Past, present, and the future: discussions surrounding a new model of sleep-dependent learning and memory processing
Commentary/ Walker: A refined model of sleep and the time course of memory formation
which settles the debate about the exclusiveness of memory con-
tical inversion of the visual field. In the second study, the persons
who experienced incorporations of the inverted visual field in
In describing the findings regarding procedural memory and
their dreams performed better on tasks (reading and writing)
sleep in humans, Walker states that the evidence is “incredibly ro-
measuring adaptation. This relation makes sense since research
bust.” Reviewing the literature, however, one must say that the
(Schredl 2000) has shown that dream content is related to specific
number of studies is quite small, and direct replication studies
brain activation patterns and other physiological parameters mea-
carried out in different laboratories are scarce. Often different
sured during sleep. Moreover, this is in line with the continuity hy-
tasks (e.g., a visual discrimination task [Stickgold et al. 2000b],
pothesis of dreaming (cf. Schredl 2003), which states that waking-
motor skills like finger tapping [Walker et al. 2003b], acquisition
life experiences, for example, the evening learning sessions, are
of probabilistic rules [Peigneux et al. 2003], and priming [Plihal
probably incorporated into subsequent dreams. An experimental
& Born 1999a]) as well as different manipulation techniques (e.g.,
approach to this topic could be the technique of lucid dreaming,
early versus late sleep [Plihal & Born 1997], REM sleep depriva-
since it is possible to carry out assigned tasks during the dream
tion [Karni et al. 1994], and correlations between sleep parame-
(e.g., LaBerge & Rheingold 1990). For a simple motor activity
ters and improvement [Stickgold et al. 2000b]) have been used.
(hand clenching), Erlacher et al. (2003) were able to demonstrate
In our laboratory, we are currently conducting a correlation study
that the related area of the motor cortex was active during the lu-
applying the mirror trace task used by Plihal and Born (1997). The
cid dream (EEG measure). This approach makes sense in the light
preliminary findings (N ϭ 12) are promising: a significant corre-
of the extensive literature on the effect of mental training on per-
lation (r ϭ .430, p Ͻ .05, one-tailed) between percentage of REM
formance (e.g., Driskell et al. 1994). Single cases of successful
sleep and improvement in speed from the evening session to the
training of sport skills in lucid dreams have been reported
morning session was found. This is not completely consistent with
(LaBerge & Rheingold 1990; Tholey 1981). On the other hand,
the finding of Stickgold et al. (2000b) for the visual discrimination
one should consider that dreaming as reportable subjective expe-
task; they reported a much higher correlation (r ϭ .74; N ϭ 14).
riences during sleep is only a small part of the total activity of the
Next, my coworkers (Orla Hornung, Francesca Regen, Heidi
sleeping brain (comparable to consciousness during the waking
Danker-Hopfe, and Isabella Heuser) and I utilized a modified
state), so it remains unclear how close the relationship between
version of the mirror-tracing task in a study of memory in elderly,
dream content and learning processes during sleep might be.
healthy persons and were also able to demonstrate a correlation
To summarize, the model proposed by Walker is a promising
between the percentage of REM sleep and performance (this is a
starting point for future research investigating, in addition to the
preliminary result; the study is still in progress). On the other
time course, influential factors such as task type, experimental dif-
hand, the insignificant finding regarding non-REM Stage 2 sleep
ficulty, and performance level in the relationship between sleep
and performance is not in line with the findings of Walker et al.
(2003b). In addition to these conflicting results, other inconsis-tencies between the different studies in the field can be pointedout. Karni et al. (1994), for example, found an effect of REM sleepdeprivation on the improvement in the visual discrimination task
Memory consolidation during sleep:
but not for slow wave sleep deprivation, whereas Stickgold et al. A form of brain restitution
(2000b) reported correlations for slow wave sleep and REM sleep. To summarize, although the amount of evidence supporting a
close relationship of procedural memory and sleep is growing,many inconsistencies have to be clarified by futures studies. University of Houston, Houston, TX 77204. [email protected]
If sleep plays a crucial role in memory consolidation, one of the
next steps will be to study patients with primary sleep disorders. Abstract: Does sleep restore brain function or does it consolidate mem- ory? I argue that memory consolidation during sleep is an offshoot of resti-
Although Fulda and Schulz (2001) published a extensive meta-
tution. Continual learning causes local synapse-specific neural fatigue,
analysis on the cognitive impairment in patients with sleep disor-
which then masks expression of that learning, especially on time-limited
ders, detailed studies using paradigms including evening training
tests of procedural skills. Sleep serves to restore the fatigued synapses, re-
sessions and morning retest sessions have not yet been carried out
vealing the consolidation-based enhancement observed as a “latent”
in these patient groups. Keeping in mind the reduced daytime vig-
ilance in these patients, it will be interesting to search for correla-tions between sleep architecture (total sleep time, percentage of
Evidence for the involvement of sleep in memory consolidation
REM sleep) and performance improvements in procedural as well
comes in many forms, such as the effects of learning on postlearn-
ing sleep and the re-expression of behavior-specific patterns dur-
Assuming that REM sleep plays a crucial role in consolidation
ing postlearning sleep. However, a cause-and-effect relationship
of procedural memory (e.g., Plihal & Born 1997), studying the ef-
or even a robust correlation between the effects of learning on
fects of REM sleep augmentation on learning will be of interest.
sleep or the replay of patterns during sleep, on the one hand, and
Schredl et al. (2001) have published the first human study in
the magnitude of consolidation, on the other, has yet to be effec-
which donepezil, an acetylcholinesterase inhibitor, was adminis-
tively demonstrated. Improved learning following a period with
tered to enhance REM sleep. A significant correlation (r ϭ .669,
sleep, compared to one without, remains the most consistent evi-
p Ͻ .05, one-tailed) between percentage of REM sleep and the
dence to date; I propose an explanation for this.
improvement of a task (relearning a word list) that comprises de-
I begin by noting that there exists emerging evidence for sleep
clarative and implicit features was found for the donepezil nights.
as a localized brain process. While Rechtschaffen (1998) suggests
Although this pilot study leaves many questions unanswered, this
that it is “difficult to arrive at a widely acceptable theory of sleep
research area is of interest because it was found that patients with
function because that function is not reflected at the organ or sys-
Alzheimer’s disease have reduced REM sleep (Bliwise 1993), and
tem level,” he and others (e.g., Moruzzi 1966) propose that sleep
cholinergic agents, which often enhance REM sleep – one of the
is a localized process that provides basic cellular resources. In-
measurable effects of these agents on the cholinergic system –
deed, no brain lesion has ever successfully eliminated sleep totally
(see Schredl et al. 2000), are widely used in the treatment of Alz-
for long periods (Rechtschaffen 1998). In certain marine animals,
sleep is sometimes localized to one brain hemisphere at a time
The last topic to be addressed here is the possible relationship
(Oleksenko et al. 1992). Continual tactile stimulation of the right
between dream content and learning. Some preliminary evidence
hand prior to sleep results in increased spectral power in the delta
has been reported by De Koninck et al. (1988) for intense lan-
band during early non-REM sleep in the contralateral so-
guage learning, and De Koninck et al. (1996) for adaptation to ver-
matosensory cortex (Kattler et al. 1994).
BEHAVIORAL AND BRAIN SCIENCES (2005) 28:1
Commentary/ Walker: A refined model of sleep and the time
evidence exists but has eluded researchers, or perhaps proceduraland declarative memories differ in the same respects that makethe former more susceptible to sleep. Procedural learning is usu-ally dependent on the context and modality in which the materialwas presented initially (Squire 1986), are “realized as cumulativechanges stored within the particular neural systems engaged dur-ing learning” (Squire 1986), and typically require training for sev-
Figure 1 (Sheth). A hypothetical evolution of local brain
eral minutes to several hours on the procedure. In contrast, de-
processes as a function of behavioral state, and the effects on
clarative learning is flexible, accessible to all modalities and can be
memory performance. Various processes (A, B, and C) combine
“one-shot.” The weaker synaptic specificity and quicker learning
to affect memory test performance (ordinate). With continual
of declarative as opposed to procedural learning implies less lo-
practice while the observer is awake, learning occurs (A), which
calized declarative storage, which means, by our hypothesis, less
improves performance. However, neuronal fatigue (B), which oc-
synapse specific fatigue, and smaller benefits of sleep.
curs hand in hand with the learning, impairs performance. Dur-
In sum, two issues are critical in the present account: (1) Synap-
ing sleep, neural circuits slowly recover (C), which gives rise to “la-
tic specificity: With greater synaptic specificity, there is greater
tent learning.” A, B or C are transparent to the experimenter.
impact of sleep on local synaptic recovery; and (2) the need for
Observed performance is some (nonlinear) combination of them.
speed: On perceptual as well as motor learning tasks, perceptionand/or motor action must be conducted within a finite period oftime for optimal performance (e.g., Stickgold et al. 2000b; Walkeret al. 2002). With time constraints, inefficiency of synaptic trans-
With these ideas as a basis, I propose that two separate local
mission takes on even greater significance, and, because speed-ac-
brain processes are involved in the learning of a procedure or skill
curacy tradeoffs are commonplace, the effects of sleep depriva-
(see Fig. 1). Over repeated trials, the awake observer practices
tion are observable on measures of speed as well as accuracy.
specifics of the procedure (A). Learning is a multifaceted process,
By varying each factor, this hypothesis can be experimentally
one facet being the progressive restriction in the brain circuits that
verified. One possibility is to vary the degree of synapse-specific
influence performance (Edelman & Tononi 2000; James 1890). I
adaptation in two sets of synapses that exhibit learning during
propose that while this process does not depend on sleep, a sec-
training. Visual discrimination skills that transfer to different con-
ond independent process exists that does. With increasing neural
ditions (Ahissar & Hochstein 1996; 1997) are suitable for this.
specialization during learning, the circuits or synapses repeatedly
Synapse-specific sleep dependent recovery will accordingly differ
engaged in the procedure adapt or fatigue (B). Synapse-specific
between the two sets. Learning following sleep loss will be im-
fatigue during procedural learning is unavoidable. Repeated stim-
paired following sleep loss in both brain circuits, but less so in the
ulus processing produces decreased responses in brain circuits as-
brain circuit that learnt the procedure indirectly via transfer.
sociated with that processing – a “repetition suppression” effect(Brown & Xiang 1998; Desimone 1996; Wiggs & Martin 1998).
The inefficiency in local signal transmission that arises from the
I thank Professor J. Siegel and Dylan Nieman for carefully proofreading
synaptic fatigue or adaptation masks expression of the learning.
With prolonged training on a task that involves both speed andskill, the net product of these two contravening processes, mea-sured behaviorally, is asymptotic learning (Karni & Sagi 1991). Over still longer training periods, a decline in performance is ob-
The incredible, shrinking sleep-learning
served (Mednick et al. 2002). Several studies (e.g., McCollough
connection
1965) have shown that the effects of adaptation can be long last-ing, particularly if a select few synapses, specific to certain stimuli
or conditions and not others, are adapted. Neurobiology Research 151A3, V.A. Greater Los Angeles Health System,
The recovery of functions related to sensory transmission, such
Sepulveda, CA 91343; and Department of Psychiatry, UCLA David Geffen
as the restoration of neurotransmitters or the re-formation of re-
School of Medicine, 16111 Plummer Street, North Hills, CA 91343.
ceptors, likely involves protein synthesis, which has its own char-
[email protected] http://www.npi.ucla.edu/sleepresearch
acteristic time course, one that is longer than the time course ofresource depletion in the synapse. Thus, sleep-dependent
Abstract: Initial claims that REM sleep is important in the consolidation
synapse-specific recovery (Fig. 1) is independent of the training in
of all memories have been revised and reduced to the claim that sleep hasa role only in the consolidation of procedural learning. Now, Walker hy-
the wake state. It is, however, dependent on sleep; sleep cannot
pothesizes that sleep has no role in the “stabilization phase of consolida-
be replaced with awake resting, which fails to control internally
tion” but only in the “enhanced learning” phase of procedural learning.
generated activity in key brain areas, or by reversible inactivations
Evidence for this vague, truncated hypothesis remains as inconsistent as
of brain areas engaged in the consolidation, which only delays the
recovery and may even shrink the critical time window duringwhich the learnt information can be actively enhanced (target ar-
The idea that REM sleep is important for memory consolidation
is attractive, since it would explain the vivid imagery of dreams as
Sleep is clearly not monolithic. SWS, and non-REM sleep in
a repetition of the events of the prior day to enable the laying down
general, are believed to have a restorative role in brain function
of permanent memory traces. Unfortunately, dream reports do
(Horne 1988). It is notable that there is correlational evidence for
not support this idea. Most dreams concern emotions and activi-
the role of non-REM sleep in memory consolidation in humans
ties that did not occur during prior days. Furthermore, most
(Stage 2 sleep for motor skill learning, Walker et al. 2002; early
dreams are not subsequently recalled unless they are immediately
SWS for visual discrimination skill consolidation, Gais et al. 2000;
rehearsed in waking following the dream (Rechtschaffen & Siegel
Stickgold et al. 2000b). This dovetails nicely with the idea of
synapse-specific recovery. Replay of behavior-specific patterns
Those working on the role of sleep in human learning have
during late REM sleep, if short-lived, may reactivate and reinforce
modified their hypotheses to include non-REM sleep as well as
the task-related synapses (target article, sect. 2.4.2) with minimal
REM sleep. Many studies of the relationship between human
sleep and learning have focused on sleep’s role in learning of word
In contrast to procedural memory, evidence that sleep improves
recognition and associations between words and events – tasks
declarative memory is inconsistent (sect. 2.2). Perhaps conclusive
mimicking most of what goes on in school; this is what learning
BEHAVIORAL AND BRAIN SCIENCES (2005) 28:1
MARINE ECOLOGY PROGRESS SERIES Vol. 235: 127–134, 2002 Published June 19 Mar Ecol Prog Ser Species differences, origins and functions of fatty alcohols and fatty acids in the wax esters and phospholipids of Calanus hyperboreus, C. glacialis and C. finmarchicus from Arctic waters Catherine L. Scott1, Slawomir Kwasniewski3, Stig Falk-Petersen4, John R. Sargent2,*
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