontal brain regions, resulting in improved retrieval after sleep. The observed time interval, as well
as the frontal topography associated with this “subsequent reactivation effect,” is similar to ERPs typically observed during
encoding for later remember items (i.e., the subsequent
memory effect). In particular, an increased negativity has been
reported during encoding of subsequently remembered stimuli
using auditory presentations (Cycowicz and Friedman 1999;
Guo et al. 2005), whereas subsequent memory for visually presented items is typically accompanied by more positive going
ERPs in prefrontal and medio-temporal regions (Friedman and
Johnson 2000; Werkle-Bergner et al. 2006). In spite of these
morphological similarities, it remains an open question
whether neural generators and mechanisms underlying the
subsequent reactivation effect observed during sleep are
indeed similar to processes underlying encoding and retrieval
during wakefulness.
To better understand the underlying function of the reported enhanced late negativity associated with successful
cueing during sleep, we can only refer to studies using auditory stimuli to investigate the extent of information processing
during sleep. Some of those studies focused on the formation
of stimulus representations in sensory memory by performing
different kinds of oddball paradigms (for a review see Atienza
et al. 2001). In a study by Niiyama et al. (1995), participants
were trained to react to rare sound stimuli during wake. Reexposure to rare sounds during sleep stage N2 was associated
with an enhanced late negativity over frontal electrodes
(labeled as N350 and N550) when compared with frequent
tones. The authors interpreted this component as part of elicited K-complexes, which might reflect a certain level of information processing. In a similar oddball study (Karakaş et al.
2007), the same results concerning the late negativity with
regards to rare stimuli were obtained during sleep stage N2
and even SWS. Additionally, the authors reported that enhanced theta power was associated with the processing of rare
stimuli, suggesting that theta power during sleep might be
related to sensory/attentional processing of auditory stimuli.
However, it is still a matter of debate whether these findings
are really specific for sensory memory (Ibáñez et al. 2009). Our
results extend this interpretation by suggesting that large negativities after auditory stimuli presented during sleep might also
support processes of long-term memory formation.
In sum, our results demonstrate that cued reactivation of
foreign words during sleep enhances vocabulary learning and
that these processes are accompanied by distinct neuronal activities which involve sleep-specific slow oscillatory mechanism but possibly also share some properties with theta-related
oscillations typically observed during successful encoding
during wakefulness. Our findings suggest that verbal cueing of
foreign vocabulary during postlearning sleep might be an
Cerebral Cortex 9
Downloaded from http://cercor.oxfordjournals.org/ at Universitaet Zuerich on June 28, 2014
activity (Bergmann et al. 2012; Dongen et al. 2012; Ritter et al.
2012; Oudiette et al. 2013; Rasch and Born 2013; Rihm et al.
2014), successful cueing in our study was accompanied by an increased number of poststimulus slow oscillations. However, and
in contrast to our expectations, this difference was not accompanied by an increase in sleep spindle activity, when analyzing sleep
stage N2 and SWS together. Interestingly, the SWS-specific analysis revealed enhanced oscillatory power in the slow spindle
band (11–13 Hz) succeeding the replay of Gains with regards to
Losses. Both slow and fast sleep spindles have been related to
memory improvement (e.g., Schabus et al. 2008), while some
recent study claimed that especially slow spindles during SWS
seem to play a crucial role for memory consolidation (Cox et al.
2012), which led the authors to suggest that the possible potentiating effects of spindles for memory consolidation are tied to
their co-occurrence with slow oscillations. This interpretation
would fit to our data, since successful cueing was, as mentioned
above, accompanied by an increased number of poststimulus
slow oscillations as well as an enhanced oscillatory power in the
slow spindle band.
Slow oscillations have been shown to play a causal role in
processes of declarative memory consolidation during sleep
(Marshall et al. 2006; Ngo et al. 2013), and might therefore also
provide an important temporal time frame for stabilizing and
consolidating externally induced memory reactivations by
verbal cueing. To further examine the exact temporal relationships between verbal cueing during sleep and slow oscillations, future studies will need to systematically vary the onset
of verbal cues presented during sleep in accordance to the up
and down states of the ongoing slow oscillations.
Additionally, the results of the EEG time–frequency analysis
indicate that successful cueing during sleep (i.e., cueings
leading to enhanced memory performance) is accompanied by
poststimulus increase in induced theta power at right frontal
and left parietal regions. Induced theta during waking has
been linked to the encoding and retrieval of new declarative information (Klimesch 1999; Nyhus and Curran 2010). In addition, theta oscillations have been suggested to play a
functional role in controlling, maintaining and storing memory
content during wakefulness (Nyhus and Curran 2010; Lisman
and Jensen 2013 for reviews). During sleep, ongoing theta
rhythms have been mainly associated with hippocampal activity during REM sleep, whereas the role of theta activity during
NonREM sleep is less clear (Cantero et al. 2003). However,
some recent studies have indeed implicated theta activity
during NonREM sleep in processes of memory consolidation.
Faster theta frequency or increased theta power during
NonREM sleep predicted better subsequent memory performance in patients with Alzheimer’s disease or amnestic mild cognitive impairment (Hot et al. 2011; Westerberg et al. 2012).
Schabus et al. (2005) observed a similar results pattern in
healthy subjects, leading to the author’s speculation that increased theta activity during NonREM sleep might be associated with the reactivation of newly encoded information and
as a consequence with improved memory performance. Our
results partly support this notion emphasizing the importance
of increases in theta power after reactivation for successful
memory consolidation during sleep. However, whether these
processes observed during sleep are indeed similar to theta increases underlying successful memory encoding during wakefulness and whether or how they relate to hippocampal theta
rhythms require further examination.
efficient and effortless tool to improve foreign vocabulary
learning in educational settings as well as every-day life.
Supplementary Material
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org/.
Funding
This work was supported by a grant from the Swiss National
Foundation (SNF) (PP00P1_133685) and the Clinical Research
Priority Program “Sleep and Health” of the University of
Zurich.
Notes
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REVIEWS
sleep
The memory function of sleep
Susanne Diekelmann and Jan Born
Abstract | Sleep has been identified as a state that optimizes the consolidation of newly
acquired information in memory, depending on the specific conditions of learning and the
timing of sleep. Consolidation during sleep promotes both quantitative and qualitative
changes of memory representations. Through specific patterns of neuromodulatory activity
and electric field potential oscillations, slow-wave sleep (SWS) and rapid eye movement
(REM) sleep support system consolidation and synaptic consolidation, respectively. During
SWS, slow oscillations, spindles and ripples — at minimum cholinergic activity — coordinate
the re-activation and redistribution of hippocampus-dependent memories to neocortical
sites, whereas during REM sleep, local increases in plasticity-related immediate-early gene
activity — at high cholinergic and theta activity — might favour the subsequent synaptic
consolidation of memories in the cortex.
Declarative memory
Memories that are accessible
to conscious recollection
including memories for facts
and episodes, for example,
learning vocabulary or
remembering events.
Declarative memories rely on
the hippocampus and
associated medial temporal
lobe structures, together with
neocortical regions for
long-term storage.
Procedural memory
Memories for skills that result
from repeated practice and
are not necessarily available
for conscious recollection, for
example, riding a bike or
playing the piano. Procedural
memories rely on the striatum
and cerebellum, although
recent studies indicate that the
hippocampus can also be
implicated in procedural learning.
University of Lübeck,
Department of
Neuroendocrinology,
Haus 50, 2. OG, Ratzeburger
Allee 160, 23538 Lübeck,
Germany.
Correspondence to J. B.
e‑mail:
[email protected]‑luebeck.de
doi:10.1038/nrn2762
Published online
4 January 2010
Although sleep is a systems-level process that affects
the whole organism, its most distinctive features are the
loss of behavioural control and consciousness. Among
the multiple functions of sleep1, its role in the establishment of memories seems to be particularly important:
as it seems to be incompatible with the brain’s normal
processing of stimuli during waking, it might explain the
loss of consciousness in sleep. Sleep promotes primarily
the consolidation of memory, whereas memory encoding
and retrieval take place most effectively during waking.
Consolidation refers to a process that transforms new
and initially labile memories encoded in the awake state
into more stable representations that become integrated
into the network of pre-existing long-term memories.
Consolidation involves the active re-processing of ‘fresh’
memories within the neuronal networks that were used
for encoding them. It seems to occur most effectively
off-line, i.e. during sleep, so that encoding and consolidation cannot disturb each other and the brain does not
‘hallucinate’ during consolidation2.
The hypothesis that sleep favours memory consolidation has been around for a long time3. Recent research
in this field has provided important insights into the
underlying mechanisms through which sleep serves
memory consolidation4–7. In this Review, we first discuss
findings from behavioural studies regarding the specific
conditions that determine the access of a freshly encoded
memory to sleep-dependent consolidation, and regarding the way in which sleep quantitatively and qualitatively
changes new memory representations. We then consider
the role of slow-wave sleep (SWS) and rapid eye movement (REM) sleep in memory consolidation (BOX 1). We
finish by comparing two hypotheses that might explain
sleep-dependent memory consolidation on a mechanistic level, that is, the synaptic homeostasis hypothesis and
the active system consolidation hypothesis.
Behavioural studies
Numerous studies have confirmed the beneficial effect
of sleep on declarative and procedural memory in various
tasks8–10, with practically no evidence for the opposite
effect (sleep promoting forgetting)11. Compared with a
wake interval of equal length, a period of post-learning
sleep enhances retention of declarative information3,12–16
and improves performance in procedural skills13,17–24.
Sleep likewise supports the consolidation of emotional
information25–27. Effects of a 3-hour period of sleep on
emotional memory were even detectable 4 years later 28.
However, the consolidating effect of sleep is not revealed
under all circumstances and seems to be associated with
specific conditions29 (see below).
Sleep duration and timing. Significant sleep benefits
on memory are observed after an 8-hour night of sleep,
but also after shorter naps of 1–2 hours14,19,23,30, and even
an ultra-short nap of 6 minutes can improve memory
retention16. However, longer sleep durations yield greater
improvements, particularly for procedural memories18,21,31. The optimal amount of sleep needed to benefit
memory and how this might generalize across species
showing different sleep durations is unclear at present.
Some data suggest that a short delay between
learning and sleep optimizes the benefits of sleep on
memory consolidation. For example, for declarative
114 | FEbRuARy 2010 | VoluME 11
www.nature.com/reviews/neuro
© 2010 Macmillan Publishers Limited. All rights reserved
REVIEWS
Serial reaction time task
A task in which subjects are
required to rapidly respond to
different spatial cues by
pressing corresponding
buttons. This task can be
performed implicitly (that is,
without knowledge that there is
a regularity underlying the
sequence of cue positions) or
explicitly (by informing the
subject about this underlying
regularity).
information, sleep occurring 3 hours after learning
was more effective than sleep delayed by more than
10 hours32,33. However, these studies did not control for
the confounding effects of forgetting during the wake
interval before the onset of sleep. For optimal benefit
on procedural memory consolidation, sleep does not
need to occur immediately 18,19 but should happen on
the same day as initial training 17,22,24.
Explicit versus implicit encoding. Whether memories
gain access to sleep-dependent consolidation depends
on the conditions of encoding. Encoding of declarative memories is typically explicit, whereas procedural memory encoding can involve both implicit
and explicit processes. Most robust and reliable sleepdependent gains in speed have been revealed for the
finger sequence tapping task, which involves explicit
procedural memory 17–19,24. For the serial reaction time
task (SRTT), which can be learnt implicitly or explicitly,
the sleep-induced speeding of performance was more
robust when people learnt the task explicitly than
after implicit learning 34. These observations suggest
that explicit encoding of a memory favours access to
sleep-dependent consolidation.
The benefit of sleep is greater for memories formed
from explicitly encoded information that was more difficult to encode or that was only weakly encoded35,36, and
it is greater for memories that were behaviourally relevant.
Thus, sleep enhances the consolidation of memories for
intended future actions and plans (D. S., I. Wilhelm, u.
Wagner, J. b., unpublished observations). Notably, this
enhancement could be nullified by letting the subject
Box 1 | sleep architecture and neurophysiological characteristics of sleep stages
a
Sleep is characterized by the cyclic occurrence of rapid
eye movement (REM) sleep and non-REM sleep, which
Wake
REM sleep
includes slow wave sleep (SWS, stages 3 and 4) and lighter
sleep stages 1 and 2 (see the figure, part a). In humans, the
REM
first part of the night (early sleep) is characterized by high
Stage 1
amounts of SWS, whereas REM sleep prevails during the
second half (late sleep). SWS and REM sleep are
Stage 2
characterized by specific patterns of electrical field
potential oscillations (part b) and neuromodulator activity Stage 3
(part c, BOX 3).
SWS
The most prominent field potential oscillations during SWS Stage 4
are the slow oscillations, spindles and sharp wave-ripples,
Late sleep
Early sleep
whereas REM sleep is characterized by ponto-geniculo23:00
0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
occipital (PGO) waves and theta activity. The slow oscillations
Hours
originate in the neocortex with a peak frequency (in humans)
of ~0.8 Hz130,164. They synchronize neuronal activity into
down-states of widespread hyperpolarization and neuronal
b
Field potential oscillations
silence and subsequent up-states, which are associated with
depolarization and strongly increased, wake-like neuronal
Slow oscillation Spindle
Sharp wave-ripple
PGO wave
Theta activity
firing132,165,166 (part d). The hyperpolarization results from
2+
+
activation of a Ca -dependent K current and inactivation of
a persistent Na+ current, which dampens excitability165,167,168.
The depolarizing up-state might be triggered by summation
of miniature EPSPs (from residual activity from encoding
information) and is formed by activation of T-type Ca2+ and
c
Neuromodulators
persistent Na+ currents.
Spindle activity refers to regular electroencephalographic
Acetylcholine
Acetylcholine
oscillations of ~10–15 Hz, which are observed in human sleep
Noradrenaline/
Noradrenaline/
stage 2 as discrete waxing and waning spindles, but are present
serotonin
serotonin
at a similar level during SWS (although here they form less
Cortisol
Cortisol
discrete spindles)169. Spindles originate in the thalamus from an
interaction between GABAergic neurons of the nucleus
reticularis, which function as pacemakers, and glutamatergic
d
Slow oscillations
thalamo-cortical projections that mediate their synchronized
132,168,169
and widespread propagation to cortical regions
.
Hippocampal sharp waves are fast depolarizing events, generated in the CA3, on which
Field potential
high-frequency oscillations (100–300 Hz) originating from an interaction between inhibitory
interneurons and pyramidal cells in CA1 (so-called ripples) are superimposed104,121. Sharp
wave-ripples occur during SWS and also during waking, and accompany the re-activation
Up-state
of neuron ensembles that are active during a preceding wake experience70,71,121,122,170.
PGO-waves are driven by intense bursts of synchronized activity that propagate from the pontine
brainstem mainly to the lateral geniculate nucleus and visual cortex. They occur in temporal association
Single cell
with REM in rats and cats but are not reliably identified in humans. Theta oscillations (4–8 Hz) hallmark
recording
tonic REM sleep in rats and predominate in the hippocampus141. In humans, theta activity is less
Down-state
1s
coherent144,145.
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execute the intended behaviour before sleep. Similarly,
subjects who had been trained on two different fingertapping sequences showed greater sleep-dependent gains
in performance for the sequence for which they expected
to be rewarded for optimal performance at re-testing after
sleep37. Thus, a motivational tagging of memories, which
probably relies on the function of the prefrontal cortex38,
might signal behavioural effort and relevance and mediate
the preferential consolidation of these memories.
In summary, a great number of studies indicate that
sleep supports the consolidation of memory in all major
memory systems, but preferentially those that are explicitly
encoded and that have behavioural relevance to the individual. There is growing evidence that explicit encoding,
even in procedural tasks, involves a dialogue between
the prefrontal cortex and the hippocampus38–40, which
also integrates intentional and motivational aspects of
the task. Activity of this circuit may be crucial in making a memory susceptible to sleep-dependent memory
consolidation.
Implicit learning
Learning without being aware
that something is being
learned.
Explicit learning
Learning while being aware
that something is being
learned.
Memory systems
Different types of memory,
such as declarative and
non-declarative memory, are
thought to be mediated by
distinct neural systems, the
organization of which is still a
topic of debate.
Sleep changes memory representations quantitatively
and qualitatively. Consolidation of memory during
sleep can produce a strengthening of associations as
well as qualitative changes in memory representations.
Strengthening of a memory behaviourally expresses itself
as resistance to interference from another similar task
(‘stabilization’) and as an improvement of performance
(‘enhancement’) that occurs at re-testing, in the absence
of additional practice during the retention interval.
The stabilizing effects of sleep have been observed in
declarative41 and procedural19 memory tasks. Similarly,
enhancements in performance after sleep have been
shown for declarative information13,14,20 and in procedural tasks13,17,18,21,22,31. However, it is still controversial
to what extent these improvements reflect actual performance ‘gains’ induced by sleep, because the measured
gains depend on the pre-sleep performance used as a
reference, which itself can be subject to rapid changes
after training 42,43.
There is a long-standing debate about whether sleep
passively protects memories from decay and interference or actively consolidates fresh memory representations44 (for a review see Ref. 45). Importantly, a lack
of enhancement of memory performance after sleep
does not preclude an active role of sleep in memory
consolidation. There is strong evidence for an active consolidating influence of sleep from behavioural studies,
which indicate that sleep can lead to qualitative changes
in memory 46–48. For example, in one study, subjects
learned single relations between different objects which,
unknown to the subject, relied on an embedded hierarchy 47. When learning was followed by sleep, subjects at a
re-test were better at inferring the relationship between
the most distant objects, which had not been learned
before. likewise, after sleep subjects more easily solved
a logical calculus problem that they were unable to solve
before sleep or after corresponding intervals of wakefulness46. of note, sleep facilitated the gain of insight into
the problem only if adequate encoding of the task was
ensured before sleep.
Interacting or competing memory systems? The behavioural findings described above show that sleep can
‘re-organize’ newly encoded memory representations,
enabling the generation of new associations and the
extraction of invariant features from complex stimuli,
and thereby eventually easing novel inferences and
insights. Re-organization of memory representations
during sleep also promotes the transformation of
implicit into explicit knowledge, as was shown in an
SRTT which was implicitly trained but in which explicit
knowledge about the underlying sequence was examined during the re-test 48. Following post-training sleep,
subjects were better at explicitly generating the SRTT
sequence. Interestingly, subjects who developed explicit
sequence knowledge no longer showed the improvement in implicit procedural skill (that is, faster reaction
times) that is normally observed after sleep, suggesting
that procedural and declarative memory systems interact
during sleep-dependent consolidation.
Contrasting with this view of interacting memory
systems, it has also been proposed that disengagement
of memory systems is an essential characteristic of sleepdependent consolidation49. This idea derives mainly
from experiments showing that declarative learning of
words immediately after training of a procedural skill
can block off-line improvement in that skill if the subject
does not sleep between learning and re-testing, but not if
the subject sleeps between learning and re-testing 50. This
suggests that memory systems compete and reciprocally
interfere during waking, but disengage during sleep,
allowing for the independent consolidation of memories
in different systems. The two views might be reconciled
by assuming a sequential contribution of interaction and
disengagement processes to consolidation, which might
be associated with different sleep stages (REM sleep and
SWS), as discussed below.
Influence of sleep stages on consolidation
Early studies in rats and humans investigating whether
different sleep stages have different roles in memory
consolidation mainly focused on REM sleep and the
consequences of REM sleep deprivation (REMD) by
repeatedly waking subjects at the first signs of REM
sleep. However, this approach is of limited value for logical reasons and because the repeated awakenings cause
stress, which itself influences memory function51,52.
overall, these studies have provided mixed results52–55.
of note is a recent study showing that pharmacological suppression of REM sleep by administration of antidepressant drugs (selective noradrenaline or serotonin
re-uptake inhibitors) did not impair consolidation of
procedural memory 56, which is in agreement with clinical observations that antidepressant treatment does not
affect memory function57. However, such substances also
exert direct effects on synaptic plasticity and synaptic
forms of consolidation that could compensate for a loss
of REM sleep58.
Some studies performed in rats showed that REMD
is only effective during specific periods after learning
— the so-called ‘REM sleep windows’54. During postlearning sleep, increases in the amount and intensity
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of REM sleep occur several hours or even days after
learning, depending on the kind of task and amount of
initial training 54, and memory is particularly impaired if
REMD coincides with these periods. of note, the memory tasks used in rats are typically emotionally loaded. As
there is evidence that REM sleep preferentially benefits
the consolidation of emotional aspects of a memory 25,27,
this could partly account for the strong REMD effect
observed in many animal studies53,55.
Studies in humans have compared the effects on
consolidation between sleep periods with different
proportions of SWS and REM sleep. In humans, SWS
and REM sleep dominate the early and late part of nocturnal sleep, respectively (BOX 1). SWS-rich, early sleep
consistently benefits the consolidation of declarative
memories12,13,59, whereas REM-rich sleep benefits nondeclarative types of memory (that is, procedural and
emotional aspects of memory)13,25,59. These results are consistent with the ‘dual-process hypothesis’, which assumes
that SWS facilitates declarative, hippocampus-dependent memory and REM sleep supports non-declarative,
hippocampus-independent memory 6.
other studies have shown that SWS can also
improve procedural skill (that is, non-declarative)
memories31,60,61 and that REM sleep can also improve
declarative memory 62,63. Although these divergent findings could reflect that stimuli used in memory tasks are
often not of one type of memory system, they agree
with the ‘sequential hypothesis’, which argues that the
optimum benefits of sleep on the consolidation of both
declarative and non-declarative memory occur when
SWS and REM sleep take place in succession31,64. Thus,
overnight improvements in visual texture discrimination correlated with both the amount of SWS in the
first quarter of sleep and the amount of REM sleep in
the last quarter 21. Texture discrimination also improved
following a short midday nap of 60–90 minutes containing solely SWS, but more so if the nap included
both SWS and REM sleep23. Also, memory consolidation seems to be impaired by disruptions of the natural
SWS–REM sleep cycle that left the time spent in these
sleep stages unchanged65.
Intermediate sleep stages (non-REM sleep stage 2 in
humans, transitory sleep in rats) can also contribute to
memory consolidation66,67. For example, pharmacological suppression of REM sleep in humans produced
an unexpected overnight improvement in procedural
skill that was correlated with increased non-REM sleep
stage 2 spindle activity (see below)56. Such findings highlight the fact that it is not a particular sleep stage per se
that mediates memory consolidation, but rather the
neurophysiological mechanisms associated with those
sleep stages, and that some of these mechanisms are
shared by different sleep stages.
Transitory sleep
Short transitory periods of
sleep in rats that, based on
eeG criteria, can neither be
classified as ReM sleep or
SWS.
Core features of off-line consolidation
Since the publication of Hebb’s seminal book68, memory
formation has been conceptualized as a process in which
neuronal activity reverberating in specific circuits promotes enduring synaptic changes. building on this, it is
widely accepted that the consolidation process that takes
place off-line after encoding relies on the re-activation
of neuronal circuits that were implicated in the encoding of the information. This would promote both the
gradual redistribution and re-organization of memory
representations to sites for long-term storage (that is,
system consolidation; BOX 2) and the enduring synaptic
changes that are necessary to stabilize memories (synaptic consolidation). The conditions that enable these
two processes during sleep differ strongly between SWS
and REM sleep.
Re-activation of memory traces during sleep. The finding
that in rats the spatio-temporal patterns of neuronal
firing that occur in the hippocampus during exploration of a novel environment or simple spatial tasks are
re-activated in the same order during subsequent sleep
was an important breakthrough in memory research69–74
(fIG. 1a, see Ref. 75 for methodological considerations
on the identification of neuronal re-activations). Such
neuronal re-activation of ensemble activity mostly
occurs during SWS (it is rarely observed during REM
sleep76,77) and during the first hours after learning (but
see Ref. 78), and typically only in a minority of recorded
neurons69–74. Moreover, unlike re-activations that occur
during wakefulness, re-activations during SWS almost
always occur in the order in which they were experienced79. Compared with activity during encoding
phases, re-activations during SWS seem to be noisier,
less accurate and often happen at a faster firing rate71.
They are also observed in the thalamus, the striatum
and the neocortex 72–74,78. Sleep-dependent signs of reactivation in brain regions implicated in prior learning
were also shown in human neuroimaging studies80,81.
The first evidence for a causal role of re-activation
during SWS in memory consolidation came from a study
in humans learning spatial locations in the presence of an
odour 15. Re-exposure to the odour during SWS, but not
REM sleep, enhanced the spatial memories (fIG. 1b) and
induced stronger hippocampal activation than during
wakefulness, indicating that during SWS hippocampal
networks are particularly sensitive to inputs that can
re-activate memories (fIG. 1c). It is assumed that the reactivations during system consolidation stimulate the
redistribution of hippocampal memories to neocortical storage sites, although this has not been directly
demonstrated yet 82,83.
Synaptic consolidation. In addition to system consolidation (BOX 2), consolidation involves the strengthening
of memory representations at the synaptic level (synaptic consolidation)84,85. long-term potentiation (lTP)
is considered a key mechanism of synaptic consolidation, but it is unclear whether memory re-activation
during sleep promotes the redistribution of memories
by inducing new lTP (at long-term storage sites not
involved at encoding) or whether re-activation merely
enhances the maintenance of lTP that was induced
during encoding.
lTP can be induced in the hippocampus during
REM sleep but less reliably so during SWS86. lTP induction in the hippocampus or neocortex during SWS is
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Immediate early genes
Genes that encode
transcription factors that are
induced within minutes of
raised neuronal activity without
requiring a protein signal.
Immediate-early gene
activation is, therefore, used as
an indirect marker of neuronal
activation. The immediate
early genes Arc and Egr1
(zif268) are associated with
synaptic plasticity.
Hebbian plasticity
Refers to the functional
changes at synapses that
increase the efficacy of
synaptic transmission and
occurs when the presynaptic
neuron repeatedly and
persistently stimulates the
postsynaptic neuron.
Spike-time dependent
plasticity
Refers to the functional
changes at synapses that alter
the efficacy of synaptic
transmission depending on the
relative timing of pre- and
postsynaptic firing (‘spiking’).
The synaptic connection is
strengthened if the presynaptic
neuron fires shortly before the
postsynaptic neuron, but is
weakened if the sequence of
firing is reversed.
Box 2 | The two-stage model of memory consolidation
A key issue of long-term memory formation, the
Long-term store (slow learning)
so-called stability–plasticity dilemma, is the problem
of how the brain’s neuronal networks can acquire new
information (plasticity) without overriding older
Encoding
knowledge (stability). Many aspects of events
experienced during waking represent unique and
irrelevant information that does not need to be stored
long term. The two-stage model of memory offers a
widely accepted solution to this dilemma2,7,85,152 (see
the figure). The model assumes two separate memory
Encoding
Consolidation
stores: one store allows learning at a fast rate and
serves as an intermediate buffer that holds the
information only temporarily; the other store learns at
a slower rate and serves as the long-term store.
Initially, new events are encoded in parallel in both
stores. In subsequent periods of consolidation, the
newly encoded memory traces are repeatedly
re-activated in the fast-learning store, which drives
Temporary store (fast learning)
concurrent re-activation in the slow-learning store,
and thereby new memories become gradually
redistributed such that representations in the slow-learning, long-term store are strengthened.
Through
the
Nature Reviews
| Neuroscience
repeated re-activation of new memories, in conjunction with related and similar older memories, the fast-learning
store acts like an internal ‘trainer’ of the slow-learning store to gradually adapt the new memories to the
pre-existing network of long-term memories. This process also promotes the extraction of invariant repeating
features from the new memories. As both stores are used for encoding information, in order to prevent interference,
the re-activation and redistribution of memories take place off-line (during sleep) when no encoding occurs.
Because in this model consolidation involves the redistribution of representations between different neuronal
systems that is, the fast- and slow-learning stores, it has been termed ‘system consolidation’. For declarative
memories, the fast- and slow-learning stores are represented by the hippocampus and neocortex, respectively.
Figure modified, with permission, from Ref. 85 © (2005) Macmillan Publishers Ltd. All rights reserved.
probably temporally restricted to the up-states of the
slow oscillation and its concurrent phenomena of ripples and spindles87,88 (see BOX 1 and below). Indeed, in
neocortical slices, stimulation that mimicked neuronal
activity during SWS could induce long-term depression
(lTD)89 or lTP87 depending on the pattern of stimulation (rhythmic bursts or spindle-like trains, respectively).
lTP maintenance in the rat hippocampus, but not in the
medial prefrontal cortex, was impaired if induction was
followed by REMD90. In humans, sleep strengthened
lTP-like plasticity that had been induced in the neocortex by transcranial magnetic stimulation (TMS) prior
to sleeping 91.
Globally (meaning measured in whole-brain or large
cortical samples) sleep suppresses the molecular signals that mediate lTP-related synaptic remodelling but
enhances lTD-related signalling, and this effect seems
to be mediated by SWS92–95. This observation, however,
does not preclude that lTP occurs during sleep (during
SWS or REM sleep) in specific regions, for example in
those that were engaged in memory encoding prior to
sleeping. In rats, both induction of hippocampal lTP
and exposure to a novel tactile experience during waking increased the expression of the plasticity-related
immediate early genes (IEGs) Arc and Egr1 (which are
implicated in lTP) during subsequent sleep, mainly in
cortical areas that were the most activated by the novel
experience, and this effect seemed to be mediated by
REM sleep96–98. Investigations in visual cortex in cats
and humans have demonstrated that sleep-dependent
plasticity depends on the activation of glutamatergic
NMDA (N-methyl-d-aspartate) receptors and associated cAMP-dependent protein kinase A (PKA), and
on AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole
propionic acid) receptor activation, that is, the postsynaptic machinery that is crucial for the induction and
maintenance of lTP99–102. These findings indicate that
local, off-line re-activation of specific glutamatergic
circuits supports both lTP induction and maintenance,
and the molecular processes underlying synaptic consolidation. Moreover, these processes probably occur
preferentially during REM sleep, although they are
likely to be triggered by the re-activations that occur
during prior SWS (see below). Evidence about how lTP
induction and maintenance is linked to specific sleep
stages is presently scarce, but based on the available
data it is tempting to speculate that SWS supports the
re-activation of new memories (system consolidation)
and thus, could initialize lTP and prime the relevant
networks for synaptic consolidation during subsequent
REM sleep. This idea seems to be supported by electroencephalographic (EEG) rhythms that characterize
these sleep stages.
sleep-specific field potential oscillations
Sleep stages are characterized by specific electrical
field potential rhythms that temporally coordinate
information transfer between brain regions and
might support Hebbian and spike-time-dependent
plasticity103,104.
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a
Cortex
Run
Hippocampus
Run
Sleep
5
Cell number
Cell number
7
Sleep
0
7
0
1s
0
5
0
0.5 s
1s
0.2 s
b
Learning
Sleep
Retrieval
Odour/vehicle
Wake
REM
Stage 1
Stage 2
Stage 3
Stage 4
Odour
Odour
20:00
24:00
Retrieval
performance
Recalled card locations
Odour re-exposure
%
Time of day
During SWS
***
%
04:00
During REM
08:00
%
100
100
100
90
90
90
80
0
No odour Odour
80
0
80
0
c
No odour Odour
x = 11
During waking
No odour Odour
y = -15
6.0
t21
3.5
Figure 1 | Memory re-activation during slow wave sleep (sWs). a | In awake rats
running on a circular track (Run), neurons in the sensory cortex
andReviews
hippocampus
fire
Nature
| Neuroscience
in a characteristic sequential pattern. Each row represents an individual cell and each
mark in the upper parts of the diagrams indicates a spike; the curves in the lower parts
indicate the respective average firing patterns of the cells. During subsequent slow
wave sleep (SWS) (Sleep), temporal firing sequences observed in the cell assemblies
during running re-appear both in the cortex and in the hippocampus72. b | Human
subjects learned a two-dimensional object location task on a computer while an odour
was presented as a context stimulus. Re-exposure to the odour specifically during
subsequent SWS enhanced retention performance (recalled card locations) when
tested the next day. There was no enhancement in retention when no association was
formed between object locations and odour (that is, odour presentation during SWS
but not during learning) or when odour re-exposure occurred during rapid eye
movement (REM) sleep or waking15. c | When participants slept in an fMRI scanner after
learning in the presence of odour, re-exposure to the odour during SWS activated the
left anterior hippocampus (left) and neocortical regions like the retrosplenial cortex
(right), which was not observed without odour presentation during prior learning7. Part
a is modified, with permission, from Ref. 72 © 2007 Macmillan Publishers Ltd. All rights
reserved; part b is modified, with permission, from Ref. 15 © 2007 American
Association for the Advancement of Science; part c modified, with permission, from
Ref. 7 © 2007 Elsevier.
Field potentials associated with SWS. Neocortical slow
oscillations, thalamo-cortical spindles and hippocampal
ripples have been associated with memory consolidation
during SWS (BOX 1). The neocortical slow oscillations (of
<1 Hz), by globally inducing up- and down-states of neuronal activity, are thought to provide a supra-ordinate
temporal frame for the dialogue between the neocortex
and subcortical structures that is necessary for redistributing memories for long-term storage8,105,106. The amplitude and slope of the slow oscillations are increased
when SWS is preceded by specific learning experiences60,107,108 and decreased when the encoding of information was prevented109. These changes occur locally, in
the cortical regions that were involved in encoding, and
can also be induced in humans by potentiating synaptic circuits through TMS91,110,111. Inducing slow oscillations during non-REM sleep by transcranial electrical
stimulation using slow (0.75 Hz) but not fast (5 Hz)
oscillating potential fields improved the consolidation
of hippocampus-dependent but not hippocampusindependent (procedural) memories112, indicating that
slow oscillations have a causal role in the consolidation
of hippocampus-dependent memories.
Thalamo-cortical spindles seem to prime cortical
networks for the long-term storage of memory representations. Repeated spindle-associated spike discharges
can trigger lTP87 and synchronous spindle activity
occurs preferentially at synapses that were potentiated
during encoding 113. Studies in rats and humans showed
increases in spindle density and activity during nonREM sleep and SWS after learning of both declarative
tasks and procedural motor skills20,108,114–118. In some
studies these increases correlated with the post-sleep
memory improvement 30,119,120 and were localized to the
cortical areas that were activated during encoding, for
example, in the prefrontal cortex after encoding of difficult word pairs117,119, the parietal cortex after a visuospatial task120 and the contralateral motor cortex after
finger motor-skill learning 30.
Hippocampal sharp wave-ripples accompany the
sleep-associated re-activation of hippocampal neuron
ensembles that were active during the preceding awake
experience70,71,121,122. The occurrence of sharp waveripples is facilitated in previously potentiated synaptic circuits123 and sharp wave-ripples might promote
synaptic potentiation88,124. During an individual ripple
event only a small subpopulation of pyramidal cells fire
— the subpopulation varies between successive ripples,
indicating modulation of select neuronal circuits121,125.
In rats, learning of odour–reward associations produced a robust increase in the number and size of ripple
events for up to two hours during subsequent SWS126. In
humans (epileptic patients) the consolidation of picture
memories that were acquired before a nap correlated
with the number of ripples recorded from the peri- and
entorhinal cortex, which are important output regions
of the hippocampus127. Selective disruption of ripples
by electrical stimulation during the post-learning rest
periods in rats impaired formation of long-lasting spatial
memories128, suggesting that ripples have a causal role in
sleep-associated memory consolidation.
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Interestingly, there is a fine-tuned temporal relationship between the occurrence of slow oscillations, spindles and sharp wave-ripples during SWS that coordinates
the bidirectional information flow between the neocortex and the hippocampus. With some exceptions (which
are probably due to methodological differences 129) a
consistent finding in humans, cats, rats and mice is that
spindle activity and ripples increase during the up-state
and become suppressed during the down-state of a slow
oscillation105,129–132. The top–down control of neuronal
activity by neocortical slow oscillations probably extends
to activity in other brain regions that are also relevant to
memory consolidation, such as the noradrenergic
burst activity of the locus coeruleus133,134. Sharp waveripple complexes are also temporally coupled to sleep
spindles105,135,136, with individual ripple events becoming nested in individual spindle troughs135. It has been
suggested that such ripple-spindle events provide a
mechanism for a fined-tuned hippocampal-neocortical
information transfer, whereby ripples and associated
hippocampal memory re-activations feed exactly into
the excitatory phases of the spindle cycle8,105,137,138. In
this scenario, the feed-forward control of slow oscillations over ripples and spindles enables transferred
information to reach the neocortex during widespread
depolarization (during the up-state), that is, a state that
favours the induction of persistent synaptic changes,
eventually resulting in the storage of the information in
the cortex. The extent to which the grouping effect of the
slow oscillation on hippocampal activity is associated
with transfer of memory-specific information in the
opposite direction (from cortex to hippocampus), is
currently unclear.
Up- and down-states
The slow oscillations that
predominate eeG activity
during SWS are characterized
by alternating states of
neuronal silence with an
absence of spiking activity and
membrane hyperpolarization
in all cortical neurons
(‘down-state’) and strongly
increased wake-like firing of
large neuronal populations and
membrane depolarization
(‘up-state’).
Field potentials associated with REM sleep. Pontogeniculo-occipital (PGo) waves and the EEG theta
rhythm seem to support REM sleep-dependent consolidation processes (BOX 1). The significance of PGo-waves
for memory consolidation is indicated by findings in rats
of a robust increase in REM sleep PGo-wave density
for 3–4 hours following training on an active avoidance task67,139,140. The increase was proportional to the
improvement in post-sleep task performance, and was
associated with increased activity of plasticity-related
IEGs and brain-derived neurotrophic factor (bdnf )
in the dorsal hippocampus within 3 hours following
training 140.
The theta (4–8 Hz) oscillations that characterize REM
sleep in rats are also thought to contribute to consolidation, based mainly on the finding that theta activity during
waking occurs during the encoding of hippocampusdependent memories141. However, evidence for this
assumption is scarce. There is evidence of neuronal
re-play of memories in the hippocampus during REM
sleep-associated theta activity 76,77. Place cells encoding a
familiar route were re-activated preferentially during the
troughs of theta oscillations during post-training REM
sleep, whereas cells encoding novel sites fired during the
peaks77. As lTP induction in hippocampal CA1 cells
during theta activity depends on the phase of burst activity 142, this finding is consistent with the idea that REM
sleep de-potentiates synaptic circuits that encode familiar events but potentiates synaptic circuits that encode
novel episodes77. In humans, neocortical theta activity
was enhanced during REM sleep following learning of
word pairs62. Theta activity specifically over the right
prefrontal cortex was correlated with the consolidation
of emotional memories27. by contrast, mice exhibited
reduced REM sleep theta activity after fear conditioning 143. Thus, although overall there is some evidence for
an involvement of theta activity in memory processing
during sleep, its specific contribution to consolidation is
obscure at present.
Theta activity occurring in conjunction with activity
in other EEG frequencies points to another important
feature that is relevant to memory processing: during
REM sleep, EEG activity in a wide range of frequencies,
including theta, shows reduced coherence between limbic-hippocampal and thalamo-cortical circuits than during SWS or waking 144,145. likewise, >40 Hz gamma band
activity shows reduced coherence between CA3 and
CA1 during tonic REM sleep146. These findings suggest
that memory systems become disengaged during REM
sleep49, possibly as a pre-requisite for establishing effective local processes of synaptic consolidation in these
systems (see below).
synaptic homeostasis versus system consolidation
There are currently two hypotheses for the mechanisms underlying the consolidation of memory during
sleep (fIG. 2). The synaptic homeostasis hypothesis11,147
assumes that consolidation is a by-product of the global synaptic downscaling that occurs during sleep. The
active system consolidation hypothesis proposes that
an active consolidation process results from selective
re-activation of memories during sleep 2,8. The two
models are not mutually exclusive; indeed, the hypothesized processes probably act in concert to optimize the
memory function of sleep.
Synaptic homeostasis. According to the synaptic homeostasis hypothesis, information encoding during wakefulness leads to a net increase in synaptic strength in the
brain. Sleep would serve to globally downscale synaptic
strength to a level that is sustainable in terms of energy
and tissue volume demands and that allows for the reuse of synapses for future encoding 92,94. Slow oscillations
are associated with downscaling: they show maximum
amplitudes at the beginning of sleep when overall synaptic strength is high, due to information uptake during encoding prior to sleep, and decrease in amplitude
across SWS cycles as a result of the gradual synaptic depotentiation. Memories become relatively enhanced as
downscaling is assumed to be proportional in all synapses, nullifying weak potentiation and thus improving the signal-to-noise ratio for the synapses that were
strongly potentiated during prior waking 147 (fIG. 2a).
However, there is no clear evidence on how slow
oscillations might induce synaptic downscaling. The
low levels of excitatory neurotransmitters during SWS
(BOX 3) and the sequence of depolarization (up-states)
and hyperpolarization (down-states) of slow oscillations
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at a frequency of <1 Hz might specifically promote the depotentiation of synapses148. Indeed, slow oscillations and
the associated activation of T-type Ca2+ channels seem to
favour lTD over lTP89; however, thalamo-cortical spindles
and hippocampal ripples nesting in depolarizing up-states
of slow oscillations support lTP87,88,124.
In addition, although the expression of markers of
synaptic potentiation (such as plasticity-related IEGs) is
globally reduced after a period of sleep, it is increased in
specific regions, particularly if sleep was preceded by a
learning experience78,96,98, indicating that synaptic potentiation might still take place during sleep. Consistent
with downscaling, some neuroimaging studies (which
measure relative changes in brain activation) have shown
reduced task-related activity in cortical regions after
sleep (e.g. Ref. 149), but these reductions were accompanied by increases in activity in other regions82,83,149,150.
Also, global synaptic downscaling implicates that weakly
encoded memories are forgotten, which contrasts with
behavioural evidence indicating either no or, under
certain conditions, a greater benefit from sleep for
weakly than strongly encoded memories35,36. Therefore,
a
Synaptic strength
Waking – Synaptic potentiation
W=100
W=150
W=5
W=100
W=100
downscaling per se does not explain key features of sleepdependent consolidation. However, the synaptic downscaling model explains a second memory-related function
of sleep, namely that sleep pro-actively facilitates the
encoding of new information during subsequent wakefulness through the de-potentiation of synapses that had
become saturated during preceding wakefulness (this
topic is beyond the scope of this Review)151.
Active system consolidation. This concept originated
from the standard two-stage model of consolidation proposed for declarative memory2,7,85,121,152 (BOX 2; fIG. 2b), but
might also account for consolidation in other memory
systems8. It is assumed that in the waking brain events
are initially encoded in parallel in neocortical networks
and in the hippocampus. During subsequent periods of
SWS the newly acquired memory traces are repeatedly
re-activated and thereby become gradually redistributed
such that connections within the neocortex are strengthened, forming more persistent memory representations.
Re-activation of the new representations gradually adapt
them to pre-existing neocortical ‘knowledge networks’,
Sleep – Synaptic downscaling
W=80
W=120
Time
b
SWS
REM
Synaptic plasticity
Neocortex
Slow oscillations
Synchronizing
feed-forward
effect
Hippocampus
Sharp wave-ripples
LTP
Ca2+
Synchronous
feedback
Thalamus
Spindles
NMDAR
CaMKII
PKA
AMPAR
IEG
Figure 2 | synaptic homeostasis versus active system consolidation. The synaptic homeostasis hypothesis (a)
Nature(large
Reviews
| Neuroscience
proposes that due to encoding of information during waking, synapses become widely potentiated
yellow
nerve
ending), resulting in a net increase in synaptic strength (W = synaptic weight). The small nerve ending represents a new
synapse and the unfilled nerve ending is not activated and therefore does not increase in weight. The slow oscillations
during subsequent SWS serve to globally downscale synaptic strength (burgundy nerve endings). Thereby, weak
connections are eliminated, whereas the relative strength of the remaining connections is preserved. Thus, a memory is
enhanced as a consequence of an improved signal-to-noise ratio after downscaling. The active system consolidation
model (b) assumes that events during waking are encoded in both neocortical and hippocampal networks. During
subsequent slow wave sleep (SWS), slow oscillations drive the repeated re-activation of these representations in the
hippocampus, in synchrony with sharp wave-ripples and thalamo-cortical spindles (synchronizing feed-forward effect of
the slow oscillation up-state). By synchronizing these events the slow oscillations support the formation of ripple-spindle
events, which enable an effective hippocampus-to-neocortex transfer of the re-activated information. Arrival of the
hippocampal memory output at cortical networks, coinciding with spindle activity during the depolarizing slow oscillation
up-state predisposes these networks to persisting synaptic plastic changes (for example, expression of immediate early
genes (IEG) through Ca2+/calmodulin-dependent protein kinase II (CaMKII) and protein kinase A (PKA) activation) that are
supported primarily by subsequent rapid eye movement (REM) sleep. AMPAR, α-amino-3-hydroxy-5-methyl-4-isoxazole
propionic acid receptor; LTP, long-term potentiation; NMDAR, N-methyl-d-aspartate receptor. Part a is modified, with
permission, from Ref. 147 © 2006 Elsevier; part b is modified, with permission, from Ref. 5 © 2006 Sage publications.
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thereby promoting the extraction of invariant repeating features and qualitative changes in the memory
representations2,7.
Corroborating this concept, studies showed that
memory re-activation during post-learning SWS and
hippocampal ripples accompanying this re-activation
have a causal role in consolidation15,128. Re-activation in
hippocampal networks seems to be enabled by the low
cholinergic tone that characterizes SWS153–155 (BOX 3).
Moreover, there is evidence that the re-activation and
redistribution of memories during SWS is regulated
by a dialogue between the neocortex and the hippocampus that is essentially under feed-forward control of
the slow oscillations, which provide a temporal frame.
Box 3 | Neuromodulators
The specific neurochemical milieu of neurotransmitters and hormones differs strongly
between slow wave sleep (SWS) and rapid eye movement (REM) sleep. Some of these
neuromodulators contribute to memory consolidation. Interestingly, the most
prominent contributions to memory processing seem to originate from the cholinergic
and monoaminergic brainstem systems that are also involved in the basic regulation of
sleep171.
sWs
Cholinergic activity is at a minimum during SWS; this is thought to enable the
spontaneous re-activation of hippocampal memory traces and information transfer to
the neocortex by reducing the tonic inhibition of hippocampal CA3 and CA1 feedback
neurons8,154,155. Accordingly, increasing cholinergic tone during SWS-rich sleep (using
physostigmine) blocked the sleep-dependent consolidation of hippocampus-dependent
word-pair memories153. Conversely, blocking the high cholinergic tone in awake
subjects improved consolidation but impaired the encoding of new information172,
suggesting that acetylcholine serves as a switch between modes of brain activity, from
encoding during wakefulness to consolidation during SWS154,155. This dual function of
acetylcholine seems to be complemented by glucocorticoids (cortisol in humans), the
release of which is also at a minimum during SWS. Glucocorticoids block the
hippocampal information flow to the neocortex, and if the level of glucocorticoids
is artificially increased during SWS, the consolidation of declarative memories is
blocked173,174.
Noradrenergic activity is at an intermediate level during SWS, and seems to be
related to slow oscillations. In rats, phasic burst firing in the locus coeruleus (the brain’s
main source of noradrenaline) can be entrained by slow oscillations in the frontal
cortex, with a phase-delay of ~300 ms133. It is possible that such bursts enforce
plasticity-related immediate early gene (IEG) activity in the neocortex93,95, and thereby
support at the synaptic level the stabilization of newly formed memory
representations. In humans, the consolidation of odour memories was impaired after
pharmacological suppression of noradrenergic activity during SWS-rich sleep and
improved after increasing noradrenaline availability (S. Gais, B. Rasch, J.C. Dahmen, S.J.
Sara and J. B., unpublished observations).
reM sleep
Cholinergic activity during REM sleep is similar or higher than during waking. This high
cholinergic activity might promote synaptic consolidation by supporting
plasticity-related IEG activity162 and the maintenance of long-term potentiation163.
Accordingly, blocking muscarinic receptors in rats by scopolamine during REM sleep
impaired memory in a radial arm maze task175. In humans, blocking cholinergic
transmission during REM-rich sleep prevented gains in finger motor skill176. Conversely,
enhancing cholinergic tone during post-training REM-rich sleep improved
consolidation of a visuo-motor skill177.
Noradrenergic and serotonergic activity reaches a minimum during REM sleep, but it
is unclear whether this contributes to consolidation. It has been proposed that the
release from inhibitory noradrenergic activity during REM sleep enables the
re-activation of procedural and emotional aspects of memory (in cortico-striatal and
amygdalar networks, respectively), thus supporting memory consolidation154,178.
However, enhancing noradrenergic activity during post-learning REM sleep in humans
failed to impair procedural memory consolidation56.
The depolarizing cortical up-states repetitively drive the
re-activation of memory traces in hippocampal circuits
in parallel with thalamo-cortical spindles and activity
from other regions (for example, noradrenergic locus
coeruleus bursts, see BOX 3). This enables synchronous
feedback from these structures to the neocortex during
the slow oscillation up-state, which is probably a prerequisite for the formation of more persistent traces in
neocortical networks8,106. Consistent with this concept,
neuronal re-activations in the timeframe of cortical slow
oscillations have been demonstrated, in which hippocampal re-play leads re-activation in the neocortex 72,122 (and
also in other structures like the striatum156). Moreover,
slow oscillations drive the ripples that accompany hippocampal re-activation, thus allowing for the formation
of spindle-ripple events as a mechanism for effective
hippocampus-to-neocortex information transfer 105,137,138
(fIG. 2b). Spindles reaching the neocortex during slow
oscillation up-states probably act to prime specific neuronal networks, for example, by stimulating Ca2+ influx,
for subsequent synaptic plastic processes87,157.
The concept of active system consolidation during
SWS integrates a central finding from behavioural studies, namely that post-learning sleep not only strengthens memories but also induces qualitative changes in
their representations and so enables the extraction of
invariant features from complex stimulus materials, the
forming of new associations and, eventually, insights
into hidden rules46–48. The concept of a redistribution
of memories during sleep has been corroborated by
human brain imaging studies82,83,149,150,158. Interestingly, in
these studies, hippocampus-dependent memories were
particularly redistributed to medial prefrontal cortex
regions82,83,122 that also contribute to the generation of
slow oscillations159,160. These regions not only have a key
role in the recall and binding of these memories once
they are stored for the long term85, but also, together
with the hippocampus, form a loop that supports the
explicit encoding of information. As mentioned above,
behavioural data indicate that sleep does not benefit all
memories equally, but seems to preferentially consolidate explicitly encoded information34. In this context, the
prefrontal–hippocampal system might provide a selection mechanism that determines which memory enters
sleep-dependent consolidation.
A role for ReM sleep in synaptic consolidation
The active system consolidation hypothesis leaves open
one challenging issue: although it explains a re-activationdependent temporary enhancement and integration
of newly encoded memories into the network of preexisting long-term memories, active system consolidation alone does not explain how post-learning sleep
strengthens memory traces and stabilizes underlying
synaptic connections in the long term. Hence, sleep presumably also supports a synaptic form of consolidation
for stabilizing memories and this could be the function
of REM sleep.
The view that synaptic consolidation is promoted
by REM sleep is supported by the molecular and electrophysiological events that characterize this stage.
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Sequential
Waking
SWS
REM sleep
Time
Long-term store
Encoding
Synaptic
consolidation
Active system
consolidation
Temporary store
Figure 3 | sequential contributions of sWs and reM sleep to memory consolidation in a two-stage memory
system. During waking, memory traces are encoded in both the fast-learning, temporary store and the slow-learning,
Nature
Reviews | Neuroscience
long-term store (in the case of declarative memory these are represented by the hippocampus
and neocortex,
respectively). During subsequent slow wave sleep (SWS), active system consolidation involves the repeated re-activation
of the memories newly encoded in the temporary store, which drives concurrent re-activation of respective
representations in the long-term store together with similar associated representations (dotted lines). This process
promotes the re-organization and integration of the new memories in the network of pre-existing long-term memories.
System consolidation during SWS acts on the background of a global synaptic downscaling process (not illustrated) that
prevents saturation of synapses during re-activation (or during encoding in the subsequent wake-phase). During ensuing
rapid eye movement (REM) sleep, brain systems act in a ‘disentangled’ mode that is also associated with a disconnection
between long-term and temporary stores. This allows for locally encapsulated processes of synaptic consolidation, which
strengthen the memory representations that underwent system consolidation (that is, re-organization) during prior SWS
(thicker lines). In general, memory benefits optimally from the sequence of SWS and REM sleep. However, declarative
memory, because of its integrative nature (it binds features from different memories in different memory systems),
benefits more from SWS-associated system consolidation, whereas procedural memories, because of their specificity
and discrete nature, might benefit more from REM sleep-associated synaptic consolidation in localized brain circuits.
Figure modified, with permission, from Ref. 85 © 2005 Macmillan Publishers Ltd. All rights reserved.
Although any links between sleep phases of short duration and gene expression are difficult to demonstrate
for methodological reasons, several studies suggest that
REM sleep, unlike SWS, is associated with an upregulation of plasticity-related IEG activity (RefS 97,98,139).
The upregulation depends on learning experience during prior wakefulness and is localized to brain regions
involved in prior learning 97,98,139. Interestingly, this IEG
activity is correlated with EEG spindle activity during
preceding SWS98. Spindles (which, as discussed above,
represent a candidate mechanism that tags networks for
the neocortical storage of memories during system consolidation) per se do not induce IEG activity, but might
prime particular brain areas for it, possibly by enhancing Ca2+ concentrations in select subgroups of cortical
neurons87,157. The activity of plasticity-related early genes
depends on cholinergic tone161,162, which is enhanced to
wake-like levels during REM sleep (BOX 3). Cholinergic
activation strengthens the maintenance of lTP in the hippocampus-medial prefrontal cortex pathway 163, a main
route for transferring memories during SWS-dependent
system consolidation82,83,122,136. Electrophysiological signatures of REM sleep, such as PGo waves, are increased
during post-learning sleep and might promote IEG
activity and memory consolidation140. EEG recordings
indicate that during REM sleep brain activation is as
high as during waking, but less coherent between different regions and noisier 144–146. This high level of activation
could act non-specifically to amplify local synaptic plasticity in an environment that, compared with the awake
state, is almost entirely unbiased by external stimulus
inputs. The disentangled, localized nature of synaptic
consolidation might also explain why REM sleep alone
fails to improve declarative memory consolidation: this
process essentially relies on the integration of features
from different memories in different memory systems and corresponding information transfer between
widespread brain areas, that is, SWS-dependent system
consolidation.
Conclusions and future directions
SWS and REM sleep have complementary functions to
optimize memory consolidation (fIG. 3). During SWS —
characterized by slow oscillation-induced widespread
synchronization of neuronal activity — active system
consolidation integrates newly encoded memories with
pre-existing long-term memories, thereby inducing conformational changes in the respective representations.
System consolidation (which preferentially affects explicitly encoded, behaviourally relevant information) acts in
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REVIEWS
concert with global synaptic downscaling, which serves
mainly to preclude the saturation of synaptic networks.
Ensuing REM sleep — characterized by de-synchronization of neuronal networks, which possibly reflects a disengagement of memory systems — might act to stabilize the
transformed memories by enabling undisturbed synaptic
consolidation. Although REM sleep has been suspected
for a long time to have a key role in memory consolidation, research has paid little attention to the fact that REM
sleep naturally follows SWS. This points to complementing
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Acknowledgments
We apologize to those whose work was not cited because of
space constraints. We thank Drs. B. Rasch, L. Marshall, I.
Wilhelm, M. Hallschmid, E. Robertson and S. Ribeiro for helpful discussions and comments on earlier drafts. This work was
supported by a grant from the Deutsche Forschungsgemeinschaft
(SFB 654 ‘Plasticity and Sleep’).
Competing interests statement
The authors declare no competing financial interests.
DATABAses
Entrez Gene: http://www.ncbi.nlm.nih.gov/gene
Arc | Egr1
UniProtKB: http://www.uniprot.org
Bdnf |
FURTHeR INFORMATION
Jan Born’s homepage: http://www.kfg.uni-luebeck.de
All liNks Are Active iN the oNliNe pdf
www.nature.com/reviews/neuro
© 2010 Macmillan Publishers Limited. All rights reserved
Talk 1: Sleep and Memory
Memory formation is a highly dynamic process. According to a widely held concept, the
formation of long-term memories relies on a redistribution of newly acquired memory
representations from temporary stores to neuronal networks supporting long-term
storage. It is assumed that this process of system consolidation takes place
preferentially during sleep as an "off-line" period during which memories are
spontaneously reactivated and redistributed in the absence of interfering external
inputs. In my talk, I would like to provide evidence showing that sleep is beneficial for
the formation of memories. In addition, I aim at discussing different mechanisms
proposed to underlie the sleep-related memory benefits: the active system consolidation
hypothesis, the synaptic down selection hypothesis and the opportunistic theory of
sleep.
Talk 2: Improving memories by reactivation during sleep
In part 2 if my talk, I will present recent evidence supporting the notion that memories
are spontaneously reactivated during sleep and that induced reactivations during sleep
by cueing improves memory consolidation during sleep, but not during wakefulness.
Furthermore, I will discuss possible oscillatory mechanisms underlying the stabilizing
effect of targeted memory reactivations on memory consolidation during sleep.