The right time to learn: Mechanisms and optimization of spaced learning

Abstract

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning.

Full text

Repetitive training helps to form a long-term memory.
Training or learning that includes long intervals between
training sessions is termed spaced training or spaced
learning. Such training has been known since the sem-
inal work of Ebbinghaus to be superior to training that
includes short inter-trial intervals (massed training or
massed learning) in terms of its ability to promote mem-
ory formation. Ebbinghaus stated: “with any considera-
ble number of repetitions a suitable distribution of them
over a space of time is decidedly more advantageous
than the massing of them at a single time” (REF.1). His
studies were based on the self-testing of acquired mem-
ory for lists of syllables, but the superiority of spaced
training has now been established for many additional
forms of human learning. For example, spaced learning
is more effective than massed learning for facts, concepts
and lists2–4, skill learning and motor learning5,6, in class-
room education (including science learning and vocab-
ulary learning)7–9, and in generalization of conceptual
knowledge in children10. Spaced training also leads to
improved memory in invertebrates, such as the mollusk
Aplysia californica11–14, Drosophila melanogaster15,16 and
bees17, and in rodents18,19 and non-human primates20,21.
Memory extinction is commonly considered to involve the
formation of a new memory, and in rat fear conditioning
spaced extinction trials are more effective than massed
trials at establishing new memories22.
Although it has been established that spaced train-
ing is superior to massed training in terms of inducing
memory formation, key questions remain. What are
the mechanisms underlying this superiority? Is it pos-
sible to use this mechanistic information to determine
the optimal intervals between learning trials? If so, are
fixed, expanding or irregularly spaced intervals opti-
mal? Another key question is whether an understand-
ing of the mechanisms for optimal intervals can provide
insights into the design of pharmacological approaches
for memory enhancement. Computational models based
on such a mechanistic understanding may be able to pre-
dict more complex approaches to memory improvement
in which the application of multiple drugs, or combi-
nations of drugs and training protocols, can enhance
memory or treat deficits in learning andmemory.
In this Review, we describe how new insights from
molecular studies may help to explain the effectiveness of
spaced training, and how the molecular findings relate to
the traditional learning theories that aim to account for
this effectiveness. We also review how models of signal-
ling pathways that are involved in synaptic plasticity can
suggest, and experiments empirically validate, training
protocols that improve learning and that rescue plas-
ticity impaired by deficits of key molecular components.
Finally, we discuss recent models that have suggested
combined-drug therapies that may further enhance some
forms of learning and that may have synergistic effects
with optimized spaced learning on memory formation.
Traditional learning theories
We briefly summarize three of the well-known cognitive
theories that have been proposed to explain the super-
iority of spaced training over massed training: encod-
ing variability theory, study-phase retrieval theory and
deficient-processingtheory.
Encoding variability theory23–25 posits that repeated
stimulus presentations or learning trials are more likely
to occur in multiple contexts if they are spaced further
Department of Neurobiology
and Anatomy, W.M.Keck
Center for the Neurobiology
of Learning and Memory,
TheUniversity of Texas
Medical School at Houston,
P.O.BOX20708, Houston,
Texas 77030, USA.
Correspondence to J.H.B.
John.H.Byrne@uth.tmc.edu
doi:10.1038/nrn.2015.18
Published online 25 Jan 2016
Memory extinction
The decline of a learned
behavioural response to a
conditioned stimulus following
the withdrawal of
reinforcement stimuli that
werepreviously paired
withrepetitions of the
conditionedstimulus.
The right time to learn: mechanisms
and optimization of spaced learning
Paul Smolen, Yili Zhang and John H.Byrne
Abstract | For many types of learning, spaced training, which involves repeated long inter-trial
intervals, leads to more robust memory formation than does massed training, which involves
short or no intervals. Several cognitive theories have been proposed to explain this superiority,
but only recently have data begun to delineate the underlying cellular and molecular
mechanisms of spaced training, and we review these theories and data here. Computational
models of the implicated signalling cascades have predicted that spaced training with irregular
inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced
training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired
synaptic plasticity and learning.
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Reinforcement
A broad term used here to
describe a stimulus or item
that enhances the strength or
lifetime of a memory.
Habituation
A decrease in the behavioural
response to a stimulus
following frequent repetitions
of that stimulus; this term is
distinct from extinction,
because habituation can
denote a decrease in
responseto a stimulus that
wasnever paired with a
reinforcing stimulus.
Memory reactivations
These are reinstatements of
conditioned behavioural
responses or of neural activity
associated with specific
responses and can be elicited
by presentation of
aconditioning stimulus or of
the context in which learning
previously occurred, or be
spontaneous, occurring as
apart of normal ongoing
neural activity.
apart in time and that a memory trace for repeated tri-
als therefore includes elements of each of these contexts.
Thus, spaced training would tend to bind together more
contexts and hence form a more robust memory, as a
greater number of testing contexts could elicit retrieval
of thememory.
Study-phase retrieval theory26–29 posits that spaced
stimulus presentations or learning trials are more effec-
tive than massed trials for memory reinforcement because
each spaced trial elicits retrieval and reactivation of a
memory trace that was formed by the preceding trial. By
contrast, with short massed trials, the preceding memory
trace is still active, so it is not retrieved or reactivated and
therefore the memory cannot be reinforced. Study-phase
retrieval theory also accounts for a decline in learning
in trials with excessively long intervals because in those
cases the preceding memory trace can no longer be
retrieved. A recent variant, retrieved context theory, also
incorporates elements of encoding variability theory and
has succeeded in predicting the results of subsequently
performed spaced learning experiments in humans30.
Deficient-processing theory posits that spaced train-
ing forms a stronger memory than does massed training
because, in the latter, some processes that are necessary
to form memories are not effectively executed. The
reasoning here becomes clearer by examining variants
of this theory that specify the nature of the deficient
process. One variant posits that excess habituation dur-
ing massed trials prevents effective reinforcement of
memory traces31, whereas others posit that there is a
failure to consolidate a memory (known as consolida-
tion theory)32,33, a lack of voluntary attention to massed
presentations31,34, or a lack of cognitive rehearsals or
memory reactivations within the short intervals that are
characteristic of massed training27,35.
Consolidation occurs as a memory trace becomes
more fixed and stable with time after training2,36. Thus,
consolidation theory37–39 posits that a long-term mem-
ory trace is more efficiently stabilized or strengthened
by spaced trials. The lack of cognitive rehearsals variant
of deficient-processing theory might also be considered
a more specific form of consolidation theory, because
it assumes that a minimum number of rehearsals, or
autonomous reactivations, are required to consolidate
a memory trace. Variants of deficient-processing theory
and relevant experiments are discussed in more detail
in REFS28,40,41. Below, we focus substantially on con-
solidation theory because, of all the traditional learning
theories, it seems to be most closely aligned with our
current understanding of the cellular and molecular
mechanisms ofmemory.
Landauer37 was one of the first researchers to develop
a conceptual model of the ways in which consolidation
principles could explain the effectiveness of spaced
training. Although the model was originally developed
to explain the effects of short spacing intervals on mem-
ory formation, it can readily be generalized for the effects
of arbitrarily long intervals (FIG.1). The model is based
on two assumptions. First, the state of a neural circuit
following the first learning trial is such that a second
re inforcing trial soon after will not markedly increase the
consolidation of the learning trace resulting from the first
trial (FIG.1a). Thus, in massed training, overlap between
traces, which may cause saturation of an unspecified
molecular mechanism, diminishes the summed impact
of the traces on the consolidation of memory. Only when
the effects of the first trial decay can the effects of a sec-
ond trial be fully expressed (FIG.1b,c), leading to greater
potential consolidation of a memory in spaced training
than in massed training (greater net gain; see FIG.1d). The
second assumption is that the probability that the second
trial can successfully reinforce the first trial declines with
time (FIG.1e). Actual consolidation is the product of these
two assumptions, yielding a prediction of an optimal
interval for spaced learning (FIG.1f).
Peterson38 described a similar model that focused
on the dynamics of verbal learning. Furthermore,
Wickelgren39 extended consolidation theory by positing
that the resistance of a memory trace to decay increases
with the age of the trace over the total duration of a
spaced learning protocol. Thus, a trace would become not
only strong but also highly resistant to decay following
spacedtrials.
Molecular traces of time
Substantial progress has been made in understanding the
molecular mechanisms of memory. Given this progress, in
this section we focus on potential molecular mechanisms
of the spacing effect on long-term memory formation.
There is now agreement that learning is implemented,
at least in part, by changes in synaptic strength (synap-
tic plasticity). For example, fear-conditioned memories
can be alternately erased and reinstated by long-term
depression (LTD) and long-term potentiation (LTP),
respectively, of a defined synaptic pathway42. Thus, the
molecular processes that are essential for spaced learning
might reinforce extantLTP.
Reliable correlates of LTP are the remodelling and
enlargement of postsynaptic dendritic spines, which are
small protrusions that are associated with most excita-
tory synapses43. Thus, studying the differential dynamics
of dendritic spine remodelling following massed versus
spaced stimuli is likely to provide insight into processes
underlying the effectiveness of spaced training. Studies
using rat hippocampal slices found that LTP induced by
multiple trains of theta-burst stimuli was accompanied
byextensive remodelling of synaptic ultrastructures44,45
and that subsequent spaced trains of theta-burst stim-
uli, with intervals of 60minutes or more between the
trains, were needed for optimal reinforcement of LTP46.
Stimulated dendritic spines were remodelled over a period
of more than 1hour, leading to enlargement of the exist-
ing functional postsynaptic density45 and the presynaptic
active zone44. The resulting increase in the numbers of
AMPA-type and NMDA-type glutamate receptors at the
synapse correlated with the magnitude ofLTP.
Two hypotheses that involve spine remodelling
have been put forward to explain the greater efficacy
of spaced trials over massed trials in memory forma-
tion. These hypotheses have a common theme, which
is that the learning process includes a refractory period
during which the second of two closely spaced stimuli
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would be ineffective in enhancing the effects of the first
(FIG.2a). One hypothesis is that spaced but not massed
repetitions of a stimulus allow the refractory period to
be overcome and lead to repeated enlargement of a set
of spines and strengthening of the synaptic connections
mediated by these spines47 (FIG.2b). A second, not mutu-
ally exclusive, hypothesis47,48 is that molecular processes
enable later spaced stimuli to induce LTP at spines that do
notundergo initial enlargement. In this case, spaced, but
not massed, inter-trial intervals would allow for a molec-
ular process termed ‘priming’ to be completed at these
additional spines. After being primed, these spines would
be strengthened by subsequent stimuli and incorporated
into the memory trace (FIG.2c). Currently, the molecular
components of such a priming process are notknown.
Through the use of Schaffer–commissural projec-
tions in rat hippocampal slices, two studies47,48 have
characterized the recruitment of additional synaptic
contacts with the application of spaced stimuli. Theta-
burst stimuli applied at intervals of 10 or 40minutes
did not cumulatively increase LTP. However, for longer
intervals (60 or 90minutes), a cumulative increase in
LTP was observed over three bursts of stimulation. Each
theta-burst stimulus led to actin filament polymer-
ization in spines, which is known to be important for
the stabilization of LTP49. The second theta-burst stim-
ulus yielded polymerization in spines that were not
apparently affected by the first stimulus, if the second
followed the first by 60 or 90minutes. These data do
not suggest that successive theta-burst stimuli further
strengthen the efficacy of the same spines. Instead, they
suggest that the first theta-burst stimulus initiates prim-
ing at all synaptic contacts of the stimulated afferents
but only initiates consolidation and strengthening at a
subset of contacts. Spines that undergo priming but not
consolidation exhibit a refractory period of ~60min-
utes, suggesting that priming takes time to complete
(FIG.2a). If the second theta-burst stimulus is applied
after the refractory period, some or all of the primed
spines undergo consolidation. These data are consistent
with the second hypothesis presented in the preceding
paragraph, because the first theta-burst stimulus appears
to enlarge and strengthen some spines but, at others, it
only initiates priming. These primed spines can then be
strengthened by the second theta-burst stimulus.
The dynamic properties of transcription factors and
their interactions could also account for the superior
efficacy of spaced training. LTP that persists for several
hours or more requires translation and transcription50,51,
which is reliant on key transcription factors such as
Figure 1 | Early conceptual model of how learning trace dynamics generate an optimal interval. As described by the
early model of Landauer37, spaced training is more effective than massed training at strengthening some form of trace
corresponding to memory storage in the brain, although this conceptual model does not posit a biochemical or structural
form for the trace. This model posits that memory formation becomes more effective with longer inter-stimulus intervals
between training sessions because of decreasing temporal overlap between successive, short-lived learning traces.
Theselearning traces do not themselves constitute a memory. However, their net effect contributes to the formation of
along-lived memory trace. a–c | Learning traces elicited by two successive trials are shown. The model assumes that, for
each value of the inter-trial interval (ITI) length, a quantity denoted ‘net gain owing to the reinforcing trial’ is proportional
to the red area. Shorter intervals are associated with more overlap of learning traces and less net gain. Thus, a reinforcing
trial is most effective after a refractory period following the preceding trial. For this conceptual model, units for amplitude
and time are arbitrary. d | A greater summed effect, or net gain, of reinforcing trials occurs for longer inter-stimulus
intervals. The effect reaches a plateau for long intervals as the overlap between successive learning traces reaches zero.
e | Over longer times, a different quantity — the probability that a reinforcing trial will be effective at all in reactivating
processes that constituted the preceding learning trace — declines. f | An optimum interval for maximizing the strength
ofthe long-lived memory trace results when the greater net gain of reinforcement at longer intervals (from part d) is
multiplied by the slowly declining probability that a reinforcement will reactivate a previous learning trace (from part e).
The optimum interval for net learning is the one that produces the peak level of the trace in part f.
Nature Reviews | Neuroscience
a Short interval c Long intervalb Medium interval
d Net gain (a–c)f Net learning (product of e and d)e Reinforcement probability
Learning trace
magnitude
Length of interval
Time Time Time
Amplitude
Amplitude
Amplitude
Length of intervalLength of interval
Learning trace
magnitude
Learning trace
magnitude
Reinforcing trial First trial Net gain due to reinforcing trial
ITI ITI ITI
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cyclic AMP-responsive element (CRE)-binding protein
(CREB)52. Spaced training may be more effective, in part,
because it may allow sufficient time for transcription fac-
tors such as CREB to be activated, bind to promoters and
induce a round of transcription for the consolidation of
LTP 53 or for long-term facilitation (LTF) of synapses54. In
massed training, the trials would come too close together
to initiate separate rounds of transcription. Indeed, in
co-cultures of sensory and motor neurons from A.cali-
fornica, five spaced applications of 5-hydroxytryptamine
(5-HT; also known as serotonin), each lasting 5minutes
with an inter-stimulus interval of 20minutes (an ana-
logue of spaced training), robustly elicit LTF that lasts
for more than 24hours14, whereas 5-HT applied contin-
uously over 25minutes (an analogue of massed training)
fails to yield reliableLTF.
In these sensory neurons, levels of the transcrip-
tion activator CREB1 are elevated for at least 24hours
after the spaced 5-HT treatments54,55. This prolonged
elevation of CREB1 levels is due to a positive feedback
loop in which this protein, by binding to a CRE regu-
latory element near creb1, increases the expression of
creb1 (REFS54,55) and other genes that are upregulated by
CREB1. In addition, in these sensory neurons, the level
of the transcription repressor CREB2 shows a late drop
at ~12hours after treatment56. This drop in the level of
CREB2, coupled with the rise in the level of CREB1,
plausibly corresponds to an increased potential for gene
induction. Thus, an additional 5-HT pulse near 12hours
after treatment might optimally reinforceLTF.
LTF at these sensorimotor synapses is associated
with a simple form of learning, long-term sensitization
(LTS) of withdrawal reflexes. Invivo, four spaced elec-
trical stimuli (with 30minutes intervals between the
stimuli) yielded LTS that lasted for more than 24hours,
with weak residual LTS being detectable at 4days post-
training, and repetition of this spaced protocol once per
day for 4days yielded much stronger LTS that lasted for
Figure 2 | Model and hypotheses describing synaptic strengthening during spaced learning. a | In the refractory-state
model, spaced stimuli (left panel; stimulus 1, followed substantially later by stimulus 2) cumulatively strengthen a memory
trace (blue time course). By contrast, massed stimuli (right panel; stimulus 1 followed shortly after by stimulus 2) fail to
cumulatively strengthen the memory trace. b | The cumulative synaptic strengthening in spaced training may be due to
progressive enhancement of long-term potentiation (LTP), which could result from successive increases in the strength of
the same synaptic contacts (shown here as successive increases in the volume of the same postsynaptic dendritic spine).
Thus, in one of two current hypotheses describing synaptic strengthening during spaced learning, stimulus 1 enlarges a
population of spines. If stimulus 2 follows shortly after the first stimulus (as in massed training), it cannot further affect
spines. However, if stimulus 2 comes after a refractory period (as in spaced training), it can further enlarge the same
population of spines. c | Alternatively, enhancement of LTP could result from successive rounds of strengthening of new
synaptic contacts. Thus, in the second current hypothesis, stimulus 1 only enlarges a subset of affected spines, but primes
additional spines. If stimulus 2 follows shortly after stimulus 1 (as in massed training), it has no effect. If stimulus 2 comes
later (as in spaced training), it does not further enlarge the first subset of spines. Instead, stimulus 2 enlarges those spines
that were primed, but not enlarged, by stimulus 1.
Nature Reviews | Neuroscience
Time Time
Memory strength
Memory strength
a
b c
Memory trace Memory trace
11
2
2
Before
stimulus
Stimulus 1
Stimulus 2
(spaced
training)
Stimulus 1
Stimulus 2
(massed
training)
Before
stimulus
Stimulus 2
(spaced
training)
Stimulus 2
(massed
training)
Primed
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more than 1week13,57,58. These data suggest that, in this
system, the dynamics of transcription activation and
gene expression have slow components that can summate
over multiple days, yielding long-lastingmemory.
Recent data also illustrate that, in the hippocampus,
CREB and CCAAT enhancer-binding protein (C/EBP),
another transcription factor that is important for LTP, can
remain active for many hours after learning. Following
inhibitory avoidance training in rats, late peaks in
brain-derived neurotrophic factor (BDNF) expression
and in C/EBP expression occur at ~12hours post-
training, and inhibiting BDNF action at this time blocks
memory maintenance59. These BDNF dynamics result
from a positive feedback loop in which C/ebp induction
leads to Bdnf upregulation, with the resulting increase
in BDNF levels further activating the C/EBP signalling
pathway60. Although this slow feedback loop was acti-
vated by single-trial training rather than spaced training,
it would be of interest to model these dynamics, and to
examine whether an additional spaced trial at ~12hours
post-training, leading to a second induction of C/ebp
at the time of elevated C/EBP levels, might optimally
reinforce learning. A second prediction would be that
massed stimuli are less effective if repeated at an interval
too brief to allow the transcription regulation, and thus
Bdnf expression, that is necessary to activate this feedback
loop. Insights that can be obtained from computational
models of learning are discussed later in the article.
On a shorter timescale, the dynamics of second mes-
sengers, kinases and phosphatases may contribute to the
superiority of spaced training. One study in mice61 found
marked phosphorylation and activation of CREB in the
hippocampus and the cortex when object recognition
trials were separated by an interval of 15minutes but not
by an interval of 5minutes. Protein phosphatase 1 (PP1)
appeared to be necessary for this spacing effect, because
PP1 inhibition allowed the shorter interval to activate
CREB. A study involving A.californica sensory neuron–
motor neuron co-cultures62 found that protein kinase C
(PKC) is activated to a greater extent during a massed
stimulus (continuous 5-HT application) than during a
spaced stimulus (15-minute intervals between applica-
tions). It is known that PKC acts to downregulate protein
kinase A (PKA) and that PKA activation is necessary for
LTF; thus, these data delineate crosstalk between signal-
ling pathways such that LTF is suppressed, in part, by
stronger PKC activation during massed training.
Another study16 characterized the dynamics of
mitogen-activated protein kinase (MAPK) and of MAPK
phosphatase in D.melanogaster. In an olfactory learning
protocol, each spaced training trial generated a distinct
wave of MAPK activity, whereas massed training trials
were too close together to generate distinct waves. The
authors therefore hypothesized that effective learn-
ing depended on the generation of distinct waves of
MAPKactivity.
Another phosphorylation-based mechanism has also
been hypothesized to help to explain the efficacy of spaced
intervals in D.melanogaster. Spaced (15-minute) intervals
were more effective than massed (1-minute) intervals in
inducing olfactory learning, even given the same total
training time (and thus more massed presentations)63.
Two isoforms of D.melanogaster CREB — dCREB2-a
and dCREB2-r — can activate and repress transcription,
respectively. The authors proposed64 that the kinetics
of the phosphorylation of these isoforms differed such
that the kinase activation generated by less frequent,
spaced trials was sufficient to phosphorylate and activate
dCREB2-a, whereas dCREB2-r could only be effectively
phosphorylated by massed trials. Thus, training involving
spaced intervals could maximally activate transcription
and possibly induce the formation of long-term mem-
ory by activating dCREB2-a but not the counteracting
repressor dCREB2-r.
Computational simulations have supported the
plausi bility of this mechanism65, but it has not been vali-
dated empirically. However, it appears to be likely that a
similar type of mechanism that is based on competition
between an activator of long-term memory formation
and a repressor, with the repressor only activated at short
intervals, might be needed to explain any similar data
in which massed training is less effective than spaced
training even given equal total trainingtimes.
In experiments with A.californica, when two electric
shocks were given to induce LTS, maximal LTS was pro-
duced when the inter-stimulus interval was 45minutes.
LTS was not produced with intervals of 15 or 60minutes66.
The 45-minute optimum was associated with activation
of MAPK. Following either a single 5-HT pulse or a
single electric shock, MAPK activation peaked at or near
45minutes post-trial12,66; thus, a 45-minute interval might
optimally reinforce the effects of MAPK. It is known that
this delayed MAPK activation requires protein synthe-
sis12, although the upstream mechanisms underlying the
dynamics of the peak in MAPK activity at ~45minutes are
not well understood. Nevertheless, the key finding from
these studies is that delayed activation of MAPK is intim-
ately associated with the effectiveness of spaced stimuli to
induce long-termmemory.
Similarly, training with intervals of 60minutes,
but not 20 or 120minutes, enhanced object recogni-
tion learning in wild-type mice and in a mouse model
of fragile X syndrome (fragileX mental retardation1
(Fmr1)-knockout mice), at least partly by increasing syn-
aptic activation of extracellular signal-regulated kinase1
(ERK1; also known as MAPK3) and ERK2 (also known
as MAPK1)67. This 60-minute interval was predicted
to be optimal for learning because stimuli separated by
60minutes had previously been found to enhance LTP
in wild-type rodents47. Thus, in A.californica, D.melano-
gaster and mammals, MAPK activation appears to be a
component of the molecular mechanism that underlies
the spacingeffect.
Some of these molecular mechanisms appear to fit
with a theory in which spaced training sessions are effec-
tive because they reinforce the same memory trace or
group of strengthened synapses. However, spaced stim-
uli might also reinforce memory by recruiting new syn-
apses. ERK1 and ERK2 (ERK1/2) activation is needed for
some forms of LTP68, and one study69 compared ERK1/2
activation in rat hippocampal pyramidal neurons follow-
ing three spaced tetanic bursts (at 5-minute intervals)
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with that after three massed bursts (at 20-second
intervals). About twice as many dendrites with active
ERK1/2 were found following spaced bursts, suggesting
that spaced trials may recruit additional synapses on dif-
ferent dendrites for LTP. Thus, a range of molecular and
cellular mechanisms appears to contribute to the efficacy
of spaced training, in parallel or inseries.
An extremely broad range of inter-trial intervals,
from seconds to days, has been used for spaced train-
ing (FIG.3). For example, in honeybee olfactory learning,
efficient spaced training can occur with intervals as
short as 1minute17. Such brief intervals might allow for
the reinforcement of the activity of a short-lived second
messenger such as cAMP that is produced by preced-
ing trials. The dynamics of kinase activation constitute
a second substrate of spacing effects. In A.californica,
D.melanogaster and mammals, the data discussed above
indicate that commonly reported intervals, ranging from
~5minutes to 1hour, may allow for the reinforcement of
the activities of key kinases essential for LTP or LTF, and
consolidate structural changes in dendriticspines.
It is plausible that the minimum inter-stimulus inter-
val for effective learning, for a given protocol and system,
corresponds to the interval that is necessary to allow
each stimulus to contribute separately to a rate-limiting
biochemical process. For example, for rapid honeybee
olfactory learning with an effective interval of 1minute,
the rate-limiting process might be second messenger
accumulation or rapid activation of a kinase. For even
shorter intervals, the timescale of the rate-limiting pro-
cess might be too long to permit each brief stimulus to
contribute separately to the process — a group of closely
spaced stimuli would instead tend to act as just a single
stimulus. For intervals of 1minute or more, each stim-
ulus would be able to contribute a discrete increment
to the rate-limiting process, allowing effective learning.
For the spaced LTP protocol of Gall, Lynch and col-
leagues47,48, an interval of 40–60minutes is needed for
successive theta-burst stimuli to further increase LTP.
Here, the rate-limiting process would be different —
plausibly slower activation of an unspecified kinase or
other intracellular signalling event, with a time constant
near the minimum effective interval of ~40minutes.
Stimuli at intervals much shorter than this would not be
able to generate summation of the rate-limiting process
and would therefore not cause additionalLTP.
For other systems, a similar assumption may apply to
the dynamics of transcription activation. For LTF and
LTS in A.californica, transcription, as discussed above,
may constitute a rate-limiting process that helps to deter-
mine the efficacy of spaced training. However, it is evi-
dent that even for systems such as honeybee olfactory
learning that involve short, spaced intervals, effective
long-term memory formation relies on the activation
of transcription and translation, downstream of the
intracellular signalling pathways that are activated by
these intervals17,70. Reactivation of memory traces may
constitute an additional temporal substrate that under-
lies the longest reported effective intervals, on the order
of a week71. Such intervals are likely to reactivate and
reinforce consolidated patterns of strengthened synapses
that correspond to memory traces that are maintained
by neuronal network activity72. Spaced learning with
these long intervals would reactivate critical components
at these synapses, and in particular reactivate NMDA
receptors at these synapses. Studies using inducible and
reversible NMDA receptor knockouts have demon-
strated that such NMDA receptor reactivation, which
may also in part result from spontaneous neuronal activ-
ity, is required to sustain remote memory storage73,74.
Positive feedback loops that maintain key kinases and
other molecules in persistently active states at strength-
ened synapses may also contribute to such long-term
memory storage75–79. An important topic for future
research will be to further investigate the molecular pro-
cesses that support effective spaced learning in humans
that involves inter-trial intervals of a day ormore.
An implication of the work outlined above is that
multiple temporal domains of spaced training may be
engaged in spaced training (FIG.3). Indeed, an effective
protocol for LTS training in A.californica is the use of
four trials with an inter-trial interval of 30minutes,
repeated four times with a 1-day inter-trial interval13.
Thus, at least in some cases, there appears to be a hier-
archy of temporal domains of training protocols, with
briefer protocols embedded within longerones.
The above considerations, and most empirical
studies, are concerned with only typical, or minimum,
inter-trial or inter-stimulus intervals for effective spaced
learning or for the summation of LTP. Only a few studies
have delineated, for any specific system (that is, a given
species and stimulus protocol), both minimum and
maximum effective intervals. One study80 found that in
a hippocampal slice preparation, 5–10-minute intervals
between tetani were ideal for induction of LTP, and they
produced similar levels of LTP, with longer or shorter
intervals yielding both less LTP and less ERK1/2 acti-
vation. In A.californica, LTS was effectively induced by
an interval of 45minutes between electrical stimuli, but
not by intervals of 15minutes or 60minutes66. As noted
above, the authors of this study hypothesized that the
coincidence of peak MAPK activation with the second
trial was necessary for effective learning. In addition,
60-minute intervals were effective for forming object
location memory in mice with three trials, but intervals
of 20minutes or 120minutes werenot67.
Owing to the small number of such studies and the
lack of sufficient characterization of the accompany-
ing molecular processes, it is not yet possible to make
detailed statements about the ways in which intracellu-
lar signalling pathways could cooperate to generate both
minimum and maximum intervals. For maximum inter-
vals, a reasonable qualitative assumption is that each
trial or stimulus generates a separate, relatively short-
lived biochemical trace and that, for effective spaced
learning, these traces must overlap and summate, with
the summed magnitude driving long-lasting synaptic
potentiation. These dynamics would be analogous to the
necessary overlap of traces in the conceptual model of
Landauer (FIG.1a–c). For intervals longer than the max-
imum, the individual biochemical traces would decay
and not overlap.
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Recent data and learning theories
Do the biochemical and morphological mechanisms
that are proposed to contribute to the greater efficacy of
spaced training align with traditional cognitive theories?
At this point, much of the extant cellular data seem to be
compatible with the deficient-processing theory, particu-
larly two of its variants: the consolidation theory and the
lack of cognitive rehearsals theory. In the consolidation
theory, intervals between massed trials are proposed to
be too short for the consolidation and consequent sum-
mation of memory traces that are engendered by suc-
cessive trials. In the cognitive rehearsals theory, massed
trials are proposed to lead to fewer cognitive rehearsals,
or autonomous reactivations, than do spaced trials, and
therefore less cumulative consolidation and persistence
of amemory.
The required refractory period of ~1hour between
successive theta-burst stimuli to induce progressive
increments in hippocampal LTP46,47 may be in line with
the first of these variants, which is that short intervals
are insufficient for consolidation and consequent sum-
mation of memory traces. The refractory period appears
to be necessary to complete the priming of dendritic
spines that were stimulated, but not potentiated, by the
first theta-burst stimulus. Priming allows these spines to
potentiate after the second stimulus, and thus constitutes
a biochemical stimulus trace (FIGS1d,2a). Kramár etal.47
noted that in hippocampal slices, additional potenti-
ation can be induced up to 4hours after induction of
the first LTP increment81. The stimulus trace associated
with priming may therefore take at least 4hours to decay.
Such a long trace lifetime might allow a broad temporal
window for optimal trainingtrials.
In rat hippocampal slices, theta-burst stimuli lead to
proteolytic inactivation of integrin receptors at stimu-
lated dendritic spines82. These receptors are then replaced
by vesicular transport of new receptors over a period of
~40–60minutes, and it is hypothesized82 that subsequent
theta-burst stimuli at these synaptic contacts cannot
induce spine enlargement or LTP until after this replace-
ment has occurred, thus accounting for the refractory
period of ~1hour in order for a second theta-burst stim-
ulus to yield additional LTP. This receptor replacement
may constitute, at least in part, the priming of dendritic
spines discussed above. These hypothesized dynamics
may be in line with deficient-processing theory, with
receptor replacement being the necessary process that
can only occur during spaced inter-trial intervals (FIG.3).
Transcription factor activation also constitutes a bio-
chemical trace, and in some systems training may only
be effective if inter-trial intervals are long enough so that
each trial can induce a separate round of transcription
and translation. Similarly, short (massed) inter-trial
intervals may not lead to sufficient levels, or a sufficient
duration, of activated MAPK or other kinases to support
the consolidation of long-termmemory.
The variant of deficient-processing theory positing
that only spaced trials can generate sufficient cogni-
tive rehearsals or reactivations of a memory to support
long-term memory consolidation may also correspond
to the empirical finding that repeated theta-burst stim-
uli, spaced by ~1hour, can recruit additional dendritic
spines by potentiating spines that were primed by
preceding stimuli. A memory reactivation would be
analogous to a theta-burst stimulus in that both events
would initiate priming and potentiation. It also seems
plausible that repeated memory reactivations might
induce further rounds of transcription of genes involved
in LTP, such as C/ebp and other CREB-activated genes,
supporting further consolidation of long-termmemory.
To more strongly connect this variant of deficient-
processing theory to recent cellular and molecular data,
one must also assume that reactivations of a memory reac-
tivate some of the same neurons and synapses that were
Figure 3 | Different mechanisms may underlie enhancement of learning by spaced
intervals of widely varying lengths. For relatively brief inter-trial intervals (ITIs)
(bottom trace), successive trials may coincide with and reinforce peak second
messenger levels generated by preceding trials. In each trace, individual rectangles
represent individual trials, and converging lines between traces represent the
lengthening of timescales as one moves upwards in the illustration. For somewhat
longer ITIs (several minutes to ~1hour), successive trials may reinforce the peak
activities of kinases elicited by preceding trials and also elicit long-term potentiation
ofprimed dendritic spines. Intervals of this length may also, in the hippocampus, be
needed to allow replacement of inactivated receptors at stimulated spines82, enabling
succeeding stimulus repetitions to potentiate those spines. For intervals of ~1hour or
more, succeeding trials may also align with peaks in transcription factor activity and
gene expression owing to preceding trials. For the longest ITIs (many hours or longer),
succeeding trials may reactivate and thereby further potentiate consolidated memory
traces. All of these processes are likely to contribute to the consolidation of long-term
memory, in many if not all species. However, depending on the ITI length used in a
particular spaced learning protocol, the dynamics of a particular type of process
(forexample, kinase activation) may contribute in particular to the efficacy of
spacedlearning. Also, trials at one temporal domain (for example, 1day) may be
unitaryevents, but also may constitute a block of spaced trials from another temporal
domain (forexample, minutes to hours). For example, an effective protocol for
long-term sensitization training in Aplysiacalifornica is the use of four trials with an
ITIof 30minutes, with this block repeated four times with a 1-day ITI13. Thus, some
effectivetraining protocols consist of a hierarchy of temporal domains of training
sessions, with briefer sessions embedded within longer ones. In this illustration, intervals
are shown with regular spacing, but more effective learning may occur with irregular
spacing(FIG.4).
Nature Reviews | Neuroscience
ITI ≥ ~ 1 day
(reactivation of stored memory)
ITI minutes to ~ 1 hour
(kinase activities and replacement
of receptors)
ITI seconds to minutes
(second messengers)
ITI ≥ 1 hour
(transcription and translation)
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activated in the original learning sessions. In that way,
the rehearsals and learning trials would reinforce mem-
ory in the same way. This assumption seems plausible but
requires further empirical investigation. Although finer-
grain analyses are necessary, a study using functional MRI
during verbal learning supports this assumption83. In this
study, a specific brain region associated with rehearsal of
verbal memory, the left frontal operculum, was activated
more during spaced learning of paired-word associations
than during massed learning. We note that these pos-
ited memory reactivations, on timescales of ~1hour or
longer, are distinct from voluntary rehearsals of a memory
on a short timescale (seconds or ~1minute). Substantial
behavioural evidence suggests that this latter voluntary,
short-term rehearsal is not essential for spaced learning31,34.
The remaining variants of the deficient-processing
theory, which focus on habituation or on a lack of vol-
untary attention during massed presentations, do not
appear to relate as readily to the current single-neuron
data. These variants have also been argued not to readily
accommodate certain verbal learning observations2.
With regards to encoding variability theory, data on
neur onal network dynamics, rather than single-neuron
data, will be needed to determine to what extent the
binding of contexts to memory occurs, which is required
in this theory. Similar data will also be needed to assess
whether the binding of memories of later trials to those
of earlier trials occurs, which is required in study-phase
retrieval theory. It will be important to reassess all of
these competing spaced learning theories as more infor-
mation becomes available on the dynamics of memory
networks. Indeed, different theories may be more or less
applicable to different memory systems.
Irregular spacing can enhance learning
Attempts to optimize the spacing effect have generally been
based on trial-and-error approaches. Consequently, most,
if not all, training protocols used in animal and human
studies are probably not optimal. For almost all learning
paradigms, the training intervals are fixed, although in one
type of spaced training paradigm, the intervals between
sessions progressively lengthen2,84. However, a meta-
analysis2 and a text learning study84 found no substantial
evidence for the superiority of this approach in terms of
promoting long-term memoryformation.
It seems to be evident that at least part of the improve-
ment in learning that is found with spaced training pro-
tocols can be explained by the dynamic relationships
between the training trials and the underlying cellular
and molecular mechanisms that are associated with
memory formation (FIG.3). But is the inverse possible?
Can knowledge of the dynamics of the memory mech-
anisms be used to enhance memory processing by pre-
dicting optimal training protocols, possibly with irregular
training intervals? One approach is to develop models of
the biochemical cascades that underlie memory forma-
tion and use simulations to rapidly test the effectiveness of
different training protocols85. In recent years, models have
described the dynamics of the biochemical reactions that
transduce stimuli into LTP86–88. These models have differ-
ential equations that simulate and predict the dynamics of
the activities of key molecular species. Simulations have
reproduced the dynamics of MAPK during LTP induc-
tion80,86,88. Models have also simulated the activity time
courses of PKA, calcium/calmodulin-dependent protein
kinaseII (CaMKII), other key enzymes and downstream
transcription factors during LTP induction88–90. Each
signalling cascade in these models displays a character-
istic activity time course; thus, it is likely some irregular
sequence of intervals would be predicted to maximize the
induction of LTP. For example, subsequent trials that are
delivered at times that coincide with kinase activity peaks
might optimally reinforce learning.
One study from our laboratory developed a model
describing the 5-HT-induced PKA and ERK signalling
pathways that are essential for LTF in A.californica85.
In the model (FIG.4a), the necessity of PKA and ERK
activation for LTF was simply represented with a varia-
ble termed ‘inducer’. The value of inducer was propor-
tional to the product of PKA and MAPK activities. The
amount of LTF and LTS was predicted to increase with
an increase in the peak value of inducer. Ten thousand
different protocols consisting of five trials that were sepa-
rated by intervals of 0– 45minutes were simulated (FIG.4b).
The ability to simulate and predict the effects of so many
protocols in a relatively short space of time represents a
distinct advantage of computational studies over empirical
studies. The simulations determined that, of these proto-
cols, a massed protocol (FIG.4b) produced the lowest peak
value of inducer, consistent with data that massed 5-HT
application fails to produce LTF14. The ‘best’ protocol,
yielding the highest peak value for inducer, termed the
‘enhanced’ protocol (FIG.4b), had irregular intervals. The
protocol termed the ‘standard’ protocol (five 5-minute
pulses of 5-HT, with uniform inter-stimulus intervals of
20minutes) (FIG.4b) has been commonly used to induce
LTF in empirical studies for ~30years91. This standard
protocol yielded an intermediate peak value of inducer
and was predicted to have an intermediate effectiveness
(FIG.4c). These predictions were empirically validated. The
magnitude of LTF and LTS produced by the enhanced
protocol exceeded that produced by the standard proto-
col85 (FIG.4d). An explanation for this enhancement of LTF,
consistent with data11,12,92, is as follows. In response to each
5-HT pulse, PKA activity increases rapidly and decays
rapidly (FIG.4c). MAPK activity rises and decays more
slowly, not peaking until ~45minutes after a pulse. The
four initial pulses initiate a surge of MAPK activity, which
peaks near the time of the last pulse. This last pulse acti-
vates PKA, so that a PKA peak is approximately coinci-
dent with peak MAPK activation, maximizing inducer
and the predictedLTF.
If irregularly spaced protocols can enhance normal
learning, might modelling also predict protocols capable
of restoring learning that is impaired by a genetic muta-
tion or other physiological insults? A recent study90 tested
this hypothesis. CREB-binding protein (CBP) is an acetyl-
transferase and essential co-activator for several transcrip-
tion factors, including phosphorylated CREB (pCREB).
CBP is also required for the consolidation of long-term
memory93. Mutations that decrease CBP activity cause
a human genetic disorder termed Rubinstein–Taybi
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syndrome (RTS)94, which is associated with intellectual
disability and learning deficits, and Cbp+/− mice show
impaired LTP and long-term memory95. The recent
study90 used small-interfering RNA (siRNA) knockdown
of CBP in A.californica sensory neurons to impair LTF.
In this study, the model previously used85 to predict opti-
mal, irregularly spaced protocols that would enhance LTF
(FIG.4a) was extended to represent induction of c/ebp, a
transcription factor known to be essential for LTF96. In
simulations of the effects of different spaced protocols,
greater peak levels of phosphorylated C/EBP (pC/EBP)
were taken to predict greater LTF. Simulations showed
a substantial decrease in pC/EBP levels when the level
of CBP was reduced by a decrement that corresponded
to the siRNA effect. A ‘rescue’ protocol with irregularly
spaced intervals was predicted to restore peak pC/EBP
levels and, correspondingly, LTF. This rescue protocol
was empirically validated to restore normal LTF in A.cali-
fornica. A similar predicted rescue protocol of irregu-
larly spaced intervals rescued a deficit in LTF that was
produced by siRNA knockdown of CREB1 (REF.97).
Although these empirical studies were conducted
in A.californica, it should be noted that key molecular
mechanisms of memory are substantially conserved from
simple model organisms such as A.californica to mam-
mals52,96. For example, LTF and LTP both rely on PKA
and ERK activation12,92,98 and both rely on co operative
gene induction by pCREB and CBP53,96,99,100. LTF relies
on deactivation of CREB2, a transcriptional repressor101.
Similarly, relief of transcriptional repression owing to
ATF4, a mammalian analogue of CREB2, seems to be
important for the maintenance of hippocampal LTP102,103.
Thus, the results with A.californica suggest that it may be
possible, in complex organisms including mammals, to
computationally predict the efficacies of numerous learn-
ing or training protocols, a process that is impractical
using empirical studiesalone.
Given that knowledge of the underlying biochemical
cascades can help to develop models to predict optimal
training protocols, can models also be used to predict
pharmacological targets to improve memory? The time
may also be right for such an approach. For example, if
simulated LTP deficits were rescued by combined param-
eter changes corresponding to known drug effects, these
‘best’ parameter combinations might prioritize drug
combinations for testing in animal models. A recent
study104 took a first step by modelling LTP induction and
transcriptional regulation by CREB, and simulating the
effects of drugs on LTP by altering the parameters of the
model. In this model, the magnitude of LTP induction
was represented by an increase in a synaptic weight varia-
ble. LTP impairment seen in a mouse model of RTS95 was
first simulated. Then, starting from this simulation, the
parameters were altered in ways corresponding to plau-
sible single-drug effects. However, no single-drug effect
completely rescued LTP. Thus, pairs of parameter changes
were considered, corresponding to plausible paired-drug
effects. Two pairs were identified that restored LTP. In the
first case, an increased rate constant for histone acetyl-
ation, corresponding to application of an acetyltrans-
ferase activator, was paired with an increased duration of
Figure 4 | Dynamics of a model that has successfully predicted greater efficacy
fora learning protocol with irregularly spaced intervals. a | A simplified
mathematical model85 describes the activation and effects of two key kinases
necessary for long-term facilitation (LTF), a cellular correlate of a simple form of
learning, long-term sensitization. Brief applications of 5-hydroxytryptamine (5-HT)
activate protein kinaseA (PKA) by increasing the levels of the secondary messenger
cyclicAMP, and activate the extracellular signal-regulated kinase (ERK) isoform of
mitogen-activated protein kinase (MAPK) via a RAS–RAF–MEK cascade. PKA and ERK
interact, at least in part, via the phosphorylation of transcription factors, to induce LTF.
In the model, the variable ‘inducer’represents the PKA–ERK interaction. A higher peak
value of inducer was assumed to predict a greater amplitude of LTF. b | Six samples of
the 10,000 5-HT protocols that were simulated with the model. All protocols consist
offive 5-minute pulses of 5-HT, shown as rectangular waves, with inter-pulse intervals
chosen as multiples of 5minutes, in the range of 5–50minutes. The standard protocol
(green trace) is the protocol most commonly used in studies of LTF invitro.
Theenhanced protocol (red trace) produced the largest peak value ofinducer,
whereasthe massedprotocol (blue trace) produced the smallest peak value of inducer.
The standardprotocol has uniform inter-pulse intervals of 20minutes,
whereastheenhanced protocol has non-uniform intervals of 10, 10, 5 and 30 min.
Themassed protocol has no gaps between the 5-HT pulses. c | Simulated time
coursesof activatedPKA, activated ERK and inducer in response to the standard
protocol (greentraces), the enhanced protocol (red traces) and the massed protocol
(bluetraces). d | In an empirical validation of the model’s prediction, the LTF
inducedbythe enhanced protocol, as determined by the percentage increase
intheamplitude of excitatory postsynaptic potentials (EPSPs), was greater than
theLTFproduced by the standard protocol. Figure parts c and d are from REF.85,
NaturePublishing Group.
100
200
1 2
EPSP amplitude (% control)
Days post-test
Nature Reviews | Neuroscience
ERK
5-HT
MEK
Inducer
cAMP
a b
c
Time (min)
050 100
0.5
0
150
0
PKA (µM)
0.1
Inducer (µM)
0.25
0
ERK (µM)
PKA
RAF
Time (min)
0 50 100 150 200
Protocol 10,000 (intervals = 50, 50, 50 and 50 min)
Protocol 5,522 (intervals = 30, 30, 15 and 10 min)
Standard protocol (intervals = 20, 20, 20 and 20 min)
Enhanced protocol (intervals = 10, 10, 5 and 30 min)
Protocol 10 (intervals = 5, 5, 5 and 50 min)
Massed protocol (intervals = 5, 5, 5 and 5 min)
d
Samples of 10,000 5-HT protocols
Enhanced protocol Massed protocol Standard protocol
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Drug synergism
In combined-drug treatment,
asynergistic effect of the
combination is an effect
thatisgreater than that which
would be predicted by
considering the individual
drugs as independent and
notinteracting.
stimulus-induced increase in cAMP levels, corresponding
to application of a cAMP phosphodiesterase (PDE) inhib-
itor. The second pair corresponded to a PDE inhibitor
paired with a deacetylase inhibitor. For both pairs, addi-
tive drug synergism, defined as a combined-drug effect that
exceeds the summed effects of the individual drugs, was
also evident, as quantified by a simple additive measure
(FIG.5). A subsequent empirical study by another group
did find that pairing a PDE inhibitor with a deacetylase
inhibitor was effective in rescuing a deficit of LTP in a
mouse model of Alzheimer disease105. A further extension
of these strategies might similarly predict, and empirically
test, enhancement of synaptic plasticity when pharma-
cotherapy is combined with computationally designed
spaced protocols.
Future directions
There is reason for optimism that more predictive models
for determining optimal intervals between learning trials
will be available in the near future, because the molecular
data that are necessary for the development of such mod-
els, which can delineate the dynamics of signalling path-
ways that are important for LTP and long-term memory,
continue to accumulate rapidly. However, despite the pro-
gress being made in understanding the molecular mech-
anisms of the spacing effect, some aspects of this effect
cannot be explained by current models and constitute
important directions for future research. For example, in
human verbal learning, an interesting positive correla-
tion exists between the length of inter-trial intervals for
effective spaced learning and the retention interval (that
is, the interval between the final training trial and the
test of memory retention). With relatively short retention
intervals (~1minute−2hours), training intervals in the
broad range of ~1minute to 3hours yield greater verbal
learning than do training intervals of 2days or more2.
With a longer retention interval of 1day, a 1-day train-
ing interval yielded greater learning than did a very short
(<30-second) interval. For verbal learning with a reten-
tion interval of 6months, a training interval of 7days
was superior to an interval of 3days71. This correlation
between longer training and retention intervals suggests
that longer training intervals preferentially form a mem-
ory trace with a very long lifetime. For the temporal range
of minutes versus hours, it is plausible that a longer trace
lifetime corresponds, at least in part, to increased acti-
vation of transcription by the longer training intervals.
However, this explanation may not suffice when com-
paring training intervals of ~1day versus many days. It
would be of interest to determine whether reactivation of
stored memory representations at the network level, or
transfer of these representations between brain regions,
contributes to this correlation.
Another challenge will be to use innovative strate-
gies to test the predictions of the cognitive theories for
the spacing effect. For example, consider the variant of
deficient-processing theory positing that repeated cog-
nitive rehearsals of a memory are needed for consoli-
dation. A neuronal correlate of rehearsals is, plausibly,
repeated activation of a specific neuron assembly that
serves as a locus of storage of a long-term memory trace.
Empirically, is such repeated activation necessary for per-
sistence of memory for days or longer? Repeated spon-
taneous activation of neuron assemblies does occur106,107,
as does repeated replay or rehearsal of assemblies that
encode recent experiences108,109. One study supporting
the necessity of such replay found that the post-training
suppression of activity of neurons that were engineered to
overexpress CREB in the amygdala blocked the consoli-
dation of a memory of association between cocaine and a
location110. Similar blocking effects were obtained by the
indiscriminate activation of neurons that overexpressed
CREB. Although encouraging, these manipulations lack
the cellular precision that is necessary to demonstrate
conclusively that reactivation of a particular assembly
of neurons is essential for the persistence of long-term
memory. Future studies using optogenetic techniques
Figure 5 | A model predicts that a pair of drugs can act synergistically to enhance LTP.
A CREB-binding protein (Cbp) mutation impairs hippocampal long-term potentiation
(LTP) and impairs learning in mice, and Cbp+/− mice are considered to be a model for
aspects of Rubinstein–Taybi syndrome in humans104. We developed a model to examine
whether drugs could be used to overcome this impairment in LTP. This figure was
generated from a series of simulations of the effects of two drugs on the induction of LTP.
LTP was modelled as the percentage increase in a synaptic weight variable. In the absence
of drugs, simulated LTP induced by a high-frequency tetanic stimulus was strongly
impaired. Only a 50% increase in synaptic weight for Cbp+/− occurred, compared with an
increase in synaptic weight of 148% with non-mutated Cbp. The effect of each drug was
simply modelled as a change in the value of a kinetic parameter. In this series of
simulations, the doses of two drugs — drug 1, a cyclic AMP phosphodiesterase inhibitor,
and drug 2, an acetyltransferase activator — were concurrently varied. The effect of drug
1 was simulated by decreasing a rate constant for cAMP degradation, and the effect of
drug 2 was simulated by increasing a rate constant for histone acetylation. The ‘dose’
ofdrug 1 — the amplitude of the rate constant change — was increased, and
simultaneously the dose of drug 2 was decreased. Eighty pairs of drug doses were
simulated. Both drugs substantially enhanced LTP. For drug 2 alone (left end point of the
graph), LTP was 155%, and for drug 1 alone (right end point) LTP was 116%. For both drugs
together, with smaller doses of each drug, intermediate LTP amplitudes were observed
(combined-effect curve). This series of simulations further shows that additive synergism
persists over a substantial range of drug doses. Additive synergism is quantified as the
difference (black double arrow) between the LTP simulated when both drugs are applied
together (combined effect curve), and the LTP simulated by adding together the effect of
the drugs applied individually in separate simulations (summed effects curve).
Nature Reviews | Neuroscience
LTP (%)
100
120
140
160
Drug 1
Drug 2
Dose
High
High
Low
Low
Additive
synergism
Combined effect
Summed effects
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could provide that precision. Similarly, innovative strat-
egies will be needed to address whether effective spaced
learning requires the binding of contextual and episodic
memories at the neuronal network level, such as posited
by encoding variability theory, or increased binding
due to greater retrieval effort, as posited by study-phase
retrievaltheory.
The successful prediction of the interval structure of
behavioural training protocols that may overcome some
human learning deficits (when applied alone or in com-
bination with pharmacotherapy) will require improved
knowledge of the signalling pathways that underlie LTP
and long-term memory formation and of the ways in
which the deficits affect those pathways. Future mod-
els are still likely to be incomplete owing to gaps in
knowledge. For example, data will be incomplete and
associated with unavoidable uncertainties in the values
of biochemical parameters such as enzyme activities
or protein concentrations. In model development, data
from several preparation types (for example, cell cul-
tures and slices) and species (for example, primates and
rodents) commonly need to be used to estimate different
parameters86,88. However, although these limitations are
important, the potential benefits of combining modelling
with experiments in the ways discussed in this Review
are extensive, such that this strategy may have promise
for improving the clinical and educational outcomes for
patients with learning and memory deficits. In addition,
it is possible that education and learning in individuals
without such deficits could benefit from such a strategy.
Indeed, enhancing normal learning by judicious pharma-
cotherapy has recently received attention111, and com-
bining drugs with optimized spaced learning protocols
might yield even better outcomes.
1. Ebbinghaus,H. Memory; a Contribution to
Experimental Psychology Ch. 2 (Teachers College,
Columbia University, 1913).
2. Cepeda,N.J., Pashler,H., Vul,E., Wixted,J.T.
&Rohrer,D. Distributed practice in verbal recall tasks:
areview and quantitative synthesis. Psychol. Bull.
132, 354–380 (2006).
This extensive review and meta-analysis delineates
the comprehensive body of knowledge describing
human spaced and massed verbal learning, as well
as theories posited to explain the superiority of
spaced training over massed training in terms
ofinducing long-term memory formation.
3. Godbole,N.R., Delaney,P.F. & Verkoeijen,P.P.
Thespacing effect in immediate and delayed free
recall. Memory 22, 462–469 (2014).
4. Raman,M. etal. Teaching in small portions dispersed
over time enhances long-term knowledge retention.
Med. Teach. 32, 250–255 (2010).
5. Donovan,J.J. & Radosevich,D.J. A meta-analytic
review of the distribution of practice effect. J.Appl.
Psychol. 84, 795–805 (1999).
6. Shea,C.H., Lai,Q., Black,C. & Park,J.H. Spacing
practice sessions across days benefits the learning of
motor skills. Hum. Mov. Sci. 19, 737–760 (2000).
7. Gluckman,M., Vlach,H.A. & Sandhofer,C.M.
Spacing simultaneously promotes multiple forms of
learning in children’s science curriculum. Appl. Cognit.
Psychol. 28, 266–273 (2014).
8. McDaniel,M.A. & Callender,A.A. in Learning and
Memory: A Comprehensive Reference Vol.2 (ed.
Roediger,H.L.) 819–844 (Academic Press, 2008).
9. Sobel,H.S., Cepeda,N.J. & Kapler,I.V. Spacing
effects in real-world classroom vocabulary learning.
Appl. Cognit. Psychol. 25, 763–767 (2011).
10. Vlach,H.A. The spacing effect in children’s
generalization of knowledge: allowing children time to
forget promotes their ability to learn. Child Dev. Persp.
8, 163–168 (2014).
This study found that, in children’s education,
spacing of learning sessions promoted not only
better fact retention but also generalization of
currently learned items to bind with and extend
previously established conceptual categories.
11. Philips,G.T., Kopec,A.M. & Carew,T.J. Pattern and
predictability in memory formation: from molecular
mechanisms to clinical relevance. Neurobiol. Learn.
Mem. 105, 117–124 (2013).
12. Philips,G.T., Ye,X., Kopec,A.M. & Carew,T.J. MAPK
establishes a molecular context that defines effective
training patterns for long-term memory formation.
J.Neurosci. 33, 7565–7573 (2013).
This work characterized the dynamics of MAPK
andits activation of C/EBP, a transcription factor
that is important for long-term memory formation,
identifying the response of MAPK within a specific
time window as essential for generating an optimal
inter-trial interval.
13. Wainwright,M.L., Zhang,H., Byrne,J.H. &
Cleary,L.J. Localized neuronal outgrowth induced
bylong-term sensitization training in Aplysia.
J.Neurosci. 22, 4132–4141 (2002).
14. Mauelshagen,J., Sherff,C.M. & Carew,T.J.
Differential induction of long-term synaptic facilitation
by spaced and massed applications of serotonin
atsensory neuron synapses of Aplysia californica.
Learn. Mem. 5, 246–256 (1998).
15. Beck,C.D., Schroeder,B. & Davis,R.L. Learning
performance of normal and mutant Drosophila after
repeated conditioning trials with discrete stimuli.
J.Neurosci. 20, 2944–2953 (2000).
16. Pagani,M.R., Oishi,K., Gelb,B.D. & Zhong,Y.
Thephosphatase SHP2 regulates the spacing effect
for long-term memory induction. Cell 139, 186–198
(2009).
17. Menzel,R., Manz,G., Menzel,R. & Greggers,U.
Massed and spaced learning in honeybees: the role
ofCS, US, the intertrial interval, and the test interval.
Learn. Mem. 8, 198–208 (2001).
18. Anderson,M.J., Jablonski,S.A. & Klimas,D.B.
Spaced initial stimulus familiarization enhances
novelty preference in Long-Evans rats. Behav. Processes
78, 481–486 (2008).
19. Bello-Medina,P.C., Sanchez-Carrasco,L., Gonzalez-
Ornelas,N.R., Jeffery,K.J. & Ramirez-Amaya,V.
Differential effects of spaced versus massed training
inlong-term object-identity and object-location
recognition memory. Behav. Brain Res. 250, 102–113
(2013).
20. Medin,D.L. The comparative study of memory.
J.Hum. Evol. 3, 455–463 (1974).
21. Robbins,O. & Bush,C.T. Memory in great apes.
J.Exp. Psychol. 97, 344–348 (1973).
22. Urcelay,G.P., Wheeler,D.S. & Miller,R.R. Spacing
extinction trials alleviates renewal and spontaneous
recovery. Learn. Behav. 37, 60–73 (2009).
23. Benjamin,A.S. & Tullis,J. What makes distributed
practice effective? Cogn. Psychol. 61, 228–247
(2010).
24. Madigan,S.A. Intraserial repetition and coding
processes in free recall. J.Verb. Learn. Verb. Behav.
8,828–835 (1969).
25. Melton,A.W. The situation with respect to the
spacingof repetitions and memory. J.Verb. Learn.
Verb. Behav. 9, 596–606 (1970).
26. Braun,K. & Rubin,D.C. The spacing effect depends
on an encoding deficit, retrieval, and time in working
memory: evidence from once-presented words.
Memory 6, 37–65 (1998).
27. Greene,R.L. Spacing effects in memory: evidence for
a two-process account. J.Exp. Psychol. Learn. Mem.
Cogn. 15, 371–377 (1989).
28. Hintzman,D.L., Summers,J.J. & Block,R.A. Spacing
judgments as an index of study-phase retrieval. J.Exp.
Psychol. Hum. Learn. 1, 31–40 (1975).
29. Tzeng,O.J. & Cotton,B. A study-phase retrieval
model of temporal coding. J.Exp. Psychol. Hum.
Learn. 6, 705–716 (1980).
30. Siegel,L.L. & Kahana,M.J. A retrieved context
account of spacing and repetition effects in free recall.
J.Exp. Psychol. Learn. Mem. Cogn. 40, 755–764
(2014).
31. Hintzman,D.L. in Theories in Cognitive Psychology:
The Loyola Symposium (ed. Solso,R.L.) 77–90
(Erlbaum, 1974).
32. Dempster,F.N. Spacing effects and their implications
for theory and practice. Educ. Psychol. Rev. 1,
309–330 (1989).
33. Toppino,T.C. The spacing effect in young children’s
free recall: support for automatic-process
explanations. Mem. Cognit. 19, 159–167 (1991).
34. Hintzman,D.L. in The Psychology of Learning
andMotivation Vol. 10 (ed. Bower,G.H.) 47–91
(Academic Press, 1976).
35. Rundus,D. Analysis of rehearsal processes in free
recall. J.Exp. Psychol. 89, 63–77 (1971).
36. Lechner,H.A., Squire,L.R. & Byrne,J.H. 100years
of consolidation — remembering Müller and Pilzecker.
Learn. Mem. 6, 77–87 (1999).
37. Landauer,T.K. Reinforcement as consolidation.
Psychol. Rev. 76, 82–96 (1969).
This early paper posited and described elements
ofthe dynamics of spaced learning, such as the
saturation of memory formation by closely spaced
trials and the failure of reinforcement for long trial
spacing, and how these elements generate an
optimal inter-trial interval.
38. Peterson,L.R. Short-term verbal memory and
learning. Psychol. Rev. 73, 193–207 (1966).
39. Wickelgren,W.A. Trace resistance and the decay of
long-term memory. J.Math. Psychol. 9, 418–455
(1972).
40. Cuddy,L.J. & Jacoby,L.J. When forgetting helps
memory: an analysis of repetition effects. J.Verb.
Learn. Verb. Behav. 21, 451–467 (1982).
41. Toppino,T.C. & Bloom,L.C. The spacing effect, free
recall, and two-process theory: a closer look. J.Exp.
Psychol. Learn. Mem. Cogn. 28, 437–444 (2002).
42. Nabavi,S. etal. Engineering a memory with LTD
andLTP. Nature 511 , 348–352 (2014).
43. Bosch,M. & Hayashi,Y. Structural plasticity of
dendritic spines. Curr. Opin. Neurobiol. 22, 383–388
(2012).
44. Bell,M.E. etal. Dynamics of nascent and active zone
ultrastructure as synapses enlarge during LTP in
mature hippocampus. J.Comp. Neurol. 522,
3861–3864 (2014).
This work, one of a group of related studies by the
Harris laboratory, characterized in detail pre- and
postsynaptic structural changes that are necessary
for the consolidation of LTP.
45. Bourne,J.N. & Harris,K.M. Coordination of size and
number of excitatory and inhibitory synapses results
in a balanced structural plasticity along mature
hippocampal CA1 dendrites during LTP. Hippocampus
21, 354–373 (2011).
46. Cao,G. & Harris,K.M. Augmenting saturated LTP
bybroadly spaced episodes of theta-burst stimulation
in hippocampal area CA1 of adult rats and mice.
J.Neurophysiol. 112 , 1916–1924 (2014).
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47. Kramár,E.A. etal. Synaptic evidence for the efficacy
of spaced learning. Proc. Natl Acad. Sci. USA 109,
5121–5126 (2012).
This study, together with reference 48, substantiates
the key hypothesis that spaced stimuli first prime
and then strengthen upon repetition successive
groups of dendritic spines, creating a morphological
correlate of the memory trace that spaced, but not
massed, protocols effectively consolidate.
48. Lynch,G., Kramár,E.A., Babayan,A.H.,
Rumbaugh,G. & Gall,C.M. Differences between
synaptic plasticity thresholds result in new timing
rulesfor maximizing long-term potentiation.
Neuropharmacology 64, 27–38 (2013).
49. Rex,C.S. etal. Different Rho GTPase-dependent
signaling pathways initiate sequential steps in the
consolidation of long-term potentiation. J.Cell Biol.
186, 85–97 (2009).
50. Nguyen,P.V., Abel,T. & Kandel,E.R. Requirement of
a critical period of transcription for induction of a late
phase of LTP. Science 265, 1104–1107 (1994).
51. Scharf,M.T. etal. Protein synthesis is required for the
enhancement of long-term potentiation and long-term
memory by spaced training. J.Neurophysiol. 87,
2770–2777 (2002).
52. Kandel,E.R. The molecular biology of memory
storage: a dialogue between genes and synapses.
Science 294, 1249–1254 (2001).
53. Silva,A.J., Kogan,J.H., Frankland,P.W. & Kida,S.
CREB and memory. Annu. Rev. Neurosci. 21 ,
127–148 (1998).
54. Liu,R.Y., Fioravante,D., Shah,S. & Byrne,J.H. cAMP
response element-binding protein 1 feedback loop is
necessary for consolidation of long-term synaptic
facilitation in Aplysia. J.Neurosci. 28, 1970–1976
(2008).
55. Liu,R.Y., Cleary,L.J. & Byrne,J.H. The requirement
for enhanced CREB1 expression in consolidation of
long-term synaptic facilitation and long-term
excitability in sensory neurons of Aplysia. J.Neurosci.
31, 6871–6879 (2011).
56. Liu,R.Y., Shah,S., Cleary,L.J. & Byrne,J.H.
Serotonin- and training-induced dynamic regulation of
CREB2 in Aplysia. Learn. Mem. 18, 245–249 (2011).
57. Frost,W.N., Castellucci,V., Hawkins,R.D. &
Kandel,E.R. Monosynaptic connections made by the
sensory neurons of the gill- and siphon-withdrawal
reflex in Aplysia participate in the storage of long-
term memory for sensitization. Proc. Natl Acad. Sci.
USA 82, 8266–8269 (1985).
58. Pinsker,H.M., Hening,W.A., Carew,T.J. &
Kandel,E.R. Long-term sensitization of a defensive
withdrawal reflex in Aplysia. Science 182,
1039–1042 (1973).
59. Beckinschtein,P. etal. Persistence of long-term
memory storage requires a late protein synthesis- and
BDNF-dependent phase in the hippocampus. Neuron
53, 261–277 (2007).
60. Bambah-Mukku,D., Travaglia,A., Chen,D.Y.,
Pollonini,G. & Alberini,C.M. A positive
autoregulatory BDNF feedback loop via C/EBPβ
mediates hippocampal memory consolidation.
J.Neurosci. 34, 12547–12559 (2014).
This study characterizes the dynamics of a
molecular positive feedback loop demonstrated to
be important for the consolidation of long-term
(24hours or more) memory, as well as the late
repression of Bdnf that terminates the feedback.
61. Genoux,D. etal. Protein phosphatase 1 is a molecular
constraint on learning and memory. Nature 418,
970–974 (2002).
62. Farah,C.A., Weatherill,D., Dunn,T.W. & Sossin,W.S.
PKC differentially translocates during spaced
andmassed training in Aplysia. J.Neurosci. 29,
10281–10286 (2009).
63. Tully,T., Preat,T., Boynton,S. & Del Vecchio,M.
Genetic dissection of consolidated memory in
Drosophila melanogaster. Cell 79, 35–47 (1994).
64. Yin,J.C., Del Vecchio,M., Zhou,H. & Tully,T. CREB as
a memory modulator: induced expression of a
dCREB2 activator isoform enhances long-term
memory in Drosophila. Cell 81, 107–115 (1995).
65. Smolen,P., Baxter,D.A. & Byrne,J.H. Frequency
selectivity, multistability, and oscillations emerge from
models of genetic regulatory systems. Am. J.Physiol.
274, C531–C542 (1998).
66. Philips,G.T., Tzvetkova,E.I. & Carew,T.J. Transient
mitogen-activated protein kinase activation is confined
to a narrow temporal window required for the
induction of two-trial long term memory in Aplysia.
J.Neurosci. 27, 13701–13705 (2007).
67. Seese,R.R., Wang,K., Yao,Y.Q., Lynch,G. &
Gall,C.M. Spaced training rescues memory and
ERK1/2 signaling in fragile X syndrome model mice.
Proc. Natl Acad. Sci. USA 111, 16907–16912
(2014).
68. Rosenblum,K. etal. The role of extracellular regulated
kinases I/II in late-phase long-term potentiation.
J.Neurosci. 22, 5432–5441 (2002).
69. Ajay,S.M. & Bhalla,U.S. A propagating ERKII switch
forms zones of elevated dendritic activation correlated
with plasticity. HFSPJ. 1, 49–66 (2007).
70. Chen,C. etal. Visualizing long-term memory formation
in two neurons of the Drosophila brain. Science 335,
678–685 (2012).
71. Cepeda,N.J. etal.Optimizing distributed practice:
theoretical analysis and practical implications.
Exp.Psychol. 56, 236–246 (2009).
72. Wei,Y. & Koulakov,A.A. Long-term memory
stabilizedby noise-induced rehearsal. J.Neurosci. 34,
15804–15815 (2014).
73. Cui,Z. etal. Inducible and reversible NR1 knockout
reveals a crucial role of the NMDA receptor in
preserving remote memories in the brain. Neuron 41,
781–793 (2004).
This study demonstrates the key point that ongoing
neural activity and NMDA receptor activation are
essential for long-lasting preservation of stored
memory.
74. Tetzlaff,C., Kolodziejski,C., Timme,M., Tsodyks,M.
&Worgotter,F. Synaptic scaling enables dynamically
distinct short- and long-term memory formation.
PLoSComput. Biol. 9, e1003307 (2013).
75. Miller,P., Zhabotinsky,A.M., Lisman,J.E. &
Wang,X.J. The stability of a stochastic CaMKII switch:
dependence on the number of enzyme molecules
andprotein turnover. PLoS Biol. 3, e107 (2005).
76. Pettigrew,D.B., Smolen,P., Baxter,D.A. &
Byrne,J.H. Dynamic properties of regulatory motifs
associated with induction of three temporal domains
of memory in Aplysia. J.Comput. Neurosci. 18,
163–181 (2005).
77. Shema,R., Sacktor,T.C. & Dudai,Y. Rapid erasure of
long-term memory associations in the cortex by an
inhibitor of PKMζ. Science 317, 951–953 (2007).
78. Volk,L.J., Bachman,J.L., Johnson,R., Yu,Y. &
Huganir,R.L. PKM-ζ is not required for hippocampal
synaptic plasticity, learning and memory. Nature 493,
420–423 (2013).
79. Fioriti,L. etal. The persistence of hippocampal-based
memory requires protein synthesis mediated by the
prion-like protein CPEB3. Neuron 86, 1433–1448
(2015).
80. Ajay,S.M. & Bhalla,U.S. A role for ERKII in synaptic
pattern selectivity on the time scale of minutes.
Eur.J.Neurosci. 20, 2671–2680 (2004).
81. Frey,U. & Morris,R.G. Synaptic tagging: implications
for late maintenance of hippocampal long-term
potentiation. Trends Neurosci. 21 , 181–188 (1998).
82. Babayan,A.H. etal. Integrin dynamics produce a
delayed stage of long-term potentiation and memory
consolidation. J.Neurosci. 32, 12854–12861
(2012).
83. Callan,D.E. & Schweighofer,N. Neural correlates of
the spacing effect in explicit verbal semantic encoding
support the deficient-processing theory. Hum. Brain
Mapp. 31, 645–659 (2010).
84. Karpicke,J.D. & Roediger,H.L. Is expanding retrieval
a superior method for learning text materials?
Mem.Cognit. 38, 116–124 (2010).
85. Zhang,Y. etal. Computational design of enhanced
learning protocols. Nat. Neurosci. 15, 294–297
(2012).
This study found that computational models
describing long-term synaptic strengthening can
successfully predict optimal spaced training
protocols, with irregular inter-trial intervals, that
enhance learning.
86. Bhalla,U.S. & Iyengar,R. Emergent properties of
networks of biological signaling pathways. Science
283, 381–387 (1999).
87. Kim,M., Huang,T., Abel,T. & Blackwell,K.T.
Temporal sensitivity of protein kinase A activation in
late-phase long term potentiation. PLoS Comput. Biol.
6, e1000691 (2010).
88. Smolen,P., Baxter,D.A. & Byrne,J.H. A model of the
roles of essential kinases in the induction and
expression of late long-term potentiation. Biophys.J.
90, 2760–2775 (2006).
89. Aslam,N. & Shouval,H.Z. Regulation of cytoplasmic
polyadenylation can generate a bistable switch.
BMCSyst. Biol. 6, 12–20 (2012).
90. Liu,R.Y. etal. Deficit in long-term synaptic
plasticityis rescued by a computationally predicted
stimulus protocol. J.Neurosci. 33, 6944–6949
(2013).
91. Montarolo,P.G. etal. A critical period for
macromolecular synthesis in long-term heterosynaptic
facilitation in Aplysia. Science 234, 1249–1254
(1986).
92. Müller,U. & Carew,T.J. Serotonin induces temporally
and mechanistically distinct phases of persistent PKA
activity in Aplysia sensory neurons. Neuron 21,
1423–1434 (1998).
93. Korzus,E., Rosenfeld,M.G. & Mayford,M. CBP
histone acetyltransferase activity is a critical
component of memory consolidation. Neuron 42,
961–972 (2004).
94. Park,E. etal. Epigenetic mechanisms of Rubinstein–
Taybi syndrome. Neuromolecular Med. 16, 16–24
(2014).
95. Alarcon,J.M. etal. Chromatin acetylation, memory,
and LTP are impaired in CBP+/− mice: a model for
thecognitive deficit in Rubinstein–Taybi syndrome
andits amelioration. Neuron 42, 947–959 (2004).
96. Alberini,C.M.Transcription factors in long-term
memory and synaptic plasticity. Physiol. Rev. 89,
121–145 (2009).
97. Zhou,L. etal. Rescue of impaired long-term facilitation
at sensorimotor synapses of Aplysia following siRNA
knockdown of CREB1. J.Neurosci. 35, 1617–1626
(2015).
98. Abel,T. & Nguyen,P.V. Regulation of hippocampus-
dependent memory by cyclic AMP-dependent
proteinkinase. Prog. Brain Res. 169, 97–115
(2008).
99. Bartsch,D., Casadio,A., Karl,K.A., Serodio,P. &
Kandel,E.R. CREB1 encodes a nuclear activator, a
repressor, and a cytoplasmic modulator that form a
regulatory unit critical for long-term facilitation. Cell
95, 211–223 (1998).
100. Kida,S. A functional role for CREB as a positive
regulator of memory formation and LTP. Exp.
Neurobiol. 21, 136–140 (2012).
101. Bartsch,D. etal. Aplysia CREB2 represses long-term
facilitation: relief of repression converts transient
facilitation into long-term functional and structural
change. Cell 83, 979–992 (1995).
102. Dong,C., Upadhya,S.C., Ding,L., Smith,T.K. &
Hegde,A.N. Proteosome inhibition enhances the
induction and impairs the maintenance of late-phase
long-term potentiation. Learn. Mem. 15, 335–347
(2008).
103. Pasini,S., Corona,C., Liu,J., Greene,L.A. &
Shelanski,M.L. Specific downregulation of
hippocampal ATF4 reveals a necessary role in synaptic
plasticity and memory. Cell Rep. 11, 183–191
(2015).
104. Smolen,P., Baxter,D.A. & Byrne,J.H. Simulations
suggest pharmacological methods for rescuing long-
term potentiation. J.Theor. Biol. 360, 243–250
(2014).
105. Cuadrado-Tejedor,M. etal. Concomitant histone
deacetylase and phosphodiesterase 5 inhibition
synergistically prevents the disruption in synaptic
plasticity and it reverses cognitive impairment in a
mouse model of Alzheimer’s disease. Clin. Epigenetics
7, 108 (2015).
106. Ikegaya,Y. etal. Synfire chains and cortical songs:
temporal modules of cortical activity. Science 304,
559–564 (2004).
107. Miller,J.E., Ayzenshtat,I., Carrillo-Reid,L. &
Yuste,R. Visual stimuli recruit intrinsically generated
cortical ensembles. Proc. Natl Acad. Sci. USA 111,
E4053–E4061 (2014).
108. Karlsson,M.P. & Frank,L.M. Awake replay of remote
experiences in the hippocampus. Nat. Neurosci. 12,
913–919 (2009).
109. Wu,X. & Foster,D.J. Hippocampal replay captures
the unique topological structure of a novel
environment. J.Neurosci. 34, 6459–6469 (2014).
110 . Hsiang,H.L. etal. Manipulating a “cocaine engram”
in mice. J.Neurosci. 34, 14115–14127 (2014).
111. Lynch,G., Cox,C.D. & Gall,C.M. Pharmacological
enhancement of memory or cognition in normal
subjects. Front. Syst. Neurosci. 8, 90 (2014).
Acknowledgements
This work was supported by US National Institutes of Health
grants NS073974 and NS019895.
Competing interests statement
The authors declare no competing interests.
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