The influence of learning on sleep slow oscillations andassociated spindles and ripples in humans and rats

Matthias Molle,1,* Oxana Eschenko,2,*,� Steffen Gais,1 Susan J. Sara3 and Jan Born1

1Department of Neuroendocrinology, University of Lubeck, Ratzeburger Allee 160, Haus 23a, 23538 Lubeck, Germany2Neuromodulation, Neuroplasticity and Cognition, CNRS, UMR 7102, University Paris 6, Paris, France3College de France, Centre National de la Recherche Scientifique, UMR 7152, Paris, France

Keywords: humans, rats, ripples, sleep, slow oscillations, spindles

Abstract

The mechanisms underlying off-line consolidation of memory during sleep are elusive. Learning of hippocampus-dependent tasks

increases neocortical slow oscillation synchrony, and thalamocortical spindle and hippocampal ripple activity during subsequent non-

rapid eye movement sleep. Slow oscillations representing an oscillation between global neocortical states of increased (up-state) and

decreased (down-state) neuronal firing temporally group thalamic spindle and hippocampal ripple activity, which both occur

preferentially during slow oscillation up-states. Here we examined whether slow oscillations also group learning-induced increases in

spindle and ripple activity, thereby providing time-frames of facilitated hippocampus-to-neocortical information transfer underlying the

conversion of temporary into long-term memories. Learning (word-pairs in humans, odor–reward associations in rats) increased slow

oscillation up-states and, in humans, shaped the timing of down-states. Slow oscillations grouped spindle and rat ripple activity into

up-states under basal conditions. Prior learning produced in humans an increase in spindle activity focused on slow oscillation

up-states. In rats, learning induced a distinct increase in spindle and ripple activity that was not synchronized to up-states. Event-

correlation histograms indicated an increase in spindle activity with the occurrence of ripples. This increase was prolonged after

learning, suggesting a direct temporal tuning between ripples and spindles. The lack of a grouping effect of slow oscillations on

learning-induced spindles and ripples in rats, together with the less pronounced effects of learning on slow oscillations, presumably

reflects a weaker dependence of odor learning on thalamo-neocortical circuitry. Slow oscillations might provide an effective temporal

frame for hippocampus-to-neocortical information transfer only when thalamo-neocortical systems are already critically involved

during learning.

Introduction

Research in recent years has generated an upsurge in the literature

indicating a role for sleep in the consolidation of memories (Stickgold,

2005; Born et al., 2006). A leading assumption is that memory

consolidation during sleep occurs during repeated reactivation of

neuronal networks that were previously engaged in encoding the

information. In rats, temporal patterns of neuronal reactivation in

hippocampal, neocortical and striatal areas during slow-wave sleep

(SWS) after hippocampus-dependent tasks have been described

(Pennartz et al., 2002; Ribeiro et al., 2004; Buzsaki, 2006; Ji &

Wilson, 2007). Human studies using neuroimaging suggested a

causative role of hippocampal reactivation during SWS in consolida-

tion of hippocampus-dependent memories (Peigneux et al., 2004;

Rasch et al., 2007). In the hippocampus, reactivations are assumed to

occur during ripple events, typically forming sharp wave–ripple

complexes that originate from strong depolarization of CA3 collaterals

(Buzsaki, 1989, 1996). Reactivation of memories during SWS

co-occurring in the hippocampus and neocortex has been proposed

to promote the redistribution of the newly acquired memories to

preferential representation in neocortical networks, where they are

eventually stored for the long term (McClelland et al., 1995; Gais

et al., 2007; Ji & Wilson, 2007; Marshall & Born, 2007; Rasch &

Born, 2008).

Neocortical slow oscillations have been suggested in this context to

provide a temporal frame for hippocampus-to-neocortical information

transfer underlying the sleep-dependent consolidation of memories

(Buzsaki, 1998; Sirota et al., 2003; Marshall et al., 2006; Marshall &

Born, 2007). The slow oscillation occurs during SWS in humans at a

peak frequency of �0.75 Hz, and is generated within neocortical

networks (Steriade, 2006). Findings of increased synchrony of slow

oscillation activity and increased slow oscillation amplitude during

sleep after learning indicate that their generation at least partially

Correspondence: Dr M. Molle, as above.

E-mail: moelle@kfg.uni-luebeck.de

*M.M. and O.E. contributed equally to this work.

�Present address: Department of Physiology of Cognitive Processes, Max Planck Institute

for Biological Cybernetics, Spemannstr. 38, D-72076 Tubingen, Germany.

Received 5 September 2008, revised 23 December 2008, accepted 9 January 2009

European Journal of Neuroscience, pp. 1–11, 2009 doi:10.1111/j.1460-9568.2009.06654.x

ª The Authors (2009). Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd

EJN 6654

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depends on the prior use of these networks for encoding (Huber et al.,

2004; Molle et al., 2004). The slow oscillation synchronizes neuronal

activity into generalized up-states (depolarization) and down-states

(hyperpolarization) not only in the neocortex, but also via efferent

pathways to other brain regions. A grouping influence of the slow

oscillation on thalamocortical spindles, such that periods of cortical

hyperpolarization are followed by strong rebound spindle activity, has

been established in cats, rodents, and humans (Molle et al., 2002b;

Sirota et al., 2003). In parallel, neocortical slow oscillations impact,

via entorhinal cortex activity, on the hippocampus, which does not

appear to develop slowly oscillating up-states and down-states on its

own (Isomura et al., 2006; Molle et al., 2006; Clemens et al., 2007).

Sharp wave–ripple events and CA1 interneuron activity are suppressed

during slow oscillation down-states, and show a rebound during the

development of up-states, with cortical up-states and down-states

leading the temporal dynamics in hippocampal activity by 30–50 ms.

Thus, by repeatedly resetting networks during the down-phase, the

neocortical slow oscillation provides a temporal frame that synchro-

nizes the generation of thalamocortical spindles with sharp-wave

ripple events occurring in the hippocampal circuitry during memory

reactivation. Sharp-wave ripples, temporally linked to the occurrence of

spindles in this way, may constitute a mechanism of hippocampus-to-

neocortical information transfer during states of enhanced neocortical

excitability (i.e. up-states), eventually supporting the conversion of

hippocampal memories into longer-term neocortical stores (Siapas &

Wilson, 1998; Sirota et al., 2003).

Learning of tasks such as word-pair associates in humans and odor–

reward association in rats, prior to sleep, increases thalamocortical

spindle activity, with this increase being associated with improved

retention of the acquired memories (Gais et al., 2002; Schabus et al.,

2004; Eschenko et al., 2006; Schmidt et al., 2006). In rats, acquisition

of such tasks increased hippocampal ripple activity during subsequent

SWS as well (Eschenko et al., 2008). Here, in a comparative approach

used to identify commonalities and differences between humans and

rats, we examined the effects of prior learning of these tasks on slow

oscillations and their synchronizing influence on spindle and ripple

activity. We hypothesized that learning-dependent enhancements in

spindle and ripple activity (measured in rats only) would occur

selectively during the slow oscillation up-state.

Materials and methods

Subjects and procedures

Humans

Recordings were obtained from 12 subjects (six females, six males,

24.0 ± 1.0 years) participating in a series of experiments exploring

memory functions of sleep at the University of Lubeck. Subjects

regularly obtained 7–8 h of sleep per night, and had no disruptions of

the sleep–wake cycle during the 6 weeks before the experiments. All

subjects had spent an adaptation night in the sleep laboratory

(including the same recordings as during experimental nights) before

beginning the experiments proper. Subjects abstained from caffeine

and alcohol on the day before the experimental session. They were

instructed to get up before 07:00 h and not to take any naps during the

day. Also, as the effects of experimental learning on subsequent sleep

were examined, subjects were requested not to engage in any intensive

learning activities on the days before the experiments. Compliance

with instructions was confirmed by interview at the beginning of the

experimental session. Written informed consent was obtained from all

subjects. The experiments were approved by the local ethics

committee.1

Subjects slept in the laboratory on two experimental nights

(separated by at least 7 days) between 23:00 h and 07:00 h. Between

21:30 h and �22:30 h, they performed in balanced order on a

‘learning task’ and a ‘non-learning task’, described previously (Gais

et al., 2002). A word-pair associate learning task was employed for

examining effects of hippocampus-dependent declarative learning on

subsequent sleep. In this task, subjects learned a paired-associate list

of 336 unrelated words, arranged in 21 groups of eight pairs (e.g.

factory–horse and circle–scarf). Each group of pairs was presented

twice for 106 and 70 s on the first and second run, respectively,

resulting in a total learning time of 61.6 min. The inter-stimulus

interval between groups of word-pairs was 2 s. The first and the

second run of presentations were separated by a break of 2 min. To

induce comparable mnemonic strategies, subjects were instructed to

visually imagine a relation of the two otherwise unrelated words of

each pair. Cued recall was tested immediately after the second run of

presentations and in the next morning by presenting the subject with

the first word of each pair and asking him or her to name the second

one. No feedback was given. The non-learning task was designed to

closely resemble the learning task but without the intentional learning

component. In this task, subjects were instructed to count all letters

containing curved lines (e.g. J, P and U, but not W, Y and K) on word-

pair stimulus displays identical to those used for the learning task.

Thus, visual input, task duration and difficulty were equal in both task

conditions, but on the non-learning task, subjects were prevented from

semantically processing the words. Previous studies (Gais et al., 2002)

had confirmed that the two tasks were also comparable with respect to

subjective cognitive strain, task difficulty, and wearisomeness. Also,

tiredness, sleepiness and tenseness induced by performing the tasks

were comparable. At a delayed recall testing, subjects after the non-

learning condition were hardly able to recall any of the word-pairs,

ensuring that no substantial encoding of the word-pairs occurred

during the non-learning control condition.

Animals

Recordings were performed in male Sprague–Dawley rats (Charles

River Laboratories, Le Genest-St-Isle, France; n = 8; weight,

350–400 g). Recordings from all eight rats formed part of a dataset

used previously to describe the effects of odor–reward association

learning on hippocampal ripple activity (Eschenko et al., 2008). The

rats were housed individually, handled daily, and kept on a 12-h

light ⁄ 12-h dark cycle with lights on at 08:00 h. Water and food were

available ad libitum. All procedures were performed following the

1986 European Communities Council Directive and the Ministere de

l’Agriculture et de la Foret – Commission Nationale de l’Experimen-

tation Animal Decree 87848.2

After a 1-week recovery from surgery (described below), rats were

put on a food-restricted diet (20 g ⁄ day; body weight not less than 80%

of free-food weight) and habituated to the recording box and the

plugging procedure to ensure that they behaved naturally and spent

sufficient time in sleep. During recordings, rats were connected to the

amplifier by a cable allowing free movement within the box

(25 · 25 · 25 cm). Behavior was additionally tracked by a video

camera (Quickcam; Logitech, Moulin du Choc, Switzerland) mounted

on the top of the recording box. The video image was synchronized

with electrophysiological recordings. Each rat was tested on a non-

learning and a learning condition with at least a 1-day interval between

conditions (on average, 2.0 ± 0.6 days). Six rats were first tested on

the non-learning condition, and an opposite order of testing was used

in the remaining two rats. The order of testing was not completely

balanced, because data from several rats (the majority of which

2 M. Molle et al.

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belonged to the group tested first on the non-learning condition) had to

be discarded from analysis: (i) because histology revealed incorrect

positioning of the hippocampal electrode; (ii) because of technical

artefacts in EEG recordings; (iii) because of insufficient learning at

training; or (iv) because of a lack of SWS or slow oscillations during

the criterion period after training. (supplementary analyses accounting

for possible order influences were performed in rats and also in

humans, but did not reveal any significant influence of this factor). For

the non-learning condition, rats were put into the recording box

directly from the home cage before any learning experience. Then,

sleep was recorded for at least 3 h. To examine the effects of learning,

rats were trained on a four-way odor–reward association digging task

requiring the discrimination of odors placed at different locations

(Eschenko et al., 2006). The learning procedure proper took place on

the day after the pretraining for digging in gravel to find rewards. Four

gravel-filled cups were placed in the corners of the experimental box

(100 · 100 · 50 cm). Each cup contained a powdered spice with a

distinct odor, and the reward was placed on the bottom of one cup with

the predetermined target odor. Location of the cups varied from trial to

trial. Rats were trained to make an odor–reward association on 10

massed trials with a 3-min cut-off time on each trial and a 1–2 min

inter-trial interval. After learning, sleep was again recorded for at least

3 h. Behavioral tests and recordings were performed at the same time

of the day for an individual rat, and always between 10:00 h and

18:00 h during the light period, that is, when rats spend most of the

time sleeping. After the recording session, rats were deeply anesthe-

tized with pentobarbital (100 mg ⁄ kg) and perfused intracardially, and

brains were extracted for histological verification of recording sites.

Electrophysiological recordings

Humans

The EEG was recorded digitally during sleep using a SynAmps EEG

amplifier (NeuroScan Inc., Sterling, VA, USA). EEG signals were

sampled at a frequency of 500 Hz and filtered between 0.15 and

70 Hz. Ag–AgCl electrodes were placed according to an extended 10–

20 System (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4,

T5, T6, FT7, FT8, FC3, FC4, TP7, TP8, CP3, CP4, Fz, Cz, Pz) and

referenced to linked electrodes attached to the mastoids. Additionally,

horizontal and vertical eye movements and the electromyogram (chin

and neck) were recorded for standard polysomnography.

Animals

Animals were anesthetized with intraperitoneal sodium pentobarbital

(40 mg ⁄ kg initial dose with 0.1-mL supplements given as necessary)

and fixed in a stereotaxic frame. Atropine sulfate (0.2 mg ⁄ kg) was

administered to minimize respiratory distress. For the placement of

electrodes, the skull was exposed and burr holes were made. Two

stainless steel screw electrodes were placed over the prefrontal cortex

(AP = +4.0, L = 0.5; reference, AP = +0.5, L = 0.5) for subdural

EEG recordings. Another skull screw served as ground. This ground

electrode was placed over the prefrontal cortex contralateral to the two

EEG electrodes (i.e. AP = +2.0, L = )2.0). Hippocampal activity was

recorded as local field potentials (LFPs) from CA1. A tungsten

microelectrode (FHC; Bowdoin, ME, USA; resistance, > 1 MX;

diameter, 75 lm) was placed in the CA1 pyramidal cell layer

(AP = )3.5, L = 2.0) at a depth of 2.0–2.5 mm. The depth of the

electrodes was confirmed by monitoring unit activity during implan-

tation and by histological analysis. Hippocampal recordings were

referenced to a skull screw over the cerebellum. To validate sleep

scoring in a subset of two animals, electromyographic activity was

additionally recorded from the dorsal neck muscles with insulated

multistranded wires.3

EEGs and LFPs were recorded continuously and digitized with

16-bit resolution using a CED Power1401 converter and Spike2

software (Cambridge Electronic Design, Cambridge, UK). EEG and

LFP signals were amplified (·1000) and filtered between 0.01 and

300 Hz (Grass P5 Series Pre-Amplifier, Ouincy, MA, USA). The

signals were sampled at 1 kHz. Data were stored on a PC for off-line

analysis.

Data processing and statistical analysis

Sleep

Data processing was generally performed using Spike2 software and

the built-in script language (Cambridge Electronic Design). First, sleep

data were scored to determine the epochs of non-rapid eye movement

(REM) sleep for analyses. For the human data, each 30-s epoch of

sleep EEG was scored visually according to standard criteria

(Rechtschaffen & Kales, 1968). Sleep stages (1, 2, 3, 4 and REM

sleep), awake time and movement artefacts were scored. Stage 2 sleep

corresponds to light non-REM sleep, and stages 3 and 4 correspond to

SWS.

For the animal data, sleep was scored by visual assessment of

succeeding 10-s epochs according to the standard criteria of Bjorvatn

et al. (1998), after continuous power spectra of delta (1–4 Hz), theta

(5–10 Hz) and spindle (12–15 Hz) frequency bands had been

calculated. The awake state was marked by the presence of low-

amplitude fast activity; SWS was identified by continuous high-

amplitude slow activity and the regular appearance of spindles; and

transitions from SWS into REM sleep were identified by a decrease in

high-amplitude slow activity, increase of theta activity, and the

presence of spindles. REM sleep was characterized by dominant theta

activity and low-voltage fast activity. Behavioral states were addi-

tionally verified by video.

Slow oscillations, spindles, and ripples

Analyses of the relationship between slow oscillations and spindles

were performed on the human and rat data in basically the same way.

Ripples were analysed only in rats using the LFP recordings from

CA1. EEG recordings (Fz, Cz and Pz in humans, prefrontal in rats)

during non-REM sleep (excluding sleep stage 1 in humans and

intermediate sleep in rats) were analysed during the first hour of sleep

after learning. We include stage 2 human non-REM sleep in our

analyses in order to use sleep stages as comparable as possible with

the not further subdivided rat non-REM sleep. To reduce the amount

of data and facilitate subsequent operations, the EEG was down-

sampled to 100 Hz. Before down-sampling, a low-pass filter of 30 Hz

was used as an anti-aliasing filter to reduce the bandwidth of the signal

(i.e. to satisfy the Shannon–Nyquist sampling theorem criterion).

Subsequently, to identify slow oscillations, a low-pass filter of 2 Hz

was applied and time points of positive to negative zero crossings

were computed in the resulting signal. Then, the lowest and highest

value between every two of these time points were detected (i.e. one

negative and one positive peak between two succeeding positive to

negative zero crossings; Fig. 1). The averages of the negative peak

amplitudes (x), the positive peak amplitudes (y) and their difference

(y–x) were calculated. Intervals of positive to negative zero crossings

with a length of 0.9 s (for rats, 0.5 s) to 2 s were marked as slow

oscillation epochs if the corresponding negative peak amplitude was

lower than two-thirds of x and the corresponding amplitude difference

Spindles, ripples and sleep slow oscillations 3

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(positive peak minus negative peak) was at least two-thirds of y–x. By

application of this amplitude threshold criterion, we restricted our

analyses to epochs containing the largest slow oscillations. In humans,

the majority of these large slow oscillations were found in SWS

(1645.5 ± 145.2). During non-REM sleep stage 2, 1071.8 ± 150.7

slow oscillation events were identified.

To determine spindle activity in humans and rats, a bandpass of

12–15 Hz was applied on the 100-Hz EEG signal, and to identify

ripple activity, a bandpass of 150–250 Hz was applied on the CA1

LFP. Subsequently, the mean standard deviation of the bandpass-

filtered signals was computed over all artefact-free non-REM sleep

epochs, and all peaks and troughs above and below the thresholds of

± 1.5 and ± 2.5 SD were used to mark the presence of spindle activity

and ripple activity, respectively. Discrete spindles and ripples were

then defined by periods comprising at least six successive events

(peaks and troughs). All other events (outside the discrete spindles and

ripples) were deleted and not included in any analysis.

A first type of comparison between learning and non-learning

conditions concentrated on waveform characteristics of the slow

oscillations. For these analyses, the EEG signals (0.15–30 Hz) were

averaged for intervals of ± 1.5 s around the negative peaks of all

detected slow oscillation epochs, and amplitudes of the averaged slow

oscillation were compared between the learning and non-learning

conditions. To further describe differences in the morphology of slow

oscillations, phase histograms were calculated to determine the

distribution of negative and positive peaks within the slow oscillation

cycle. Phase histograms were calculated by determining the frequency

of, respectively, negative and positive peaks for 10� bins with

reference to the total 0–360� slow oscillation cycle (defined by the

distance between one positive-to-negative zero crossing to the next).

The counts in every bin were divided by the number of slow

oscillation cycles used for calculation in the individual subject and

experimental conditions (learning ⁄ non-learning).

The second type of comparison was aimed at investigating how

spindle and ripple activity were distributed with respect to the phase of

the slow oscillation. For these analyses, phase histograms were

calculated for spindle and ripple counts (number of peaks and troughs

exceeding the threshold), again for succeeding bins of 10� of the entire

0–360� slow oscillation cycle, in the same way as described above for

slow oscillation peaks.

In a third step, to investigate time courses of spindle activity in

relation to ripples, event-correlation histograms were calculated for the

counts of spindle activity with reference to the first event (peak or

trough) of each ripple, using 5-s windows (2.5-s offset) and a bin

width of 50 ms. The counts in every bin were divided by the number

of ripples used, and then divided by the bin width to give event rate

Fig. 1. Identification of slow oscillations, spindles, and ripples. (A) A 10-s excerpt of prefrontal EEG and hippocampal local field potential (LFP) recording duringslow-wave sleep in a single rat is shown together with corresponding signal traces and event channels calculated during slow oscillation analysis and spindle ⁄ rippledetection. From top to bottom: prefrontal cortex EEG (PFC EEG), slow oscillation activity (low-pass 2 Hz, LP2), intervals of positive-to-negative zero crossings(PosNegCr), detected negative and positive half-wave peaks for accepted slow oscillation intervals (Neg Hw, Pos Hw), spindle activity (bandpass, 12–15 Hz, BPSpin), detected spindle events, that is, troughs and peaks, hippocampal local field potential (CA1 LFP), ripple activity (bandpass, 150–250 Hz, BP Ripp), detectedripple events (troughs ⁄ peaks). Discrete spindles and ripples were defined by periods comprising at least six successive events. Parallel vertical lines indicate thebeginning and end of the 1.0-s interval of one slow oscillation epoch shown enlarged on the right side (B).

LOW

RESOLUTIO

NFIG

4 M. Molle et al.

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per second (Hz). The individual histograms were subjected to

z-transformation to eliminate the considerable variability across rats

and conditions. Grand mean averages of the histograms across all

animals were calculated for the non-learning and learning conditions.

Statistical comparisons in general relied on analyses of variance and

two-sided paired t-tests. The t-tests were calculated for summarized

epochs; that is, the mean values over all points of these epochs were

compared between the learning and non-learning conditions across all

subjects. The summarized epochs were chosen after exploratory point-

by-point t-tests had indicated significant differences across several

successive points. Epochs were included for further analyses only if

the numbers of points summarized in the respective epochs were

greater than the minimum numbers required to demonstrate a non-

chance difference (Guthrie & Buchwald, 1991).

Results

Humans

About 900 slow oscillation epochs were detected in each channel

during non-REM sleep (mainly SWS – 60.8 ± 5.0% of all identified

events) in the first hour of sleep. This equates to �8.3 slow oscillations

per 30-s interval. Because supplementary analyses on a dataset

restricted to slow oscillations occurring during SWS yielded essen-

tially the same results as analyses including all slow oscillations

identified in non-REM sleep, this report is restricted to the findings in

the extended set of slow oscillations. There were no significant

differences between the learning and non-learning conditions in the

number and length of detected slow oscillations, the total time of

analysis, or the threshold for spindle activity (Table 1, left). Note that

in humans the length of the slow oscillation epochs was about 500 ms

longer than in rats. However, in both species, the mean length of the

slow oscillation epochs corresponded exactly to the respective peak in

the power spectrum of the EEG non-REM sleep signal, that is, at

0.8 Hz in humans and at 1.35 Hz in rats (Fig. 2).

Figure 3A shows the averaged EEG signals at Fz time-locked to the

negative peaks of all detected slow oscillation epochs. As compared

with the non-learning condition, learning induced a discrete increase

in amplitude during the depolarizing up-state of the slow oscillation,

that is, in an interval 500–800 ms following the negative peak

(30.4 ± 2.6 vs. 28.0 ± 2.4 lVat non-learning, P < 0.01, gp2 = 0.413).

The same, although smaller, learning-induced increase in amplitude

was seen during the depolarizing up-state that preceded the negative

peak (i.e. 450–200 ms before the negative peak, 9.2 ± 1.0 vs.

Table 1. Slow-oscillation analysis: humans

Analysis andelectrode site Non-learning Learning P-value*

No. slow oscillationsFz 928.4 ± 41.7 904.2 ± 56.7 0.76Cz 904.5 ± 32.6 904.2 ± 63.1 1.00Pz 903.1 ± 29.3 890.3 ± 59.5 0.86

Threshold spindle activity (lV)Fz 11.7 ± 1.2 11.8 ± 1.1 0.83Cz 13.2 ± 1.1 13.2 ± 1.0 0.99Pz 12.2 ± 1.1 12.2 ± 1.0 0.91

Slow oscillation length (s)Fz 1.283 ± 0.007 1.274 ± 0.008 0.23Cz 1.318 ± 0.006 1.314 ± 0.010 0.67Pz 1.334 ± 0.008 1.335 ± 0.011 0.82

Time of analysis (min)All 53.9 ± 1.2 53.5 ± 1.1 0.73

*P-values derive from comparison of learning and non-learning conditions bypaired t-test.

Fig. 2. Power spectrum of (A) human EEG at Fz, Cz and Pz, and (B) of ratprefrontal cortical (PFC) recordings during non-REM sleep in the first hour ofsleep. Note that slow-wave activity peaks at 0.8 Hz in the human spectrum andat 1.35 Hz in rats. Unlike the human spectrum, the rat spectrum does not showa distinct peak in the spindle frequency band (12–15 Hz).

Fig. 3. (A) Averaged human EEG in Fz and (B) EEG from rat prefrontalcortex (PFC), time-locked to the negative peaks of all detected slow oscillationepochs after learning (black) and non-learning (gray). Thick horizontal linesand asterisks indicate epochs with significant amplitude differences between thelearning and non-learning condition (**P < 0.01, *P < 0.05).

Spindles, ripples and sleep slow oscillations 5

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7.2 ± 0.7 lV, P < 0.05). The increase in slow oscillation amplitude

was restricted to Fz and was not found in the EEG averages at Cz

and Pz.

For a more detailed account of possible effects of learning on

waveform morphology of the slow oscillations, we determined the

phase distribution of negative and positive peaks (phase distributions

of peaks are shown together with the distribution of spindle events

during the slow oscillation in Fig. 4A–C). Generally, negative peaks of

the slow oscillation, that is, down-state peaks, in most cases (> 70%)

occurred in the 30–110� range of the slow oscillation, whereas most

positive peaks (> 70%), that is, up-state peaks, fell between 220� and

320�. Learning shaped the slow oscillation waveform by strongly

increasing the frequency of negative peaks in the standard 30–110�

down-state range (72.7 ± 0.6% vs. 70.6 ± 0.7% at non-learning,

P < 0.001, gp2 = 0.683), simultaneously decreasing the frequency of

delayed negative peaks (falling in the 140–220� range: 14.7 ± 0.4%

vs. 16.6 ± 0.7%, P < 0.01, gp2 = 0.565; Fig. 4A). Although this

strong shaping influence of learning on the phase of the negative slow

oscillation peaks was only apparent over the prefrontal cortex (at Fz),

learning in all recording sites (also including posterior locations)

shifted the positive up-state peaks to an earlier time. This was

expressed mainly as an increased frequency of up-state peaks in an

early range (at Fz between 130� and 200�: 15.6 ± 0.9% vs.

13.6 ± 0.6%, P < 0.05, gp2 = 0.364) and a decreased frequency of

delayed up-state peaks occurring after 320� (Fz: 23.7 ± 1.4% vs.

25.9 ± 1.9%, P < 0.05, gp2 = 0.324; see Fig. 4B and C for the

corresponding changes at Cz and Pz).

Spindle activity was clearly modulated by the slow oscillation phase

and, as expected, reduced during the down-state (i.e. 30–110�) and

enhanced during the up-state (220–320�, P < 0.001; for a comparison

between up-phases and down-phases in Fz, Cz, and Pz4 ). As compared

with the non-learning control condition, after learning, spindle activity

was enhanced at the transition into and during the up-states. This

learning-dependent increase was clearest at Cz in the broad 130–330�

range (in mean counts of activity per phase range: 0.220 ± 0.005 vs.

0.209 ± 0.004, P < 0.01, gp2 = 0.505) and at Pz in the 140–290�

range (0.239 ± 0.006 vs. 0.228 ± 0.007, P < 0.01, gp2 = 0.601;

Fig. 4B and C). At Fz, spindle activity was enhanced in the more

narrow 190–270� up-state range (0.212 ± 0.006 vs. 0.203 ± 0.005,

P < 0.05; Fig. 4A).

Animals

More than 1100 slow oscillation epochs were detected within non-

REM epochs during the first hour of sleep. This equates to �16.2 slow

oscillations per 30-s interval. The number of identified slow oscilla-

tions and their length (i.e. duration between the two succeeding

positive-to-negative zero crossings) did not differ between the learning

and non-learning conditions. There was also no difference in the total

time of analysis and the thresholds for spindle and ripple activity

between learning conditions (Table 2). Although the threshold for the

ripple activity was on average more than 1 lV higher after learning,

this difference failed to reach significance (P > 0.1).

Figure 3B shows the average prefrontal EEG, time-locked to the

negative peaks of all detected slow oscillation epochs. As in humans,

learning increased the amplitude of the positive up-state of the slow

oscillation around its peak time (i.e. 180–240 ms after the negative

peak: 48.5 ± 7.4 vs. 44.2 ± 6.4 lV, P < 0.05, gp2 = 0.422). In the

time interval after the depolarizing positive up-state peak, slow

oscillations appeared to recover to baseline values earlier when the rats

had learned than in the non-learning control condition, resulting in

decreased amplitudes 450–530 ms after the negative peak ()4.3 ± 4.1

vs. )1.1 ± 3.3 lV, P < 0.05; Fig. 3B). With regard to the phase

distribution, negative peaks mostly (> 70%) fell in the 60–120� range

of the slow oscillation, and positive peaks mostly (> 70%) within

240–300�. The phase distribution of negative and positive peaks did

not differ between the learning and baseline conditions (Fig. 5).

As expected, spindle and ripple activity were reduced during the

down-state (60–120�) as compared to the slow oscillation up-state

(240–300�, P < 0.001; Fig. 5). Learning increased spindle activity,

relative to the non-learning conditions, with this increase being

predominant at the transition from the depolarizing up-states to the

hyperpolarizing down-states of the slow oscillation (10–80�:

0.58 ± 0.04 vs. 0.51 ± 0.035 ; P < 0.01, gp2 = 0.565). Learning

induced an even greater increase in ripple activity. Surprisingly, this

increase in ripple activity was not restricted to the positive up-state of

the slow oscillation, where it was concentrated during the late 280–

360� down-phase (1.51 ± 0.23 vs. 1.24 ± 0.216 in the non-learning

condition, P < 0.01, gp2 = 0.682). It was equally present during the

negative down-states, being most distinct in the 110–160� interval

(0.53 ± 0.08 vs. 0.43 ± 0.08, P < 0.01, gp2 = 0.679, P > 0.4, for

respective slow oscillation phase · learning condition anova inter-

action term).

Event-correlation histograms were calculated to investigate the time

course of spindle activity in relation to hippocampal ripples, using the

first identified ripple trough or peak as reference. Histograms show a

distinct increase in spindle activity around the ripple bursts (Fig. 6).

Spindle activity began to increase about 200 ms before the ripple and

thereafter remained elevated for more than 1.25 s. After learning, this

enhancement lasted even longer. Thus, as compared with non-learning

conditions, spindle activity after learning was significantly enhanced

1.5–2.0 s after the ripple onset (z-transformed event counts: 0.31 ± 0.2

vs. )0.37 ± 0.19, P < 0.01, gp2 = 0.644; Fig. 6).

Discussion

We compared slow oscillations and the temporally grouping influence

of slow oscillations on neocortical spindles and hippocampal ripples,

in humans and rats, during non-REM sleep after learning (vs. non-

learning conditions). We focused on tasks previously shown to induce

robust increases in thalamocortical spindle and hippocampal ripple

activity during post-learning sleep (Gais et al., 2002; Molle et al.,

2002b; Eschenko et al., 2006, 2008). The main findings of the study

are as follows. (i) In both humans and rats, learning shaped the slow

oscillation waveform such that the depolarizing up-states exhibited an

increased amplitude relative to non-learning. The effect appeared to be

stronger in humans, where learning additionally shaped the timing of

the frontocortical negative slow oscillation peak, with a distinctly

increased frequency of negative peaks falling into a narrow 30–110�

standard range defining the hyperpolarizing down-state of the slow

oscillation. Also, positive slow oscillation peaks increased slightly

earlier in humans at all recording sites, after learning relative to non-

learning conditions. (ii) As expected, spindle activity was temporally

grouped by the slow oscillation such that spindle activity reached a

minimum during the (EEG-negative) hyperpolarizing down-state of

the slow oscillation and a maximum during the succeeding depolar-

izing up-state. This was seen in both humans and rats. Learning

increased spindle activity, confirming what we have found previously

in humans (Gais et al., 2002; Molle et al., 2002b) and rats (Eschenko

et al., 2006). This increase was more pronounced in humans than in

rats, and only in humans was it clearly concentrated in slow oscillation

up-state periods. (iii) Ripple activity in rats also showed the expected

6 M. Molle et al.

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temporal grouping by the slow oscillation, with minimum ripple

activity during the down-state and maximum activity during the

up-state of the slow oscillation (Sirota et al., 2003; Battaglia et al.,

2004; Molle et al., 2006). Although learning strongly increased ripple

activity (Eschenko et al., 2008), this increase, contrary to expectations,

did not concentrate in up-states but was equally present during down-

states and up-states of the slow oscillation. (iv) Spindle activity

distinctly increased about 200 ms before the onset of hippocampal

ripples, and this enhancement of spindle activity was significantly

prolonged after learning.

Hippocampus-dependent declarative learning of vocabulary during

subsequent SWS increased EEG coherence in the slow oscillation and

delta (1–4 Hz) frequency band in a distributed network of recording

sites. The increase in EEG coherence was focused on the up-state of

the slow oscillation (Molle et al., 2004). Also, training on a

sensorimotor task before sleep, as compared with a non-learning

control condition, increased the amplitude of slow oscillations during

subsequent SWS. The increase in slow oscillation amplitude was

restricted to those cortical areas primarily involved in the acquisition

of the task, and was correlated with the improvement in task

performance at later retrieval testing (Huber et al., 2004). Here, we

confirm that learning prior to sleep increases slow oscillation

amplitude over the prefrontal cortex in humans and, although less

strongly, in rats as well. Together, these findings suggest that slow

Fig. 4. Distribution of negative (top) and positive peaks of identified slow oscillations (middle) and of spindle activity (bottom) during a complete 0–360� slowoscillation cycle in humans. Histograms represent grand mean values (+SEM) across subjects (n = 12) in recordings from Fz (A), Cz (B) and Pz (C) during non-REM sleep after learning (black) and non-learning (white). Gray rectangles on top indicate the up-state and down-state half-waves of the slow oscillation cycle, withgray shaded areas indicating the standard up-state and down-state ranges where most (i.e. > 70%) of, respectively, the positive and negative slow oscillation peaksoccurred. Horizontal brackets and asterisks indicate phases with significant differences between the learning and non-learning conditions (***P < 0.001, **P < 0.01,*P < 0.05).

Table 2. Slow-oscillation analysis: animals

Analysis Non-learning Learning P-value*

No. slowoscillations

1149.8 ± 145.4 1137.0 ± 100.5 0.95

Threshold spindleactivity (lV)

25.13 ± 3.15 26.00 ± 3.63 0.31

Threshold rippleactivity (lV)

18.06 ± 2.90 19.31 ± 3.18 0.10

Slow oscillationlength (s)

0.754 ± 0.024 0.741 ± 0.030 0.24

Time of analysis (min) 33.90 ± 3.48 34.11 ± 2.08 0.96

*P-values derive from comparison of learning and non-learning conditions bypaired t-test.

Spindles, ripples and sleep slow oscillations 7

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oscillations are locally promoted within neocortical networks by the

prior use of these networks for encoding of information, possibly

reflecting underlying processes of synaptic potentiation induced

during learning (Tononi & Cirelli, 2006; Marshall & Born, 2007).

Our findings extend previous work in showing that the increase in

slow oscillation amplitude affects mainly the depolarizing up-state,

which fits well with the observation in rats that neuronal replay of

recently encoded visuo-spatial memories in the visual neocortex is

framed within cortical up-states (Ji & Wilson, 2007). Networks tagged

through synaptic potentiation during prior learning may develop

stronger and more synchronized membrane depolarization, which may

be mediated via hyperpolarization-dependent activation of T-type Ca2+

channels in conjunction with kainate and metabotropic glutamate

receptor activation (Hughes et al., 2002; Cunningham et al., 2006;

Destexhe et al., 2007). The increased depolarization during slow

oscillation up-states occurring only over the anterior cortex in our

human experiments may reflect a particular involvement of hippo-

campo-prefrontocortical loops during the encoding and formation of

declarative memories (Molle et al., 2002a; Jensen & Lisman, 2005;

Euston et al., 2007). Learning of (visually presented) word-pairs has

been found to involve, in particular, left prefrontal and temporal

regions, aside from widespread activation including also parietal and

occipital cortical areas (e.g. Molle et al., 2002a). Notably, a recent

study of sleep-induced changes in the representation of word-pair

memories by functional magnetic resonance imaging pointed towards

a particular involvement of medial prefrontal cortical regions (Gais

et al., 2007). As compared with post-learning wakefulness, functional

connectivity between the hippocampus and medial prefrontal cortex

was distinctly enhanced at recall testing 2 days after learning, when

subjects had slept on the night after learning, and at a recall test

6 months later, activity in the medial prefrontal cortex areas was also

enhanced in the post-learning sleep condition. Against this back-

ground, the post-learning increase in slow oscillations located over the

anterior cortex underscores the importance of this area in the presumed

process of system consolidation, taking place during sleep, when these

areas gain increasing control over the retrieval of respective declar-

ative memories (Takashima et al., 2006).

Remarkably, prior learning not only enhanced the slow oscillation

depolarization up-state but, in humans, also had a strong shaping

influence on the timing of the negative down-state peak. After

learning, negative peaks were less often delayed but occurred most

regularly in the 30–110� range of the slow oscillation cycle. This

finding underscores the importance of the hyperpolarizing down-state

for setting the temporal frame of presumed memory processing

enabled during subsequent up-states (Molle et al., 2004; Volgushev

et al., 2006). It has been suggested that the down-state to up-state

transition of the slow oscillation via hyperpolarization-activated cation

channels is supported by miniature excitatory postsynaptic potentials

summating during the down-state (Bazhenov et al., 2002). Assuming

that the probability of miniature excitatory postsynaptic potentials

during the down-phase is selectively enhanced at synapses previously

activated during learning (Eliot et al., 1994; Oliet et al., 1996; Bao

et al., 1998; Hoffman & McNaughton, 2002), this could explain an

overall accelerated and less variable timing of down-state peaks after a

learning experience.

Our data confirm a strong temporally grouping influence of the slow

oscillations on cortical spindle activity, whereby the depolarizing up-

state is associated with maximum spindle activity. This was observed

in both humans and rats (e.g. Destexhe et al., 1999; Steriade, 1999;

Molle et al., 2002b, 2006; Clemens et al., 2007). The depolarizing

phase of the slow oscillation (corresponding to a cortex surface

positive and depth negative extracellular field potential) is associated

with markedly increased firing, including corticothalamic neurons that

drive the generation of spindle oscillations in thalamo-neocortical

feedback loops (Contreras & Steriade, 1995; Steriade et al., 1996;

Destexhe et al., 1999). Thalamocortical spindle activity is not only a

general marker of learning capacity (Schabus et al., 2006), but also

shows a quite robust enhancement during non-REM sleep succeeding

acute learning (Gais et al., 2002; Schabus et al., 2004; Clemens et al.,

Fig. 5. Distribution of negative (A) and positive (B) peaks of the prefrontalslow oscillation and of cortical spindle (C) and hippocampal ripple (D) activityduring a complete 0–360� slow oscillation cycle in rats. Histograms representgrand mean values (+SEM) across all rats (n = 8) during the first hour of non-rapid eye movement sleep after learning (black) and non-learning (white). Grayrectangles on top indicate the up-state and down-state half-waves of the slowoscillation cycle, with gray shaded areas indicating the standard up-state anddown-state ranges where most (i.e. > 70%) of, respectively, the positive andnegative slow oscillation peaks occurred. Horizontal brackets and asterisksindicate phases with significant differences between the learning and non-learning conditions (**P < 0.01).

8 M. Molle et al.

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2005; Eschenko et al., 2006; Fogel & Smith, 2006; Schmidt et al.,

2006). As expected, we showed here that the learning-dependent

increase in spindle activity in humans is restricted to periods of the

depolarizing up-state of the slow oscillation. In combination with the

learning-induced enhancement of the depolarizing up-state of the slow

oscillation, this finding underscores the importance of slow oscillation

depolarization and associated corticothalamic volleys for driving

spindle activity. Spindle activity, together with T-type Ca2+ channel-

mediated slow oscillation depolarization, provides neocortical pyra-

midal cells with massive Ca2+ influx, probably facilitating plastic

changes in these networks (Contreras et al., 1997; Sejnowski &

Destexhe, 2000; Destexhe et al., 2007). In fact, repeated spike

discharges induced by spindle-like activity in vitro can efficiently

trigger long-term potentiation in neocortical synapses (Rosanova &

Ulrich, 2005). Moreover, synchronous spindle activity in vivo occurs

preferentially at synapses previously potentiated by tetanizing afferent

stimulation (Werk et al., 2005). Thus, enhanced slow oscillation up-

states enforcing thalamic generation of spindle activity could be a

mechanism whereby learning promotes lasting plastic changes in

corticothalamic feedback loops that underly the consolidation of

neocortical representations in memory.

In the hippocampal circuitry, neuronal replay of recently encoded

memories occurs in conjunction with ripples typically forming sharp

wave–ripple complexes (Nadasdy et al., 1999; Csicsvari et al., 2000;

Diba & Buzsaki, 2007). The occurrence of ripples, like that of

spindles, is grouped by the slow oscillation, with there being

minimum ripple activity during the hyperpolarization down-state in

rats, mice, and humans (Sirota et al., 2003; Battaglia et al., 2004;

Isomura et al., 2006; Molle et al., 2006; Clemens et al., 2007). Slow

oscillations of neocortical origin probably reach the hippocampus via

the temporo-ammonic pathway (e.g. Sirota & Buzsaki, 2005;

Wolansky et al., 2006). Hippocampal networks per se are unlikely

to generate slow oscillations (Isomura et al., 2006). Learning odor–

reward associations prior to sleep has been shown to enhance ripple

activity during subsequent SWS (Eschenko et al., 2008). Here we

confirm that neocortical slow oscillations exert a strong grouping

influence on hippocampal ripple activity. However, contrary to

expectations, the learning-induced increase in ripple activity was not

subject to this grouping influence, but was, in fact, comparable

during the down-states and up-states of the neocortical slow

oscillation. This observation stands in contrast with recent results

indicating that hippocampal memory replay activity occurs within

temporal frames of neocortical up-states (Ji & Wilson, 2007),

although that study analysed multi-unit activity, and direct LFP

recordings of ripples were not performed. Also, rats were trained on

a visuo-spatial task before sleep, and interactions between neocor-

tical up-states and hippocampal memory replay activity referred to

activity in the visual cortex. The failure of slow oscillations to group

learning-induced hippocampal sharp wave–ripple activity may hence

relate to the use of an olfactory discrimination task. Odor stimuli

reach primary olfactory cortical areas, including the pyriform and

enthorinal cortex, directly from the olfactory bulb (Mori &

Yoshihara, 1995), bypassing the thalamo-neocortical system, which

is the source of the sleep slow oscillation. These cortical olfactory

processing areas project directly to higher-order regions, including

the hippocampus, again bypassing the thalamo-neocortical system,

which probably allows direct induction of hippocampal sharp

wave–ripples (Zelano & Sobel, 2005). Amygdalar and prelimbic

regions of the frontal cortex represent the main regions involved in

odor–reward association learning (Tronel & Sara, 2002, 2003). It

may be this immediate access of the olfactory system to limbic and

hippocampal structures that prevents slow oscillations, originating

from thalamo-neocortical circuitry, from achieving control over

ripples that are promoted via direct pathways from the pyriform and

enthorinal cortex to the hippocampus. However, because the failure

of slow oscillations to temporally group learning-induced ripples was

unexpected, this explanation is post hoc and necessarily remains in

need of further experimental proof.

Using an odor-based task may likewise explain why learning-

induced increases in spindles and their grouping by slow oscillations,

as well as the shaping influence of prior learning on slow oscillation

waveforms altogether, appeared to be less pronounced in rats than in

humans. More generally, it can be asked whether the different tasks

used in both species, humans and rats, allow for adequate comparison

of functions of slow oscillation depending on learning and consol-

idation. The tasks were chosen on the basis of evidence from

previous studies (Gais et al., 2002) indicating that learning both of

word-pair associates in humans and of odor–reward associations in

rats induced a robust increase in thalamocortical spindle activity, with

this increase being associated with improved retention of the acquired

memories. Also, word-pair memory is a classical hippocampus-

dependent task, and there is likewise evidence for hippocampal

contributions to the formation of odor–reward associations in rats.

Performance of this task was followed by a strong increase in

hippocampal ripple activity during post-learning SWS in rats

(Eschenko et al., 2008), although the involvement of the hippocam-

pus during actual encoding may be less substantial, given that

hippocampal fos activity was not shown to be enhanced immediately

after training the task (Tronel & Sara, 2002). The tasks are also

similar in that they require behaviors typical of the respective species.

At the same time, the two behaviors (associating two words vs.

associating an odor with reward), of course, differ essentially in

regard to the sensory modality, cognitive complexity, and motor

demands. The word-pair associate task is an exclusively declarative

task, whereas it would be a stretch to consider odor–reward

associations as declarative. It cannot be certain whether task difficulty

was comparable and to what extent the two species relied on

similar cognitive resources to perform the task. Circumstances and

Fig. 6. Event-correlation histogram of prefrontal spindle activity in a 5-sinterval around the onset of hippocampal ripples. The x-axis zero represents thefirst ripple event (i.e. above threshold peak or trough). Values of spindle activity(y-axis) represent grand means of spindle event counts (peaks and troughs)during non-rapid eye movement sleep after learning (+SEM, black) and non-learning ()SEM, gray) across all animals (n = 8). Before averaging, individualhistograms were z-transformed. Horizontal brackets and asterisks indicateperiods with significant differences between the learning and non-learningconditions (**P < 0.01).

Spindles, ripples and sleep slow oscillations 9

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procedures of testing also differed for humans and rats. For example,

whereas humans learned the task in the evening before the habitual

sleep period, learning in rats took place in the morning hours and

around noon, thus overlapping with their habitual rest phase. Thus,

the circadian phase of testing may have been different from that in

humans, and some rats might have been even slightly sleep deprived

at the time of training. Also, the order of learning and non-learning

conditions was not as completely balanced in the rats as in the

humans. Collectively, although these differences in the tasks to some

extent derive from the nature of the species tested, they can be taken

to raise questions about the comparability of the tasks as a matter of

principle, and should caution against any premature conclusion of a

less clear temporal grouping effect of slow oscillations in rats than in

humans. In addition, there are essential differences between the

relevant brain regions between the species. The prefrontal cortex is

clearly more differentiated and also more tightly connected via

indirect and direct pathways with the hippocampal system in primates

than in rats (e.g. Goldman-Rakic et al., 1984; Thierry et al., 2000).

Also, the peak frequency of the slow oscillation spectral peak is

slightly slower in humans than in rats, and only humans show a

distinct spectral peak in the spindle frequency band (Fig. 2),

suggesting that this rhythm makes a more important contribution to

the human SWS activity.

Interestingly, in our rats, hippocampal ripples were followed by a

distinct increase in spindle activity, which was even more persistent

when the rats had learned prior to sleep. A similar temporal

association of ripple and spindle activity was observed in previous

studies in rodents and humans under non-learning conditions (Siapas

& Wilson, 1998; Molle et al., 2006; Clemens et al., 2007). This

pattern suggests a direct interaction between spindles and ripples,

although it still does not exclude a supra-ordinate synchronizing

influence of the slow oscillation on both events (because, independent

of learning, both spindles and ripples show a pronounced grouping in

parallel with the slow oscillation up-state). The fact that spindle

activity distinctly increased �200 ms before ripple onset and

remained elevated for up to 2 s thereafter suggests a loop-like

scenario in which emergent thalamocortical spindle activity drives

hippocampal ripples, which in turn feed back to support the generation

of continuing thalamic spindle activity. This feedback action of ripples

on spindles is enhanced after learning. Such loop-like coordination

would enable a temporally fine-tuned hippocampus-to-neocortical

information transfer, whereby ripples and associated memory replay

feeds exactly onto the excitatory phases of the spindle cycle

(7 Z. Clemens et al., unpublished observations; (Siapas & Wilson,

1998). However, although there is evidence that neocortical slow

oscillations exert a temporally grouping influence on hippocampal

neuronal activity, it is presently completely unclear whether and how

hippocampal activity during the slow oscillation cycle can affect

thalamocortical neuronal activity. Thus, despite some limited support

from other studies (Siapas & Wilson, 1998), the proposed feedback

scenario is clearly in need of further investigation.

Acknowledgements

We thank A. Otterbein for technical assistance and L. Marshall for discussionson our results. This research was supported by grants from the Volkswagen-Stiftung and the Deutsche Forschungsgemeinschaft to M. Molle, S. Sara and J.Born.

Abbreviations

LFP, local field potential; REM, rapid eye movement; SWS, slow-wave sleep.

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