System consolidation of memory during sleep

Department of Medical Psychology and Neurobiology, University of Tübingen, Gartenstr. 29, 72074, Tübingen, Germany.
Psychological Research (Impact Factor: 2.47). 05/2011; 76(2):192-203. DOI: 10.1007/s00426-011-0335-6
Source: PubMed


Over the past two decades, research has accumulated compelling evidence that sleep supports the formation of long-term memory. The standard two-stage memory model that has been originally elaborated for declarative memory assumes that new memories are transiently encoded into a temporary store (represented by the hippocampus in the declarative memory system) before they are gradually transferred into a long-term store (mainly represented by the neocortex), or are forgotten. Based on this model, we propose that sleep, as an offline mode of brain processing, serves the 'active system consolidation' of memory, i.e. the process in which newly encoded memory representations become redistributed to other neuron networks serving as long-term store. System consolidation takes place during slow-wave sleep (SWS) rather than rapid eye movement (REM) sleep. The concept of active system consolidation during sleep implicates that (a) memories are reactivated during sleep to be consolidated, (b) the consolidation process during sleep is selective inasmuch as it does not enhance every memory, and (c) memories, when transferred to the long-term store undergo qualitative changes. Experimental evidence for these three central implications is provided: It has been shown that reactivation of memories during SWS plays a causal role for consolidation, that sleep and specifically SWS consolidates preferentially memories with relevance for future plans, and that sleep produces qualitative changes in memory representations such that the extraction of explicit and conscious knowledge from implicitly learned materials is facilitated.


Available from: Ines Wilhelm
System consolidation of memory during sleep
Jan Born
Ines Wilhelm
Received: 27 July 2010 / Accepted: 2 April 2011 / Published online: 4 May 2011
The Author(s) 2011. This article is published with open access at
Abstract Over the past two decades, research has accu-
mulated compelling evidence that sleep supports the for-
mation of long-term memory. The standard two-stage
memory model that has been originally elaborated for
declarative memory assumes that new memories are tran-
siently encoded into a temporary store (represented by the
hippocampus in the declarative memory system) before
they are gradually transferred into a long-term store
(mainly represented by the neocortex), or are forgotten.
Based on this model, we propose that sleep, as an offline
mode of brain processing, serves the ‘active system con-
solidation’ of memory, i.e. the process in which newly
encoded memory representations become redistributed to
other neuron networks serving as long-term store. System
consolidation takes place during slow-wave sleep (SWS)
rather than rapid eye movement (REM) sleep. The concept
of active system consolidation during sleep implicates that
(a) memories are reactivated during sleep to be consoli-
dated, (b) the consolidation process during sleep is selec-
tive inasmuch as it does not enhance every memory, and
(c) memories, when transferred to the long-term store
undergo qualitative changes. Experimental evidence for
these three central implications is provided: It has been
shown that reactivation of memories during SWS plays a
causal role for consolidation, that sleep and specifically
SWS consolidates preferentially memories with relevance
for future plans, and that sleep produces qualitative chan-
ges in memory representations such that the extraction of
explicit and conscious knowledge from implicitly learned
materials is facilitated.
Sleep supports the formation of memory. This is a long-
standing idea dating back to Ebbinghaus and the begin-
nings of experimental memory research (Ebbinghaus
1885). While memory formation is not the only function of
sleep, it seems to be the most important because it helps to
establish the state of consciousness during wakefulness. In
fact, establishing a stream of consciousness requires that
acute sensations are continuously linked and referenced to
pre-existing memories in the brain and like our body
would fall apart if the atoms did not attract each other, our
consciousness would fall apart into as many pieces as
moments there are without the binding power of memory’
(Hering 1921). As memory has been seen by many phi-
losophers and psychologist as a major prerequisite for
consciousness, it is of course interesting if this memory is
revealed to be formed and built up to a great part during
sleep, i.e. a brain state characterized by a profound loss of
consciousness. Indeed, it appears that consciousness on the
one hand and the formation of memory—at least of long-
term memory—on the other hand, are two mutually
exclusive processes that cannot take place simultaneously
in the brain’s neuronal networks. Thus, memory formation
could be the function of sleep that eventually helps us to
understand why sleep is associated with a more or less
profound loss of consciousness.
Dedicated to Professor Frank Ro
sler on the occasion of his 65th
J. Born (&)
Department of Medical Psychology and Neurobiology,
University of Tu
bingen, Gartenstr. 29, 72074 Tu
I. Wilhelm
Department of Neuroendocrinology, University of Lu
Haus 50, Ratzeburger Allee 160, 23538 Lu
beck, Germany
Psychological Research (2012) 76:192–203
DOI 10.1007/s00426-011-0335-6
Page 1
Stability versus plasticity in memory
A basic issue of memory research, i.e. the stability–plas-
ticity dilemma, refers to the question how the brain can
maintain previously learned memories while it continues to
learn new things that tend to override the old memories.
How does the brain prevent that newly learnt materials
wash away the old memories but, rather become incorpo-
rated into the networks of pre-existing long-term memo-
ries, and how do old memories remain accessible in an
ever-changing environment (Carpenter & Grossberg
1988)? Furthermore, many aspects of episodes experienced
in the wake state represent irrelevant information that does
not need to be stored for the long term. The standard two-
stage model of memory offers a widely accepted solution
to these issues (Marr 1971; McClelland, McNaughton, &
O’Reilly 1995). It assumes two separate memory stores,
one that learns at a fast rate and holds information only
temporarily, and the other that learns at a slow rate but
shows also a slow rate of forgetting and serves as the long-
term store. New information is initially encoded in parallel
into both the temporary and the long-term store. In sub-
sequent periods of consolidation, the newly encoded
memory traces are repeatedly reactivated and thereby
become gradually reorganised such that the representations
in the slow-learning long-term store are strengthened.
Through the repeated reactivation of new in conjunction
with related older memories, the temporary store acts like
an internal ‘‘trainer’’ of the slowly learning long-term store
to gradually adapt the new memories to the pre-existing
knowledge networks. The reactivation and redistribution of
memories to the long-term store can also promote the
extraction of invariant and relevant features from the new
memories, whereas irrelevant features may be erased.
Because both stores are used also for encoding of infor-
mation, this encoding could interfere and disturb the proper
consolidation process. Therefore, to prevent such interfer-
ence, the reactivation and redistribution of memories dur-
ing consolidation take place in offline periods, i.e. during
sleep when there are no encoding demands.
Although the standard two-stage model represents a
general concept that can account for memory formation in
very different neuropsychological systems, and has even
been applied to immunological memory formation (Lange,
Dimitrov, & Born 2010), it has become most elaborated for
the declarative memory system. In the declarative memory
system the fast-learning, temporary and slow-learning
long-term stores are represented by the hippocampus and
neocortex, respectively. Based on the repeated reactivation
of temporary memories in the hippocampus, these are
gradually redistributed over periods of days and years to
neocortical networks and can ultimately lose their depen-
dence on hippocampal networks (Zola-Morgan & Squire
1990; McClelland et al. 1995; Frankland & Bontempi
2005). Declarative memory is commonly divided into
semantic memory that refers to general knowledge and
episodic memory that refers to individual events con-
sciously experienced during wakefulness. Semantic mem-
ories in the process of consolidation can in fact become
entirely independent from the hippocampus and neigh-
bouring medial temporal lobe structures implicating that
they are erased from hippocampus networks. By contrast,
for episodic memories hippocampal function may be con-
tinuously required even after years of consolidation (Nadel,
Samsonovich, Ryan, & Moscovitch 2000).
The consolidation of memory within a two-stage
memory system implies a so-called ‘system consolidation’
process as representations are redistributed between dif-
ferent brain systems. System consolidation is commonly
dissociated from another type of consolidation termed
‘synaptic consolidation’ which implicates the strengthening
of memory representations at the synaptic level and refers
to changes of synaptic connections in localized neuronal
circuits (Dudai 2004). It has been proposed that system
consolidation takes place preferentially off-line during
sleep, because this type of consolidation involves the
reactivation of fresh memory representations to promote
their redistribution to the long-term store, with these pro-
cesses possibly interfering with the brain’s normal pro-
cessing of external stimuli (Diekelmann & Born 2007). By
contrast, synaptic consolidation may occur equally well
during wakefulness.
Sleep’s role in active system consolidation
The standard two-stage model of memory has been taken
as a basis for conceptualizing the function of sleep for
memory as a process supporting active system consolida-
tion (Marshall & Born 2007; Diekelmann & Born 2010). In
an attempt to integrate a large variety of findings from
animal and human studies in the field, the role sleep and
different sleep stages play was specified mainly for con-
solidating memories at the systems level. Nevertheless, the
model can also be extended to explain a second memory-
related function of sleep, namely to ease the encoding of
new materials. It concentrates on the declarative memory
system, but may also be used to explain some observations
made for sleep-dependent consolidation of procedural
According to this model (Fig. 1), events experienced
during wakefulness are initially encoded in parallel in
neocortical networks and in the hippocampus including
adjacent medial temporal lobe structures. Encoding in
hippocampal networks is probably restricted to certain
aspects, for example, episodic features of an event. During
Psychological Research (2012) 76:192–203 193
Page 2
subsequent periods of sleep and, here mainly during slow
wave sleep (SWS), the newly acquired memory traces are
repeatedly reactivated and thereby become gradually
redistributed such that synaptic connections within the
neocortex are strengthened forming more persistent mem-
ory representations.
The reactivation and redistribution of memories during
SWS are regulated in a dialogue between neocortex and
hippocampus that is essentially under feed-forward control
of the slow oscillation, which hallmark the EEG during
SWS and occur in human sleep at a spectral frequency of
*0.75 Hz. The slow oscillations are generated primarily in
neocortical networks, probably in part as function of the
prior use of the networks for encoding of information, i.e.
the more information is encoded during wakefulness the
higher the amplitude of the slow oscillations is over
respective cortical areas during succeeding SWS (Huber,
Ghilardi, Massimini, & Tononi 2004;Mo
lle, Marshall,
Gais, & Born 2004). A major function of the slow oscil-
lations is that they temporally group neuronal activity into
hyperpolarizing down-states during which neurons are
globally silent and succeeding depolarizing up-states dur-
ing which neuronal firing is increased to wake-like levels
(Steriade 2006). Importantly, slow oscillations synchronize
neuronal activity not only in the neocortex, but via efferent
pathways also in various other brain regions relevant to
memory consolidation, i.e. in the thalamus where thalamo-
cortical spindles are generated and in the hippocampus
where reactivations of memory representations are gener-
ated arising in these networks conjointly with so-called
sharp-wave ripples in the hippocampal EEG. Thus, the
slow oscillations provide a global temporal frame whereby
the depolarizing up phases repetitively drive the reactiva-
tion of memories in hippocampal circuits in parallel with
thalamo-cortical spindles and probably also with activity
from other regions (e.g. noradrenergic locus coeruleus
bursts). This enables that the activity fed back from these
structures to the neocortex arrives at about the same time—
and still in the depolarizing up-state—at respective neo-
cortical circuitry. The hippocampo-to-neocortical transfer
of reactivated memory information reaching neocortical
circuitry in the up-state and in synchrony with thalamo-
cortical spindles is likely a prerequisite for the formation of
more persisting traces in neocortical networks. Consistent
with this concept, at the level of neuronal firing, reactiva-
tions of newly encoded memory representations in the
timeframe of the slow oscillation have been demonstrated
both in hippocampal and neocortical circuitry (Ji & Wilson
2007; Euston, Tatsuno, & McNaughton 2007). Also, con-
sistent with this concept, at the level of local field poten-
tials, slow oscillations, spindle activity and hippocampal
ripple activity are increased during sleep after a learning
experience (Gais, Molle, Helms, & Born 2002; Eschenko,
Ramadan, Molle, Born, & Sara 2008;Mo
lle & Born 2009),
and there is also evidence that these increases are linked to
an improved retention of the learned memories (Huber
et al. 2004; Clemens, Fabo, & Halasz 2005; Clemens,
Fabo, & Halasz 2006; Girardeau, Benchenane, Wiener,
Buzsaki, & Zugaro 2009).
An important feature of the synchronizing influence of
slow oscillations is that they allow for the formation of
spindle-ripple events as a mechanism that mediates the
Fig. 1 Active system consolidation during sleep. a During slow-wave
sleep (SWS) memories newly encoded into a temporary store (i.e. the
hippocampus in the declarative memory system) are reactivated to be
redistributed to the long-term store (i.e. the neocortex). b System
consolidation during SWS relies on a dialogue between neocortex and
hippocampus under top-down control by the neocorticalslow oscillations
(red). The depolarizing up phases of the slow oscillations drive the
repeated reactivation of hippocampal memory representations together
with sharp-wave ripples (green) in the hippocampus and thalamo-cortical
spindles (blue). This synchronous drive allows for the formation of
spindle-ripple events where sharp-wave ripples and associated reacti-
vated memory information becomes nested into single troughs of a
spindle (shown at larger scale); in the black-and-white version of the
figure red, green and blue correspond to dark, middle and light grey
194 Psychological Research (2012) 76:192–203
Page 3
transfer of hippocampal memory information to the neo-
cortex in a temporally fine-tuned manner. Spindle oscilla-
tions are generated in the thalamus in a frequency range
between 12 and 15 Hz in the human EEG and via thalamo-
cortical pathways reach widespread neocortical areas. The
driving influence of the depolarizing up phase of the slow
oscillation on spindle and hippocampal ripple activity
enables the ripples and accompanying memory information
to become nested into the single oscillatory troughs of
spindles (Siapas & Wilson 1998; Sirota & Buzsaki 2005;
Wierzynski, Lubenov, Gu, & Siapas 2009;Mo
lle, Esc-
henko, Gais, Sara, & Born 2009; Clemens et al. 2011).
Spindles reaching the neocortex likely act to prime
respective neuronal networks, e.g. by stimulating Ca
influx for subsequent synaptic plastic processes. Thus,
memory information carried in single troughs of spindle
oscillations maybe particularly effective in changing syn-
aptic connections underlying the long-term storage of the
information in the respective neocortical networks.
While the neurophysiological underpinnings of this
model cannot be discussed here in detail, the model has
also important implications for psychological concepts of
memory consolidation. Thus, the model considers reacti-
vation as a basic mechanism of offline consolidation. Also,
the model predicts that the system consolidation process
during sleep is selective, i.e. only some of the information
encoded into the temporal store is transferred to the long-
term store whereas others become erased. Finally, the
model predicts that memory undergoes specific qualitative
changes in the representation during the off-line transfer
from temporary to long-term storage. The three aspects will
be discussed in more detail in the following.
Reactivation as basic mechanism
of sleep-dependent memory consolidation
A central implication of the standard two-stage memory
system is that the redistribution of temporarily stored
memories to long-term stores is achieved by the repeated
reactivation of the newly encoded memories during off-line
periods following the learning experience. At the level of
neuronal firing, there is compelling evidence from studies
in rats that spatio-temporal patterns of neuronal firing
present during exploration of a novel environment and
simple spatial tasks (maze learning) are reactivated in the
same sequential order in the hippocampus during sub-
sequent sleep (Pavlides & Winson 1989; Wilson &
McNaughton 1994; Sutherland & McNaughton 2000;
Ribeiro et al. 2004; O’Neill, Pleydell-Bouverie, Dupret, &
Csicsvari 2010). Such neuronal reactivation of ensemble
activity was observed almost exclusively during SWS and
very rarely during rapid eye movement (REM) sleep. It is
not limited to hippocampal circuitry but occurs also in the
thalamus, striatum and neocortex (Pennartz et al. 2004;
Euston et al. 2007; Lansink et al. 2008). However, reacti-
vations in the hippocampus seem to precede those in
neocortex and striatum (Ji & Wilson 2007; Lansink,
Goltstein, Lankelma, McNaughton, & Pennartz 2009).
Sleep-dependent reactivation of activity in brain regions
implicated in prior learning was likewise revealed in
human neuroimaging studies (Maquet et al. 2000; Peigneux
et al. 2004). However, all these studies indicating an
association between neuronal activity at learning and
reactivation during post-learning sleep could not exclude
that neuronal reactivation during sleep represents a mere
epiphenomenon not causally contributing to the consoli-
dation of memory.
In a study in humans using a hippocampus-dependent
visuo-spatial learning task we provided first evidence for a
causal role of reactivations during sleep (Rasch, Bu
Gais, & Born 2007). Before sleep, the subjects learned the
locations of 15 pairs of cards, similar to the game ‘Con-
centration’ (German, ‘Memory-Spiel’’) with each pair
showing the same object (e.g. a red car). First, all cards are
arranged up side down and for recall testing one card of a
pair is shown to the subject and the subject has to indicate
the location of the corresponding second card. Importantly,
when learning the card locations, the subjects were
simultaneously presented with an odour, the scent of roses,
which as a context stimulus should become associated with
the card locations to be learned. If the same odour was
presented again during sleep after learning, then this sig-
nificantly improved memory for the card locations learned
before sleep. Re-exposure of the odour during sleep
effectively enhanced memory only when the odour was
presented during SWS, but not during REM sleep, or when
the subjects were awake during odour re-exposure. Also, in
control experiments odour presentation during SWS was
not effective when the odour had not been presented at
learning prior to sleep or when the odour was presented
during learning of a procedural finger-sequence tapping
task that does not essentially rely on hippocampal function.
Via the olfactory system odour stimulation acquires
immediate access to the hippocampus. Consequently, in an
fMRI study we found that the odour when re-exposed
during SWS after learning induced a distinct activation of
the left hippocampus, i.e. the odour served as a cue that
reactivated the new memories for the card locations
encoded in the left hippocampus, thereby enhancing these
memories. Interestingly, odour re-exposure during SWS
induced hippocampal activation that was even greater than
during wakefulness, indicating that hippocampal networks
are particularly sensitive in SWS to inputs capable of
reactivating memories. However, it is an unsolved issue
whether the reactivations of a specific representation
Psychological Research (2012) 76:192–203 195
Page 4
occurring in these networks in natural conditions is like-
wise linked to any specific input, or occurs spontaneously,
e.g. due to the relative freshness of a representation.
Although cuing during sleep with odours may be par-
ticularly effective as it leaves sleep architecture completely
undisturbed, a sleep-associated enhancement of spatial
memories has been also revealed after cuing with auditory
stimuli (Rudoy, Voss, Westerberg, & Paller 2009). In rats,
consolidation of hippocampus-dependent memories for a
spatial maze was impaired following suppression of sharp-
wave ripples typically accompanying memory reactiva-
tions during SWS (Girardeau et al. 2009; Ego-Stengel &
Wilson 2010). Taken together, the findings corroborate the
causal role off-line reactivations of memory representa-
tions play for the consolidation of these memories.
Reactivation of memory also occurs during wakefulness
with retrieval or mere cuing of memory. However, unlike
reactivations occurring during the offline conditions of
SWS, reactivations during wakefulness can be disturbed by
interfering external inputs. It has been shown that memo-
ries when reactivated during wakefulness undergo a period
of destabilization and need to be re-consolidated in a sep-
arate process in order to persist (Sara 2000; Nader & Hardt
2009). Based on this evidence, in a recent study we asked
whether reactivating a memory during SWS, in the same
way as reactivation during wakefulness, leads to a desta-
bilization of the fresh memory representations (Diekel-
mann, Bu
chel, Born, & Rasch 2011; Fig. 2). Like in the
Rasch et al. (2007) study, discussed above, subjects learned
card-pair locations in the presence of an odour. Subse-
quently the odour was re-exposed to reactivate the mem-
ories while the subjects were in a period of SWS or while
they were awake. Immediately after the odour-cued reac-
tivation, stability of the memory was probed by having the
subject learning an interference task, i.e. card pairs located
at different places. As expected, in comparison with a non-
reactivation condition, reactivation of the memories during
waking destabilized memories, i.e. at a final retrieval test
subjects showed a distinctly impaired memory for the
originally learned card locations when interference learn-
ing took place after the original card-pair memories had
been reactivated during wakefulness. In striking contrast,
odour-cued reactivation of the memories during SWS sta-
bilized memories, i.e. despite interference learning fol-
lowing reactivation, at the final retrieval test subjects had a
better memory for the original card location when these
memories had been reactivated during SWS than in the
control condition where these memories were not reacti-
vated. Functional magnetic resonance imaging (fMRI)
indicated that reactivation during SWS mainly activated
hippocampal and posterior neocortical areas whereas
reactivations during waking were associated mainly with
prefrontal activation. These findings suggest that
reactivation as a principle mechanism in the formation of
memories serves different functions depending on whether
it occurs during waking or SWS (Rasch et al. 2007).
Whereas reactivation during wakefulness may primarily
serve to update the reactivated memory with new infor-
mation, leading to a new memory in need of re-consoli-
dation, it is only when reactivation occurs during SWS that
memories undergo immediate stabilization. Indeed, the
strengthened resistance to interference learning of a
memory after reactivation during SWS is well in line with
the assumption that during SWS these memories are
transferred to neocortical storage sites. Thereby these
memories become less dependent on hippocampus as the
primary site where newly encoded memories can overlay
and disrupt previously encoded older representation, i.e. a
process known as retroactive interference (Kuhl, Shah,
DuBrow, & Wagner 2010).
Of note, in the study by Diekelmann et al. (2011) reacti-
vation during SWS enhanced memories although the sleep
period did not contain any REM sleep (but was terminated
right after odour cuing during SWS had been completed).
Together with a body of evidence from behavioural studies
comparing the relative benefits for memory from retention
periods rich of SWS or REM sleep (summarized in Marshall
& Born 2007) this finding underlines that the putative system
consolidation associated with sleep is supported mainly by
SWS. Although traditionally REM sleep as well as the
associated dreams reported after awakening from REM sleep
have traditionally been linked with memory processing more
closely than SWS, the function of REM sleep for memory
consolidation is presently obscure (Siegel 2001; Wamsley,
Tucker, Payne, Benavides, & Stickgold 2010). Based on the
fact that REM sleep always follows SWS, a sequential pro-
cess has been hypothesized such that SWS favours system
consolidation of memory involving the reactivation and
redistribution of memories to sites of the long-term storage,
whereas subsequent REM sleep supports local processes of
synaptic consolidation whereby the redistributed memory
representations are additionally stabilized once they are
transferred to the sites of long-term storage (Giuditta 1985;
Diekelmann & Born 2010). This view is in agreement with
above mentioned findings that the putative hippocampo-to-
neocortical transfer of memories during SWS suffices to
make these memories resistant to interference. This view is
also in agreement with the fact that, if at all, REM sleep
benefits aspects of a memory not essentially depending on
the (binding function of the) hippocampus, i.e. procedural
features of memory (Plihal & Born 1997; Plihal & Born
1999). Yet other findings contradict this view (e.g. Rasch,
Pommer, Diekelmann, & Born 2009). Thus, unmasking at
the behavioural level a presumed contribution of REM sleep
to synaptic consolidation remains a continuous challenge to
future research in the field.
196 Psychological Research (2012) 76:192–203
Page 5
Selectivity of memory consolidation during sleep
Although vast amounts of information are encoded by the
brain during a daytime period of wakefulness, only a
fraction of this information is being stored for the long
term. A global strengthening of newly acquired memory
traces and underlying synaptic connections during off-line
consolidation would inevitably result in a system overflow.
Selectivity indeed represents a major adaptive function of
active system consolidation within a two-stage memory
system. However, although the body of literature indicates
that not all memories benefit from sleep-associated con-
solidation, the factors that determine whether a memory
gains access to sleep-dependent offline consolidation and
transfer to long-term storage are currently not well
There is evidence from studies examining effects of
sleep after implicit versus explicit learning of the serial
reaction time task that the sleep-induced gain in motor
performance on the task was more robust after explicit
training, suggesting that explicit encoding favours access to
sleep-dependent memory consolidation (Robertson, Pasc-
ual-Leone, & Press 2004). Involvement of the prefrontal-
hippocampal system underlying explicit encoding has been
proposed as prerequisite for consolidation to occur during
sleep (Marshall & Born 2007). Once explicitly encoded,
the profit from sleep-dependent consolidation appears to be
greater for information that was more weakly encoded
(Drosopoulos, Schulze, Fischer, & Born 2007). Also, dif-
ficulty of encoding and emotionality of the encoded events
can increase the memory benefit from sleep (Kuriyama,
Stickgold, & Walker 2004; Wagner, Hallschmid, Rasch, &
Born 2006).
However, the properties of an explicitly encoded stim-
ulus per se do not alone determine whether a memory
enters sleep-dependent consolidation. In a recent series of
studies we showed that sleep preferentially consolidates
memories that are relevant for an individual’s future plans,
thus demonstrating the future-oriented, motivated character
of sleep-dependend memory consolidation. In one of these
studies (Wilhelm et al. 2011; Fig. 3), subjects learned
declarative memories (verbal and visual-spatial paired
associates) before periods of sleep and wakefulness, and
after this learning period were or were not informed that
retrieval of the memories will be tested later on. Post-
learning sleep, in comparison with wakefulness produced a
significant improvement at delayed retrieval testing only, if
the subjects had been informed about the retrieval testing.
On the other hand, retention during wake intervals was not
affected by the retrieval expectancy. Thus, the mere
expectancy that a memory will be used at a future occasion
provides access for this memory to sleep-associated con-
solidation. Retrieval expectancy in these experiments was
associated with increased EEG slow oscillation activity
during post-learning sleep which, furthermore, was
strongly correlated with correct recall at the delayed
retrieval testing.
Retrieval expectancy and associated rewards are also
effective in sleep-dependent procedural memory consoli-
dation (Fischer & Born 2009). In this study, before reten-
tion periods of sleep and wakefulness, subjects were
trained on a finger-sequence tapping task on two different
sequences (A and B), one after the other. After training,
Fig. 2 Stabilizing and labilizing effects of memory reactivation
during sleep and wakefulness, respectively. Subjects learned the
locations of card pairs (similar to the game ‘Concentration’) while
odour was presented as an associative context stimulus. Learning was
followed by retention intervals filled with either wakefulness (Wake)
or sleep (Sleep; i.e. NonRem sleep as no REM sleep occurred in these
intervals). During these intervals the subjects were re-exposed to the
odour to reactivate the card location memories. Vehicle was presented
in the non-reactivation control condition. Periods of memory
reactivation (and vehicle administration) were followed by learning
an interference task (with the same cards but different locations) to
probe stability of the originally learned card location. a Final recall
(mean ± SEM) of the originally learned card-pair locations tested
30 min after interference learning. Performance is given as percent-
age, with the number of card locations recalled at learning before the
retention interval set to 100%. Note, reactivation stabilized memories
(i.e. made them resistant to interference learning) when the odour was
re-exposed during SWS, but labilised memories when the odour was
re-exposed during wakefulness. b Odour-induced memory reactiva-
tion during wakefulness increased activity in the lateral prefrontal
cortex (left) whereas reactivation during SWS strongly activated the
left anterior hippocampus (right) Thresholded at P \ 0.001 uncor-
rected; superimposed on a T
-template image (adapted from Diekel-
mann et al. 2011)
Psychological Research (2012) 76:192–203 197
Page 6
they were told that at the later retrieval test they would
receive extra monetary reward if they performed particu-
larly well on one of the sequences. Performance on the
other sequence was not associated with an anticipated extra
monetary reward. To avoid that the expected reward
directly affects retrieval performance, the original reward
association was nullified before actual retrieval testing, and
the subjects were told that good performance on both
sequences would be rewarded. Nevertheless, the sleep-
dependent gain in motor finger tapping skill was distinctly
greater for the sequence associated with expected reward
than for the other one. The enhancing effect of reward
expectancy was again specific to sleep-dependent consoli-
dation, as retention across the wake interval remained
unaltered by the expectancy manipulation. Note, in both
experiments the expectancy manipulation occurred after
the learning phase, excluding that differences at encoding
per se enhanced the sleep-dependent processing of
Using two tasks of prospective memory, we showed that
sleep also supports the implementation of future plans
(Diekelmann S., Wilhelm I., Wagner U. and Born J., sub-
mitted). Comparing retention periods rich in SWS or REM
sleep indicated that the successful implementation of the
plans depended on SWS. Importantly, the enhancing effect
of sleep on the memory for the plan was nullified if the
subjects executed the planned behaviour already before the
period of retention sleep. Prior execution of the plan
abolished the enhancing effect not only on prospective
memories, but also on retrospective memories constituting
the plan.
In combination, this series of studies indicates that
sleep-dependent consolidation of memory is motivationally
driven and, in this way, selective by strengthening specif-
ically those memories that are relevant for future actions
and goals. The underlying mechanisms of this selection
process are presently obscure. Processing of anticipatory
aspects of behaviour such as expectancies and plans is
particularly linked to executive functions of the prefrontal
cortex that regulates activation of memory representations
during anticipated retrieval and accommodates specifically
the intentional and prospective aspects of a memory rep-
resentation (Cohen & O’Reilly 1996; Miller & Cohen
2001; Polyn & Kahana 2008; Hannula & Ranganath 2009).
At a neuronal level, the allocation of future-oriented
expectancies and intentions to a memory may become
manifest in a tagging of the newly encoded memory that
facilitates its access to sleep-dependent memory processes.
Consistent with this view, during post-learning SWS in
rats, neuronal ensemble activity present during learning is
replayed not only in hippocampal circuitry but also in
prefrontal cortex (Euston et al. 2007; Peyrache, Khamassi,
Benchenane, Wiener, & Battaglia 2009). Thus, a prefrontal
tagging of memories explicitly encoded under control of
the prefrontal-hippocampal system could be decisive for
the selectivity in off-line memory consolidation. Interest-
ingly, the prefrontal cortex belongs also to the regions most
strongly involved in the generation of slow oscillations
Fig. 3 a Effects of retrieval expectancy on retention of declarative
word pair memories across 9-h intervals filled with nocturnal sleep,
night-time wakefulness and daytime wakefulness. Retention perfor-
mance is indicated by the percentage of word pairs recalled at
retrieval with performance on the criterion trial during learning set to
100%. Memory performance was enhanced after post-learning sleep
in comparison with wakefulness only when subjects expected the
retrieval test (expected, black bars) but not when the retrieval was
unexpected (unexpected, empty bars). b During post-learning sleep,
slow oscillation power (0.68–1.17 Hz) within the first 120 min of
NonREM sleep (indicated for successive 20-min intervals, 0–20,
20–40 min, etc.) was enhanced when subjects expected retrieval
testing (solid lines) in comparison to subjects who did not expect the
retrieval (dotted lines). c Slow oscillation power during the first
20-min period of post-learning NonREM sleep was highly correlated
to retention of word pairs when subjects expected retrieval testing
(filled circles, solid line), but not when retrieval testing was
unexpected (empty circles, dotted lines)*P \0.05, **P \ 0.01,
***P \ 0.001 (adapted from Wilhelm et al. 2011)
198 Psychological Research (2012) 76:192–203
Page 7
(Massimini, Huber, Ferrarelli, Hill, & Tononi 2004; Mur-
phy et al. 2009). It is thus tempting to speculate that pre-
frontal slow oscillations hallmarking SWS reflect the
activity of a read-out system that via tagged connections
controls the selective reactivation and redistribution of
newly encoded hippocampal memories to neocortical sites
of long-term storage. Simultaneously, these slow oscilla-
tions may drive processes actively erasing information
from hippocampal sites that are irrelevant for future
behaviour, thus freeing the hippocampus for the encoding
of new information during subsequent wake periods (Yoo,
Hu, Gujar, Jolesz, & Walker 2007; van der Werf et al.
Changes in quality of memory during sleep-dependent
Recall of memories in the hippocampus-dependent
declarative memory system becomes gradually—although
not necessarily completely—independent of hippocampal
networks over time to rely mainly on neocortical networks
(Frankland & Bontempi 2005). Recent fMRI studies have
provided evidence that the hippocampo-to-neocortical
redistribution of memory representations is promoted by
sleep (Takashima et al. 2006; Gais et al. 2007). In parallel
to these qualitative changes in the representation of a
memory at the neuronal level sleep produces qualitative
changes in the memory at the behavioural level. In fact,
there is convergent evidence for the notion that the system
consolidation process during sleep supports the extraction
of invariant and repeating features in newly encoded
memories, and in this way, the conversion of implicit into
an explicit and conscious form of memory.
In a first study (Wagner, Gais, Haider, Verleger, & Born
2004; Fig. 4a), we examined this issue using the Number
Reduction Task (NRT). This task consists of the presen-
tation of strings of digits which the subjects are required to
process such that they reach the final solution to each string
as fast as possible. Earlier, they are taught how they can
reach the solution by sequentially processing the digits
according to a set of rules. However, unknown to the
subject, the digit strings are built up according to an
underlying structure such that once gaining insight into this
hidden structure the subject can give the correct answer to
each string right away, without processing the whole digit
string sequentially. In the study, subjects first practiced on
90 digit strings which was not sufficient to produce insight
into the hidden structure. Then, an 8-h interval of sleep or
wakefulness followed before subjects were tested again on
the task, this time for up to 300 strings. At this retest more
than twice as many subjects of the sleep group gained
insight into the hidden structure as compared with the wake
control groups. In further control experiments, sleep did
not facilitate the gain of insight into the hidden rule, if
subjects had not practiced the task before sleep, i.e. if no
representation of the task and the digit strings had been
encoded that could be re-processed during subsequent
sleep. This is an important observation because it excludes
that the facilitated gain of insight after sleep resulted from
a generally improved creative thinking but, on the contrary,
confirms that this facilitation is a memory phenomenon
requiring a specific memory to be encoded before sleep
which then becomes consolidated and reorganized such
that the gain of insight is eventually facilitated. Further
studies comparing retention periods rich in SWS or REM
sleep revealed that once subjects have acquired implicit
knowledge about the rule during the pre-sleep practice
phase, SWS rather than REM sleep is critical for the
occurrence of explicit insight into the rule (Yordanova
et al. 2008). In more fine-grained analyses, slow spindle
activity during SWS was revealed as a marker favouring the
transformation of pre-sleep implicit knowledge to post-sleep
explicit knowledge (Yordanova J., Kolev V., Wagner U.
et al., submitted; Fig. 4b).
Further approaches to demonstrate a role of sleep in
converting implicitly learned knowledge about rules and
patterns into explicit knowledge used a relational memory
task (Ellenbogen, Hu, Payne, Titone, & Walker 2007) and
the serial reaction time task (SRTT) (Fischer et al. 2006).
In the latter task, subjects are presented with a cue (e.g. an
asterisk) that appears at different horizontally arranged
positions on a computer screen. The subject’s task is to
respond as fast and as accurately as possible to the cue by
pressing a spatially corresponding key on a response pad
below the screen. Unknown to the subject, the cue changes
its position according to a complex but repeating sequence,
i.e. the underlying sequence grammar. After some training
on the task, reaction times to the cue positions typically
distinctly decrease compared with reaction times to a
random sequence of cue positions, indicating implicit
knowledge of the sequence grammar. Fischer et al. (2006)
combined this classical SRTT with a so-called generation
task where subjects are explicitly asked to deliberately
generate the sequence of cue positions in the SRTT they
had trained before under implicit conditions. In this study,
subjects were first trained on the SRTT to acquire implicit
sequence knowledge. Subsequent generation task perfor-
mance assured that subjects had not obtained any explicit
sequence knowledge during prior training. Then, a 9-h
retention interval followed which was filled with either
sleep or wakefulness, before subjects were tested a second
time on the generation task. At this retest, the subjects who
had slept after SRTT training were distinctly more able to
deliberately generate the sequence underlying the SRTT
than the subjects who had stayed awake after the initial
Psychological Research (2012) 76:192–203 199
Page 8
SRTT training. In fact, the latter group at the retest still
performed at chance level. Whether subjects are informed
or not before sleep that there was some kind of grammar in
the SRTT sequence they trained, is not essential for the
sleep-induced gain of explicit sequence knowledge,
although such information can induce delayed gains of
explicit knowledge independently of the occurrence of
sleep during the post-training interval (Drosopoulos, Har-
rer, & Born 2010). Also, preliminary findings show that the
gain of explicit knowledge from implicitly learned struc-
tures is much greater in children than in adults, in parallel
with the much greater amounts of SWS in children (Wil-
helm I., Rasch B., Rose M., Bu
chel C. & Born J.,
Taken together, these studies indicate that sleep reor-
ganizes newly encoded memory representations thereby
enabling the extraction of invariant features from complex
stimulus materials and, eventually, the gain of insight into
hidden structures and explicit knowledge about implicitly
learned rules. Insight and the emergence of explicit
knowledge in the waking brain involve activity in pre-
frontal cortical areas in connection with hippocampal and
closely connected medial temporal lobe areas (McIntosh
et al. 1999; Luo and Niki 2003; McIntosh et al. 2003; Jung-
Beeman et al. 2004; Rose et al. 2010). We speculate that
neuronal reactivation of task representations occurring in
these regions during SWS mediates a restructuring of the
representation which produces an increased binding to
prefrontal circuits and thereby facilitates the generation of
explicit knowledge.
Although strong behavioural evidence for a sleep-
induced reorganization of memory representations has
been so far only provided for the case that explicit
knowledge is extracted from implicitly acquired materials,
it is not excluded that such reorganisation occurs also in the
absence of an implicit-to-explicit conversion, i.e. for
explicit representations that stay explicit and for implicit
representations that stay implicit. For example, studies
using the Deese, Roediger, McDermott (DRM) false
memory paradigm suggest that sleep can support the
extraction of an explicit gist memory for the word lists that
were explicitly encoded before sleep (Payne et al. 2009;
Diekelmann et al. 2010). Sleep has also been revealed to
benefit the transfer of tapping performance from one hand
used at training to the contralateral hand in a procedural
finger-sequence tapping task (Witt et al. 2010; Cohen et al.
2005) and also to support tapping speed on a finger
sequence that had only been learned by observation before
sleep (van der Werf et al. 2009). Such findings suggest that
qualitative reorganisations during sleep may affect implicit
representations as well. However, these issues clearly need
further investigation.
Fig. 4 Extraction of explicit knowledge from an implicitly learned
task during slow-wave sleep (SWS). a Subjects implicitly learned a
number reduction task (NRT) before retention intervals of nocturnal
sleep, daytime wakefulness, or night-time wakefulness. Percentage of
subjects gaining explicit insight into the hidden rule of the task after
the retention intervals is indicated. The percentage of subjects gaining
insight was more than twofold higher when initial practice was
followed by sleep in comparison with wakefulness. b If some implicit
knowledge about the rule underlying the NRT had been obtained at
initial practice, those participants who gained insight after sleep
showed significantly higher power in the slow spindle band (8–12 Hz,
shaded area) and in the beta frequency band (17–25 Hz) during post-
practice SWS (red, upper line) compared with those participants who
failed (black, lower line, adapted from Wagner et al. 2004, and
Yordanova J., Kolev V., Wagner U. et al., submitted)
200 Psychological Research (2012) 76:192–203
Page 9
The formation of long-term memory is effectively
established in a two-stage memory system where offline
periods are used to gradually transfer newly encoded
memories from a temporary store into a long-term store.
Over the past two decades convergent evidence has been
accumulated that sleep and particularly SWS serves as
an off-line period in which newly encoded hippocampus-
dependent declarative memories are gradually adapted to
pre-existing knowledge networks presumably residing
mainly in the neocortex. Rather then merely supporting
consolidation in a passive manner by protecting them
from retroactive interference (Ellenbogen, Payne, &
Stickgold 2006), sleep additionally supports an active
process of system consolidation (Diekelmann & Born
2010). This process originates from the reactivation of
newly encoded memories in the hippocampus, is
selective in that memories with relevance for the indi-
vidual’s future behaviour are preferentially consoli-
dated, and leads to a reorganisation of the memory
representation that produces changes in the quality of
the memory. A particularly intriguing aspect of the
latter function is that sleep appears to prime the
transformation of implicitly encoded information into
explicit knowledge, i.e. something that is not conscious
before sleep enters consciousness through sleep. In this
way, sleep as a brain state hallmarked by a profound
loss of consciousness helps establishing consciousness
during wakefulness.
While recent findings have corroborated this concept
of an active system consolidation supported by sleep in
essential aspects, quite a number of questions are left
unanswered. The reactivation and redistribution of
memory during sleep needs to be studied in more detail
to answer, e.g. whether selectivity in offline consolidation
is established already during reactivation of the tempo-
rarily stored materials or alternatively, in the process of
adapting these memories to pre-existing long-term
memories. Do memories not selected for consolidation
just decay over time, or is there an active unlearning of
hippocampal memories during sleep? Is the involvement
of the prefrontal-hippocampal system at encoding man-
datory for system consolidation to occur during suc-
ceeding sleep, or are system consolidation processes
established during sleep also in other systems indepen-
dent of or interacting with the prefrontal-hippocampal
system? What are the underlying neurophysiological
mechanisms and what is the role of REM sleep in this
process? These are just a few of the many questions in a
rapidly growing area of research which uses sleep as a
window to the understanding of memory and, eventually,
of consciousness.
Acknowledgments This work is supported by grants from the
Deutsche Forschungsgemeinschaft (SFB 654 ‘Plasticity and Sleep’’)
and the BMBF (Bernstein Focus Neuronal Foundations of Learning).
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
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    • "It is possible that in the immediate aftermath of learning, this connection is active while undertaking the task using visuo-spatial imagery, but not successfully tuned to performance (as the 30 min group is at chance level). In the 24 h group, the connection has been modified by consolidation processes during SWS such that more SWS has strengthened the connection through reactivation (Born & Wilhelm, 2012), while insufficient SWS has failed to counteract the effects of synaptic homeostasis driven by pre-task activation from the first session (Tononi & Cirelli, 2014 ), and the connection strength is now related to subsequent performance. This intriguing hypothesis fits with our data and the iOtA theory (), but further evidence is certainly needed to confirm this. "
    [Show abstract] [Hide abstract] ABSTRACT: Extracting regularities from a sequence of events is essential for understanding our environment. However, there is no consensus regarding the extent to which such regularities can be generalised beyond the modality of learning. One reason for this could be the variation in consolidation intervals used in different paradigms, also including an opportunity to sleep. Using a novel statistical learning paradigm in which structured information is acquired in the auditory domain and tested in the visual domain over either 30 min or 24 h consolidation intervals, we show that cross-modal transfer can occur, but this transfer is only seen in the 24 h group. Importantly, the extent of cross-modal transfer is predicted by the amount of slow wave sleep (SWS) obtained. Additionally, cross-modal transfer is associated with the same pattern of decreasing medial temporal lobe and increasing striatal involvement which has previously been observed to occur across 24 h in unimodal statistical learning. We also observed enhanced functional connectivity after 24 h in a network of areas which have been implicated in cross-modal integration including the precuneus and the middle occipital gyrus. Finally, functional connectivity between the striatum and the precuneus was also enhanced, and this strengthening was predicted by SWS. These results demonstrate that statistical learning can generalise to some extent beyond the modality of acquisition, and together with our previously published unimodal results, support the notion that statistical learning is both domain-general and domain-specific.
    Full-text · Article · May 2016 · Cortex
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    • "Finally, the amygdala relates to myriad brain functions, such as fear conditioning and memory processing (LeDoux, 1992; Goldstein, 1992; McGaugh et al., 1996; Choi et al., 2010). Moreover, synchronization of neural substrates through neural oscillations has been described as an important framework for function (Steriade et al., 1993; Steriade, 1993; Born and Wilhelm, 2012). Thus, it seems very pertinent to inquire whether desynchronizing NPS applied to the amygdala has any influence on normal brain function. "
    [Show abstract] [Hide abstract] ABSTRACT: Many patients with epilepsy do not obtain proper control of their seizures through conventional treatment. We review aspects of the pathophysiology underlying epileptic phenomena, with a special interest in the role of the amygdala, stressing the importance of hypersynchronism in both ictogenesis and epileptogenesis. We then review experimental studies on electrical stimulation of mesio-temporal epileptogenic areas, the amygdala included, as a means to treat medically refractory epilepsy. Regular high-frequency stimulation (HFS) commonly has anticonvulsant effects and sparse antiepileptogenic properties. On the other hand, HFS is related to acute and long-term increases in excitability related to direct neuronal activation, long-term potentiation, and kindling, raising concerns regarding its safety and jeopardizing in-depth understanding of its mechanisms. In turn, the safer regular low-frequency stimulation (LFS) has a robust antiepileptogenic effect, but its pro- or anticonvulsant effect seems to vary at random among studies. As an alternative, studies by our group on the development and investigation of temporally unstructured electrical stimulation applied to the amygdala have shown that nonperiodic stimulation (NPS), which is a nonstandard form of LFS, is capable of suppressing both acute and chronic spontaneous seizures. We hypothesize two noncompetitive mechanisms for thetherapeutic role of amygdala in NPS, 1) a direct desynch-ronization of epileptic circuitry in the forebrain and brain-stem and 2) an indirect desynchronization/inhibition through nucleus accumbens activation. We conclude by reintroducing the idea that hypersynchronism, rather than hyperexcitability, may be the key for epileptic phenomena and epilepsy treatment.
    Full-text · Article · Apr 2016 · Journal of Neuroscience Research
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    • "Working across time zones, for example, can tax employees' cognitive resources by requiring them to work at unconventional hours as they coordinate with coworkers located on other continents (Carmel & Espinosa, 2012 ), ultimately overextending workers and threatening their learning capacity. Paradoxically, when global team members prioritize late night meetings and compromise their sleep, sleep deprivation can impair their neurocognitive performance and memory consolidation, which are necessary for learning (Born & Wilhelm, 2012). Our interviews suggest that, eventually, depletion may lead to fatigue and can even affect workers' perceptions of work, leading them to have more negative than positive appraisals. "
    Full-text · Article · Mar 2016 · Journal of International Business Studies
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