Falling asleep is accompanied by a gradual loss of consciousness of the external environment. Although the thalamic gating hypothesis had claimed a blockade of sensory information at the thalamic level (McCormick and Bal, 1994), we now know that sleepers still monitor the environment for their safety and survival (Ai et al., 2018; Andrillon et al., 2016; Andrillon et al., 2017; Arzi et al., 2012; Blume et al., 2017; Koroma et al., 2022; Ruch et al., 2014; Ruch and Henke, 2020; Züst et al., 2019). The sleeping brain decides whether an external event can be disregarded, requires immediate awakening or should be stored for later consideration in the waking state (Ameen et al., 2022; Blume et al., 2018; Formby, 1967; Holeckova et al., 2006; Moyne et al., 2022; Oswald et al., 1960; Türker et al., 2023). Although the processing of external information during sleep versus wakefulness is reduced (Andrillon et al., 2016), sleep still allows for the detection of semantic incongruity (Bastuji and García-Larrea, 1999; Ibáñez et al., 2006) and the detection of rule violations (Ruby et al., 2008; Strauss et al., 2015). Furthermore, there is evidence that the sleeping brain cannot only process information, but can store new information, ranging from tone-odor to word-word associations (Ai et al., 2018; Andrillon and Kouider, 2016; Arzi et al., 2012; Ataei et al., 2023; de Lavilléon et al., 2015; Koroma et al., 2022; Züst et al., 2019). However, it remains unclear whether and under what circumstances the most sophisticated form of human learning, namely episodic memory formation, can proceed during deep sleep (Ruch and Henke, 2020).

The term episodic memory refers to the recollection of personally experienced episodes (Tulving, 2002). Episodic memory formation depends on hippocampal-neocortical interactions (Cohen and Eichenbaum, 1993; Henke, 2010). Because episodic memory belongs to declarative/explicit memory, episodic memory was long associated with wakefulness and conscious awareness of events (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire and Dede, 2015; Tulving, 2002). In the meantime, counterevidence suggests that hippocampal-assisted episodic memory formation may also proceed without conscious awareness of the learning material (Duss et al., 2014; Reber et al., 2012; Schneider et al., 2021; Züst et al., 2019). When applying such tasks, unconscious hippocampus-assisted episodic encoding of subliminal (invisible) words was revealed (Duss et al., 2014; Reber et al., 2012).

Thus, in the current study, we define episodic memory based on its key computational properties, which are the rapid formation of new associations calling on the hippocampus' ability for one-shot relational binding, and the flexible retrieval of these associations in novel contexts. We used this definition to explore episodic verbal learning during the unconsciousness of deep sleep. We applied a sleep-learning and awake-retrieval task that requires rapid semantic associative encoding of pseudowords and German translation words, memory storage of the formed associations over days, and a cued associative retrieval that requires a flexible representation of the sleep-encoded pseudoword-word associations. As proposed and demonstrated by previous work (Cohen and Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014; O’Reilly and Rudy, 2000), these task-enforced demands call upon the episodic memory system, according to its computational definition.

Although slow-wave sleep (deep sleep) is characterized by an average neurochemical milieu and neural functional connectivity that does not favour episodic memory formation, slow-wave sleep is not a unitary state. In fact, neuronal activity and excitability waxes and wanes during slow-wave sleep, thereby generating the eponymous electroencephalographic slow-waves that oscillate at 1 Hz and are characterised by slow-wave peaks and troughs. A peak and a trough each last around 500 ms (Berry et al., 2015). Because peaks of slow-waves are associated with high neural excitability and wake-like network characteristics, peaks might provide the necessary plasticity mechanisms for episodic memory formation (Cox et al., 2014a; Diba and Buzsáki, 2007). Züst et al., 2019 reported successful paired-associate learning in humans during peaks of slow-waves recorded in a mid-day nap with the retrieval of the sleep-learned associations tested after awakening on the same day. Because peaks with their depolarized neural states are also the time windows when memories formed during the previous days are replayed, strengthened, and consolidated (Göldi et al., 2019; Mölle et al., 2002; Mölle et al., 2011; Muehlroth et al., 2019; Staresina et al., 2015), high-jacking peaks for de novo learning might impair ongoing memory consolidation. Troughs on the other hand are characterized by neural silence (Cox et al., 2014a; Destexhe et al., 2007; Schabus et al., 2012). One speculative argument in favour of troughs might be the relative absence of ongoing consolidation processes. This absence of ongoing endogenous functional activity might rise the troughs’ sensory receptiveness to external events, provided that at least local neural processing remains possible during troughs (Destexhe et al., 2007; Vyazovskiy and Harris, 2013).

Hence, both peaks and troughs possess characteristics that might support sensory processing and learning, but which of these brains states provides optimal learning conditions is unknown. Furthermore, recent results suggest that the high background firing rate during the peak state favors synaptic down-scaling/depression rather than potentiation (Bartram et al., 2017; Yoshida and Toyoizumi, 2023). Hence, the trough rather than the peak state might be beneficial for de-novo memory formation during deep sleep.

We leveraged peaks and troughs of slow-waves for the linguistic processing and the ensuing paired-associate encoding of word pairs. Here, we applied slow wave phase-targeted, brain-state-dependent stimulation for de novo memory formation, instead of memory reactivation (Ngo and Staresina, 2022) or the modification of slow waves (Navarrete et al., 2020). To this aim, we simultaneously played pseudowords and translation words during either troughs or peaks (Trough/Peak condition) of frontal slow-waves using an electroencephalography (EEG)-based brain-state-dependent stimulation algorithm of our own devising (Ruch et al., 2022). According to Züst et al., 2019, we hypothesized that the critical process of memory formation, namely paired-associate semantic encoding, is bound to peaks because only peaks provide the necessary conditions for effective hippocampal-neocortical crosstalk. Regarding the optimal state for the words’ initial psycholinguistic analysis leading up to their relational encoding, we had no directed hypothesis. We further anticipated that associations formed through hippocampal-neocortical interactions would last for hours or days thanks to immediate hippocampal long-term potentiation and an immediate hippocampally triggered replay (Frankland et al., 2001; Goto et al., 2021; Takeuchi et al., 2014; Tsien et al., 1996). This hypothesis was significantly inspired by the finding of subliminally formed unconscious episodic memories lasting over 10 hr and increasing their influence on human decision-making over this time (Pacozzi et al., 2022).

We played 27 pairs of pseudowords (e.g. aryl) and 27 translation words (e.g. bird; nouns) in the experimental condition (EC) and pseudowords alone (e.g. egref) in the control condition (CC) during either troughs or peaks of slow-waves (Figure 1). Peak/Trough-targeting was manipulated between participants with 15 participants per group. Each group was played words from the experimental condition and the control condition during sleep within the same night. Word pairs (EC) and pseudowords (CC) were played four times in succession to increase the probability that the words pass the thalamic gate. The test of whether semantic associations were formed between pseudowords and translation words during deep sleep followed 12 hr and again 36 hr later in the waking state. This retrieval test required participants to assign earlier sleep-played pseudowords to one of three semantic categories: animals, tools, places. Each sleep-played translation word was a noun that belongs to one of these superordinate categories (counterbalanced between participants: nine animals, nine tools, nine places). As participants were in deep sleep, while the vocabulary was being played, participants could not consciously remember the word pairs at test. Therefore, we encouraged them to decide intuitively about category assignments. This task triggers an unconscious associative retrieval by cueing a memory reactivation solely by the sound of the pseudoword alone. Once the meaning of the pseudoword-associated translation word was reactivated in memory, this meaning needed to be converted to the appropriate superordinate semantic category. If the number of correct category assignments exceeded chance performance (33%), we attributed this excess to successful sleep-learning. Importantly, we provided no feedback regarding category assignments to leave assignment accuracy at the 36 hr retrieval uninfluenced by the preceding 12 hr retrieval.

Figure 1

Download asset Open asset

Thirty healthy male and female volunteers were acoustically stimulated during their slow-wave night-sleep with an average of 23.98 (SD 3.84) pairs of pseudowords and translation words. We targeted these word pairs either to slow-wave peaks or to troughs using our own slow wave phase-targeted, brain-state-dependent stimulation algorithm (TOPOSO, Ruch et al., 2022). Pseudowords and translation words were simultaneously played into the right ear and left ear, respectively (Aarons, 1990; Kimura, 1961). Their presentation lasted on average 540 ms (SEM = 0.011 ms). Word pairs’ sound onset was ~100 ms before the local maxima of the targeted slow-wave phase and largely fit into this half-wave (duration of slow-wave peaks/troughs: ~500 ms, duration of word pair presentation: 540 ms, overlap: 350 ms). Each word pair was presented four times in succession to enhance the odds of their successful processing. Adding over the four repetitions, a mean of 95.33 (SD = 15.41) word pairs was played per participant.

Targeting vocabulary to troughs provided for successful sleep-learning and long-term storage

We computed a 2x2 ANOVA with the between-subjects factor Peak- versus Trough-targeting and the within-subjects factor Encoding-Test Delay (12 hr versus 36 hr). The dependent variable was retrieval accuracy expressed as the difference between the percentages of correctly retrieved associations minus the percentage of the theoretical chance level performance (33.33 %).

Over all conditions, associative retrieval performance exceeded chance performance by 2.67% (SD = 8.11), which just failed statistical significance (F _Intercept(1,28) = 3.490, p=0.077). Retrieval performance over both encoding-test delays was significantly better when word pairs were targeted to Troughs rather than Peaks (main effect Peak versus Trough: F(1,28) = 5.237, p=0.030, d=0.865, Figure 2A). Retrieval performance did not differ significantly between the two encoding-test delays (main effect Encoding-Test Delay: F(1,28) = 0.571, p=0.456). There was no significant interaction between the factor Peak versus Trough and the factor Encoding-Test Delay (F(1,28) = 0.646, p=0.428).

Figure 2 with 4 supplements see all

Download asset Open asset

Because retrieval performance was significantly better following trough vs. peak targeted stimulation, we computed a second ANOVA for the Trough condition alone (within-subjects factor Encoding-Test Delay). We wanted to determine whether the retrieval accuracy following trough targeting was above chance level (intercept: IV = % correct answers minus mean chance performance) and whether retrieval performance differed between 12 hr and 36 hr (factor Encoding-Test Delay). This ANOVA established a significant intercept: mean retrieval performance was 5.77% above chance level (M_Trough = 39.11%, SD = 10.76; F_Intercept (1,14)=5.660, p=0.032). Although the 12 hr versus 36 hr comparison was not statistically significant (F_{Encoding-Test Delay} (1,14)=1.308, p=0.272), retrieval performance was numerically larger at 36 hours than at 12 hr (M_12hours=37.4%, SD = 9.0; M_36hours=40.7%, SD = 12.4; Figure 2BC and Figure 2—figure supplement 1). Planned contrasts against chance level revealed that retrieval performance significantly exceeded chance at 36 hr only (P_36hours=0.036, P_12hours=0.094). To better understand why retrieval performance was significantly better in the Trough vs. the Peak condition and why performance exceeded chance level only after 36 hr in the Trough condition, we analysed participants’ category assignments at the single-item level. This item-based analysis revealed that participants consistently chose the same category (animals, tools, places) for specific sleep-played pseudowords at the 12- and 36 hr retrieval test (12-to-36 hr consistency rate = 47%, chance level = 33.3%). Moreover, the consistency rate did not differ significantly between the Trough and the Peak condition (M_Trough = 47.2%, M_Peak = 47.0%, p=0.98). A descriptive analysis of participants’ category assignments suggested that the better retrieval performance in the Trough compared to the Peak condition after 36 hr (Figure 2—figure supplement 2) was due to the following differences in participants' behaviors:

Participants in the Trough condition were more likely to assign pseudowords to the correct category at 36 hr when they were already correct at 12 hr (Trough: 20% of all items at 36 hr, or 54% of all initially correctly assigned items; Peak: 14% of all items at 36 hr, or 42% of all initially correctly assigned items). (B) Participants in the Trough condition were more likely to switch assignments and use the correct category at 36 hr, if they had initially assigned them to an incorrect category at 12 hr (Trough: 20% of all items at 36 hr, or 32% of the initially incorrectly assigned items; Peak: 18% of all items at 36 hr, or 27% of the initially incorrectly assigned items). Hence, in the Trough condition, pseudowords that were correctly assigned to the object category after 12 hr remained correct after 36 hr, while new correct assignments for initially incorrectly classified pseudowords were added at 36 hr. This is additional evidence that the above-chance retrieval performance at 36 hr is no fluke but originates from sleep-learning.

Because word pairs were exclusively played during slow-wave sleep, which is accompanied by a loss of consciousness (Dement and Kleitman, 1957; Massimini et al., 2012), encoding and retrieval must have been unconscious. Nevertheless, we assessed whether encoding and retrieval were subjectively unconscious by asking participants to indicate whether they had a “feeling of having heard” (FoHH) the presented pseudoword during sleep (rating from 1: not heard to 4: heard). This wording of the question should set a liberal criterion for reporting having heard a pseudoword. We wanted to obtain information of any potential residual awareness for sleep-played words. Participants’ feeling-of-having-heard (FoHH) responses in the Trough condition did not differ significantly between previously sleep-played pseudowords and new pseudowords that had not been played during sleep (see Figure 2—figure supplement 3; ANOVA factor ‘sleep-played’: F(1,14) = 0.45, p=0.51, M_{sleep-played_12hours}=2.41, SD = 0.36, M_{new_12hours}=2.42, SD = 0.353, M_{sleep-played_36hours}=2.53, SD = 0.53, M_{new_36hours}=2.56, SD = 0.618). Furthermore, differences in FoHH responses remained non-significant even if we compared only the correctly categorized pseudowords to the never heard, new pseudowords (F(1,14) = 0.08, p=0.78). We also found no significant difference between the FoHH for correct versus incorrect category assignments (ANOVA factor ‘accuracy’: F(1,14) = 0.09, p=0.77, M_{12hours_correct}=2.37, SD = 0.54, M_{12hours_incorrect}=2.42, SD = 0.39, M_{36hours_correct}=2.57, SD = 0.62, M_{36hours_incorrect}=2.49, SD = 0.55) and no difference between EC and CC trials (ANOVA factor “Association”: F(1,14) = 0.09, p=0.77, M_{12hours_correct}=2.37, SD = 0.54, M_{12hours_incorrect}=2.42, SD = 0.39, M_{36hours_correct}=2.57, SD = 0.62, M_{36hours_incorrect}=2.49, SD = 0.55). The same was true for the Peak condition, where we did not find significant differences in the FoHH reports (Figure 2—figure supplement 2; sleep-played vs new: F(1,14) = 1.027, p=0.33; correct vs incorrect EC trials: F(1,14) = 0.04, p=0.85, EC vs CC: F(1,14) = 0.04, p=0.85). These findings strongly suggest that participants could not consciously recognize sleep-played pseudowords.

Furthermore, we explored whether participants’ confidence ratings revealed an unconscious reminiscence of sleep-played word pairs for correctly versus incorrectly categorized items at retrieval. Hence, we included the confidence scale as a subtle measure of memory besides the cruder accuracy measure. At retrieval testing, participants rated their confidence for each assignment of a pseudoword (sleep-played and new) to a superordinate word category (animal, tool, place). Confidence ratings did not differ signifcantly for sleep-played (EC and CC) compared to new pseudowords in the Trough condition, were sleep-learning was successful ANOVA factor ‘sleep-played’: F(1,14) = 0.47, p=0.5; M_{sleep-played_12hours}=2.17, SD = 0.38, M_{new_12hours}=2.19, SD = 0.376, M_{sleep-played_36hours}=2.26, SD = 0.426, M_{new_36hours}=2.26, SD = 0.426. Furthermore, confidence ratings for correct versus incorrect category assignments in the experimental condition differed neither in the Trough condition (Figure 2—figure supplement 4), ANOVA factor ‘Accuracy’: F(1,14) = 2.36, p=0.15; M_{12hours_correct}=2.24, SD = 0.42, M_{12hours_incorrect}=2.16, SD = 0.35, M_{36hours_correct}=2.33, SD = 0.44, M_{36hours_incorrect}=2.28, SD = 0.4 nor the Peak condition (Figure 2—figure supplement 4), ANOVA factor Accuracy: F(1,14) = 0.48, p=0.50; M_{12hours_correct}=2.19, SD = 0.51, M_{12hours_incorrect}=2.28, SD = 0.54, M_{36hours_correct}=2.33, SD = 0.58, M_{36hours_incorrect}=2.36, SD = 0.51.

Word-evoked potentials differed initially between the peak and trough condition and then aligned between conditions from 700 ms to 2 s following word onset

The EEG data were recorded in the sleeping participants to determine differences in the processing of the played words between conditions. We analysed the electrophysiological responses to word pairs in the Trough versus the Peak condition locking the electrophysiological response to the onset of word pairs. The scalp topographies of the event-related potentials (ERPs) recorded in Troughs versus Peaks were almost diametrically opposed frontal cluster [–1.42 s to 0.48]: cluster-level Monte Carlo P<0.002; two occipital clusters [–1.38 s to-0.09s] and [0.07s to 0.71s]: cluster-level Monte Carlo for both clusters p<0.002; Figure 3A. This difference attests to the successful targeting of peaks versus troughs by the used brain-state-dependent stimulation algorithm. For further manipulation checks of the stimulation algorithm see methods. The word-evoked response in the Peak condition resembled the response to white noise clicks in slow-wave entrainment studies (Andrillon and Kouider, 2020; Cox et al., 2014b; Ngo et al., 2013). An early frontal positivity at 200 ms was followed by a large negative component at 550 ms, which in turn was followed by a late positivity at 900 ms (Figure 3). These components have been described as the generic response of the sleeping brain to any kind of sensory stimulation (Andrillon and Kouider, 2020; Cox et al., 2014b; Laurino et al., 2014; Laurino et al., 2019; Riedner et al., 2011). Halász, 2016 suggested that these components resemble K-complexes that reflect the brain’s response to maintaining sleep in the presence of sensory stimuli.

Figure 3 with 1 supplement see all

Download asset Open asset

However, when we targeted words to troughs, the played words evoked two positive frontal components. The first positive component appeared at 500 ms and the second at one second following word onset. Hence, the second component corresponded to the entrained late positivity in the Peak condition. In short, targeting words to peaks evoked a generic response that presumably blocked the encoding of sleep-played word pairs in the Peak condition. This is underlined by recent findings of Niknazar et al., 2023, who demonstrated that the large negative component of the evoked K-complex inhibits long-range communication in the brain. Targeting words to troughs shifted the EEG and inhibited this large negative component. The ERP differences between the Trough and the Peak condition vanished at 700 ms. Between 700 ms and 2 s following word onset the EEG was comparable between the two conditions.

A brain-wide voltage distribution that is typically associated with a frontal trough promoted word processing

The visual inspection of the difference in the topographical voltage distribution at word onset between subsequently (36 hr) correctly versus incorrectly assigned pseudowords (Figure 3C) suggested that sleep-learning in the Trough condition was best, if the brain-wide voltage distribution corresponded to the typical trough state as quantified with a template of a slow-wave trough. Note that we targeted slow-wave troughs/peaks by correlating the online measured EEG with a template map (Ruch et al., 2022). The template map is based on pre-recorded EEG data (Züst et al., 2019) and represents the average voltage distribution of thousands of peaks/troughs (peak template, trough template). Accordingly, we consider the trough template a prototypical voltage distribution of endogenously generated troughs. Descriptively, the measured voltage map underlying later (at 36 hr) correctly versus incorrectly assigned pseudowords corresponded to the template map of a typical trough (Figure 3C). Therefore, we tried to find out, which trough aspect had promoted sleep-learning. We examined four trough aspects measured at word onset: (A) prototypicality reflecting the correspondence (i.e. correlation) of the trough voltage distribution at word onset with the prototypical voltage distribution of a trough, that is the trough template. A high prototypicality suggests that the trough originated in the same neocortical network that generates the majority of endogenous slow waves (Michel and Koenig, 2018) (B) global field power (GFP) reflecting the synchronisation within the measured trough voltage map. A high GFP indicates strong coherence and stability of neuronal activity within the cortical network that generates the trough (Khanna et al., 2015) (C) inter-trial phase coherence (ITC) reflecting the temporal coherence of Trough-targeting across the four presentations of a specific word pair; (D) time difference reflecting the time difference between the actual acoustic stimulation and the measured trough maximum (Figure 4; for a more detailed description see method section). Prototypicality differentiated significantly between correctly (higher prototypicality) versus incorrectly assigned pseudowords at 36 hr (p=0.005, FDR corrected: q _{Benjamini-Hochberg} = 0.0125; Figure 4A). This comparison remained significant when we compared the area under the prototypicality curve (AUC; p<0.019,–150 ms to 350 ms, FDR corrected: q _{Benjamini-Hochberg} = 0. 02). AUC was computed to ensure that not only the word onset, but the entire trough period was more similar to the trough template for subsequently correctly versus incorrectly assigned pseudowords. The other trough aspects did not differentiate significantly between subsequently correctly versus incorrectly assigned pseudowords (all p>0.13). The same Trough-related analyses for correctly versus incorrectly assigned pseudowords at 12 hr are presented in the supplement (Figure 4—figure supplement 1). These analyses were not performed for the Peak condition because retrieval performance was not better than chance in the Peak condition.

Figure 4 with 1 supplement see all

Download asset Open asset

Enhanced theta and fast spindle power promoted sleep-learning

We aimed to isolate neural markers of semantic-associative encoding from word onset to 2.5 s thereafter. To this aim, we compared EEG responses to the presentation of word pairs (EC) to responses to presentations of pseudowords (CC) during sleep. Pseudowords were presented alone (no added translation word) into both ears in the control condition and were played in alternating order with word pairs (EC, CC, EC, CC, EC,...). Trough-targeted word pairs versus pseudowords enhanced the theta power at 500 ms following word onset (0.2 s to 0.7 s, cluster-level Monte Carlo p=0.018, no significant cluster for Peak-targeted word pairs, p>0.1, Figure 5A). Trough-targeted word pairs versus pseudowords enhanced the fast spindle power at 1 s following word onset (0.8 s-1.2 s, cluster-level Monte Carlo p=0.012, no significant cluster for Peak-targeted word pairs, p>0.4, Figure 5B). The average theta enhancement in the significant cluster correlated significantly with retrieval performance (theta: R=0.57, p=0.027, Figure 5A), while the spindle enhancement did not correlate with retrieval performance (spindle: R=0.034, p=0.9, Figure 5B). The comparison between correctly and incorrectly assigned pseudowords did not yield any significant cluster in the theta- or spindle-band power following word onset (window: 0–2.5 s, cluster-level Monte Carlo p_Theta >0.6; p_Spindle >0.1).

Figure 5

Download asset Open asset

Increased neural complexity in the trough condition mirrors word processing

To further identify potential neural markers of semantic-associative encoding during sleep, we examined how Peak- vs. Trough-targeted stimulation altered the post-stimulus neural complexity of the EEG signal. Measures of neural complexity provide non-linear estimates of the variability and ‘randomness’ of the EEG signal and indicate the amount of information available in the signal at each channel. Neural complexity is indicative of both the brain’s capacity to process sensory information (Waschke et al., 2017; Waschke et al., 2019) and the extent of ongoing cognitive activity (Höhn et al., 2023; Parbat and Chakraborty, 2021). Furthermore, levels of neural complexity closely correspond to the depth of sleep (Casali et al., 2013; Höhn et al., 2023; Ma et al., 2018; Türker et al., 2023), with the lowest levels typically observed during SWS. Within sleep, periods of elevated neural complexity are associated with a heightened propensity to process verbal information. Andrillon and Kouider, 2016 reported larger lateralized readiness potentials indicating correct semantic categorization of words during sleep when words were played during states of elevated neural complexity. Türker et al., 2023 further observed that sleeping participants were more likely to produce accurate facial responses to words vs. pseudowords when stimulus presentations occurred during states of high complexity. Importantly, auditory stimulation during sleep tends to temporarily reduce post-stimulus neural complexity (Alnes et al., 2024; Andrillon et al., 2016), probably reflecting an inhibitory, sleep-protective brain response to stimulation.

Here, we explored whether word presentations would have distinct impacts on post-stimulus neural complexity when stimulation was targeted to the Trough, where sleep-learning occurred, vs. to the Peak, where no sleep-learning occurred. To this aim, we calculated the Higuchi Fractal Dimension (HFD) of the EEG signal in the time domain at each channel separately for a pre- (−2 to –0.5 s) and a post-stimulus (0.5–2 s) time window. HFD is one of many measures of neural complexity (Lau et al., 2022). We analyzed HFD for a late post-stimulus window ranging from 0.5 s up to 2 s because we assumed that neural complexity for this time-window would not be biased by the differences that are due to the targeted state (Peak/Trough) but would mirror stimulus-induced neural activity. Note that for this time-window, the word-evoked EEG potentials no longer showed differences between Peak versus the Trough condition.

Baseline corrected EEG complexity following word offset (from 500 ms to 2 s, when the EEG was aligned between the two conditions) was higher in the Trough than the Peak condition (cluster-level Monte Carlo p=0.002; Figure 6). However, we observed that presentation of word pairs reduced post-stimulus complexity compared to the pre-stimulus baseline in the Peak but not the Trough condition (post-stimulus complexity compared to baseline; Peak condition: cluster-level Monte Carlo p=0.002; Trough condition: cluster-level Monte Carlo p>0.1). The Peak-specific reduction in complexity might reflect the brains’ standard inhibitory response to sensory stimulation during sleep (Andrillon et al., 2016; Höhn et al., 2023). Importantly, this inhibitory response was absent following Trough-targeted stimulation. In fact, post-stimulus complexity was descriptively (see Figure 6) but not significantly increased compared to baseline in the Trough condition. This absence of a post-stimulus dip in neural complexity could reflect learning-associated neural activity in the Trough condition (Lau et al., 2022; Parbat and Chakraborty, 2021). However, baseline-corrected HFD did not differ significantly between word pairs that were later (at 12 hr and at 36 hr) correctly versus incorrectly assigned to categories (correct vs incorrect Peak and Trough: all cluster-level Monte Carlo p>0.49). Nor did baseline-corrected HFD significantly differ between the EC and the CC (EC vs CC Peak and Trough: all cluster-level Monte Carlo p>0.16). Finally, we correlated HFD values with the participants’ accuracy of assigning pseudowords to the three categories at the 36 hr retrieval of the Trough condition. This correlation yielded an insignificant result (R=0.25, p=0.36). In conclusion, word processing during sleep appears to increase neural complexity in troughs.

Figure 6

Download asset Open asset

Natural slow-wave sleep is a state in which our conscious awareness of the surrounding world is drastically reduced. For our protection, we still need to process external events in order to decide whether an event is threatening and requires an immediate awakening or whether an event is relevant enough to warrant storage for later consideration. There is evidence indicating that we can process and store new information during slow-wave sleep (Ai et al., 2018; Andrillon and Kouider, 2016; Arzi et al., 2012; Arzi et al., 2014; Koroma et al., 2022; Ruch et al., 2014; Ruch and Henke, 2020; Züst et al., 2019). However, whether the most sophisticated form of learning - episodic learning - is possible during deep sleep, is contentious. This experiment reveals that troughs and peaks of slow-waves contribute mutually to successful episodic learning during sleep. Vocabulary played during troughs of slow-waves passed the thalamic gate and was processed perceptually and conceptually, which raised neural complexity. The following peak harbouring an increase in theta power provided the conditions for semantic-associative encoding of pseudowords and translation words. An immediately following second peak accompanied by a rise in fast spindle power around 1 s following word onset may have aided the immediate consolidation of the formed associations. This sequence of processing steps prepared the ground for later stages of memory consolidation that resulted in a successful retrieval of associations after 36 hr.

Was the sleep-presented vocabulary stored and retrieved through the episodic memory system? Traditionally, episodic memory and hippocampal processing were considered processes of declarative or explicit memory and were thought to require conscious awareness of the learning material (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire and Dede, 2015; Tulving, 2002). Yet, increasing counterevidence indicates that hippocampal-assisted episodic memory formation may proceed with and without conscious awareness of the learning material (Duss et al., 2014; Henke et al., 2003; Henke et al., 2013; Wuethrich et al., 2018; Züst et al., 2015). The conscious and unconscious rapid encoding and flexible retrieval of novel relational memories was found to recruit the same or similar neural networks including the hippocampus (Henke et al., 2003; Schneider et al., 2021). This suggests that conscious and unconscious episodic memories are processed by the same memory system. In the current study, we directed word processing to the episodic memory system by: (a) exacting associative encoding, (b) enforcing a speedy encoding process, and (c) compelling a delayed cued associative retrieval that requires a flexible representation of the sleep-formed memories. These task-enforced processing characteristics are key features of episodic memory with no other memory system disposing of these computational abilities (Cohen and Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014). Only the hippocampus can form arbitrary word-word associations and rapidly store the associations in a flexible format. The neocortex forms new arbitrary associations only slowly over dozens of learning trials and provides for fused (rather than flexible) word-word representations in memory (Cohen and Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014). Although no brain imaging was performed in the current study, evidence that relational learning during sleep is mediated by the hippocampus was provided by findings of Züst et al., 2019. Using a very similar memory task, these authors observed that successful retrieval of sleep-played associations was associated with increased hippocampal activity. Mere priming is unlikely to account for the observed learning effects because priming is not compositional and flexible enough to provide the cued retrieval of word meaning that is required by the retrieval task. Note that at retrieval, only the pseudoword was presented. Hence, participants had to infer the corresponding category by first retrieving the sleep-played translation word and then classifying the translation word’s semantics to the superordinate category (tool, animal, place). We reason that memory representations formed by priming are not compositional and flexible enough to promote the required cued retrieval of word meaning. In summary, the task requirements called upon the episodic memory system and previous evidence (Züst et al., 2019) of hippocampal activation during correctly versus incorrectly categorized items at wake-retrieval testing and the between-subjects correlation of this contrast with retrieval performance suggest that the sleep-formed memories were mediated by the episodic memory system.

While consciously acquired information in the episodic memory system tends to decay rapidly during the 24 hr following learning (Ebbinghaus, 2013; Hardt et al., 2013; Murre and Dros, 2015), we did not observe a deterioration in retrieval performance from 12 hr to 36 hr. This speaks to the longevity and robustness of sleep-formed memories. Because participants enjoyed a night of undisturbed sleep at home between the 12 hr and the 36 hr retrieval, the newly formed associations may have undergone additional sleep-assisted memory consolidation. One might argue that the retrieval task after 12 hr led to a conscious re-encoding of sleep-formed memories. However, the sleep-played translation words were not presented at test, and participants received no feedback regarding the accuracy of their responses during the retrieval task. Therefore, it seems unlikely that the successful delayed retrieval after 36 hr was due to conscious re-learning during the first retrieval. Instead, the reactivation of sleep-formed memories during the first retrieval task might have tagged these memories for further (re-) consolidation during the ensuing night (Rabinovich Orlandi et al., 2020).

In fact, sleep-dependent memory consolidation supports weak memories more than strong memories (Denis et al., 2020; Drosopoulos et al., 2007; Petzka et al., 2021; Schechtman et al., 2021; Schneider et al., 2021; Tucker and Fishbein, 2008). Hence, the presumably weak sleep-formed memories in this study may have profited especially from sleep-dependent memory consolidation. We have recently found that unconscious memories formed from subliminal (consciously invisible) cartoon clips (instead of memory formation during sleep) benefited from sleep-dependent memory consolidation, such that they were retrievable after hours (Pacozzi et al., 2022).

However, before memory consolidation can kick in, the sleep-played vocabulary needs to pass thalamic gating and undergo neocortical processing (Gent et al., 2018; McCormick and Bal, 1994). How was this possible? We played the vocabulary either during peaks or during troughs of slow-waves. The retrieval performance indicated that the vocabulary was processed only in the Trough condition. When troughs were pronounced, encoding and retrieval were best, and the EEG exhibited more neural complexity reflecting cognitive processing (Parbat and Chakraborty, 2021). We offer two speculative explanations as to why the processing of the played vocabulary was possible during troughs rather than peaks of slow-waves. First, the peaks may have been ‘occupied’ by the consolidation of previously wake-formed memories. Wake-formed memories appear to be preferentially reactivated during peaks, when hippocampus-neocortical interactions take place, which are accompanied by a rise in fast spindle power (Göldi et al., 2019; Mölle et al., 2002; Mölle et al., 2011; Muehlroth et al., 2019; Staresina et al., 2015). Even a causal role of endogenous spindles is suggested for memory consolidation (Antony et al., 2019; Chen and Wilson, 2017; Latchoumane et al., 2017; Maingret et al., 2016; Mölle et al., 2011; Staresina et al., 2015), particularly if the spindles are nested into peaks (Dang-Vu et al., 2011; Schabus et al., 2012). Troughs, on the other hand, may not be occupied by ongoing consolidation processes and might therefore be receptive to sounds. Second, targeting vocabulary to troughs bypasses the natural sleep preserving mechanisms at play during peaks of slow-waves. Any sound evokes a systematic endogenous EEG response, often described as a K-complex (Andrillon and Kouider, 2020; Cox et al., 2014b; Halász, 2005; Halász, 2016; Mölle et al., 2009; Ngo et al., 2013; Schabus et al., 2012). Such a generic response represents a cortical sensory gate that protects sleep and the consolidation of wake-formed memories (Andrillon and Kouider, 2020; Halász, 2005). The prominent frontal negativity at 500 ms in particular reflects a breakdown of the cortico-thalamic communication (Niknazar et al., 2023). This breakdown of communication appears to leave sensory processing intact in primary sensory cortices but mitigates responses at higher cortical levels (Schabus et al., 2012). When words were played into peaks, we recorded a K-complex like, generic response from frontal electrodes, while when words were played into troughs, the vocabulary evoked a phase reset of the slow-waves that differed sharply from K-complexes. To conclude, we assume that ongoing consolidation processes and sleep-protective responses generated during peaks of slow-waves inhibited the psycholinguistic processing of the simultaneously played pseudowords and translation words, while these words were psycholinguistically processed during troughs.

Because the cued associative retrieval was above chance following sleep-learning in the Trough condition, the translation words must have been understood and their meaning must have been bound to the sound of the pseudoword during slow-wave sleep. We assume that the psycholinguistic processing of the translation word lasted up to 500 ms, which was also the duration of the word utterance and the duration of the ongoing trough. The brain starts extracting the meaning of a word already while it is being uttered. The phonological, syntactic, lexical, and semantic word analyses occur in parallel during the 500 ms after word onset (Brodbeck and Simon, 2022; Hagoort, 2008; Hickok and Poeppel, 2007; Pulvermüller et al., 2009; Skeide and Friederici, 2016). ERP and MEG recordings during (awake) spoken word processing revealed that phonological word forms are processed 20–50 ms following word onset in the auditory cortex. Phonological word forms are categorized at the morphosyntactic level at 40–90 ms. Lexical–semantic word analyses occur at 50–80 ms in the left anterior superior temporal cortex, where lexical items associated with the phonological word forms are retrieved at 110–170 ms. These same temporal areas funnel lexical information to Brodmann areas 45 and 47, where semantic relations between words are analysed between 200–400 ms (Skeide and Friederici, 2016). The timing of external stimulus processing during slow-wave sleep appears to be similar to the waking state (Laurino et al., 2019; Ruby et al., 2008; Sabri et al., 2000). Awake learning experiments in humans indicated that the hippocampus ramps up its encoding machinery at 300–500 ms after word onset (Long et al., 2014; Quiroga et al., 2005; Staresina and Wimber, 2019). The perceived associations between the meaning of translation words and the sound of pseudowords need to be encoded in the hippocampus (Sakaguchi and Hayashi, 2012; Tonegawa et al., 2018) that induces long-term potentiation and increases the numbers of dendritic spines to strengthen the connectivity between neurons (Bliss and Collingridge, 1993; Engert and Bonhoeffer, 1999; Hebb, 1949). This process of memory formation requires the collaboration of the hippocampus with neocortical networks (Buzsáki and Tingley, 2018; Schreiner and Staudigl, 2020). Hippocampal associative binding depends on theta activity (and theta-gamma coupling) in the hippocampus (Axmacher et al., 2006; Fernández-Ruiz et al., 2017; Kahana et al., 2001; Mormann et al., 2005; Osipova et al., 2006). In the current study, theta power rose at 500 ms following word onset. This might reflect the moment when semantic pseudoword-word associations underwent storage in the hippocampus. The magnitude of the increase in theta power correlated, between-subjects, with retrieval performance at 36 hr. One interpretation is that theta activity supported the association of pseudowords with translation words. Importantly, at 500 ms following word onset, a slow-wave peak followed the stimulated trough, which provided for the necessary neuronal excitability and an effective hippocampal-neocortical communication (Andrillon and Kouider, 2020; Destexhe et al., 2007). The frontally recorded peaks coincided with enhanced theta power, and both may have contributed to a hippocampal encoding of new associations. Initially labile hippocampal-neocortical memory traces are usually replayed to secure storage by consolidation (Goto et al., 2021; Rasch and Born, 2013). Fast sleep spindles support this process (Chen and Wilson, 2017; Latchoumane et al., 2017; Maingret et al., 2016; Petzka et al., 2022; Schreiner and Staudigl, 2020; Staresina et al., 2015) because they constitute a ‘shuttle’ of hippocampal representations to neocortical sites (Antony et al., 2019; Cairney et al., 2018). We assume that fast sleep spindles supported the immediate replay of the sleep-formed associations at one second following word onset (similar to Abdellahi et al., 2023). This was the time when a second peak occurred and when fast spindle power ramped up in the experimental condition but not in the control condition, where no associative encoding took place because pseudowords alone were played during troughs. Both the increase in theta power at 500 ms and the increase in fast spindle power at one second were accompanied by an enhancement of neuronal activity. These neuronal events were absent if the vocabulary was played during peaks. Hence, critical events that foster learning and storage were observed in the Trough condition, where vocabulary was stored long-term, but not in the Peak condition, where no sleep-learning was observed.

According to the sequence of events described above, we speculate that learning during sleep may proceed as follows: (1) A frontal slow-wave trough allows for spoken language comprehension by providing a 500 ms time window for external stimulus processing (no sleep-protective K-complexes). (2) 500 ms following word onset, paired-associative encoding takes place during the next slow-wave peak, coincident with enhanced theta power, both providing for the necessary hippocampal-neocortical crosstalk. (3) 1000 ms following word onset, fast spindles assist the immediate replay of the encoded information through hippocampal-neocortical interactions. This model needs to be tested in future sleep-learning experiments using magnetoencephalography or the simultaneous recording of functional magnetic resonance imaging data and EEG data for a precise temporal mapping of the processing stages and the simultaneous measurement of associated activations in brain regions.

While the current experiment is the first to target acoustic stimuli to slow-wave peaks and troughs for episodic learning during sleep, we have earlier performed a similar experiment playing vocabulary at fixed intervals (open-loop) during slow-wave sleep (Züst et al., 2019). In that study, the pseudoword and translation word of a pair were played in succession rather than simultaneously. This changes the timing and the sequence of processing steps. With words played in succession (Züst et al., 2019), the paired-associative encoding in memory cannot happen before the second word of a pair is being played. In the current study, the two words of a pair were simultaneously presented and the paired-associative encoding in memory occurred around 500 ms following the onset of both words. Because hippocampal paired-associative encoding requires broadly activated neurons for an effective hippocampal-neocortical connectivity (Cox et al., 2014b; Destexhe et al., 2007; Schabus et al., 2012; Sirota and Buzsáki, 2005), a slow-wave peak at the time of associative learning seems mandatory. This time point is during the play of the second word of a pair, when word presentations are sequential, and the time point is around 500 ms following word onset, when word presentations are simultaneous. The critical paired-associate encoding process required a frontally recorded slow-wave peak in both the Züst et al., 2019 study and the current study. It should be noted that the comparison of the Züst et al., 2019 study with the current study is difficult because Züst et al., 2019 entrained the sleep-EEG by rhythmically playing words inter-stimulus-interval 1053 ms; Ngo et al., 2013, which changed the natural course of the sleep EEG. Therefore, only the current study permits conclusions regarding the role of endogenously generated slow-wave peaks and troughs in mediating sleep-learning.

Because slow-wave sleep is a state of unconsciousness, a sophisticated form of learning like rapid vocabulary acquisition is not expected given the still prevailing dogma of episodic learning depending on conscious awareness (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire and Dede, 2015; Tulving, 2002). Rapid vocabulary acquisition during deep sleep adds to evidence of successful episodic learning from subliminal (consciously invisible) words presented in the waking state (Duss et al., 2014; Henke et al., 2013; Reber et al., 2012). These findings support the claim that episodic memory formation may proceed without conscious awareness (Dew and Cabeza, 2011; Hannula and Greene, 2012; Henke, 2010). The sleep-formed memories did not significantly subside but (non-significantly) strengthened over 36 hr to the point where they influenced deliberative decision-making. Evidence of language processing and rapid verbal associative learning during deep sleep challenges the views that slow-wave sleep is a state of general synaptic depotentiation (Rasch and Born, 2013; Tononi and Cirelli, 2006), that we have a strict gating of sensory information at the thalamus, and that the direction of information flow during sleep is strictly from hippocampus to neocortex (Eban-Rothschild et al., 2016; Rasch and Born, 2013).

Finally, we would like to acknowledge certain limitations inherent to our research design, methodology, and interpretation. First, our interpretation of how learning may have occurred during sleep is speculative and hypothetic at this point. The delineated sequence of word processing steps during slow-wave phases should inspire future research aimed at testing these hypotheses. Second, sound duration was a bit longer than the average duration of the targeted slow-wave phases (500 ms) and therefore extended into the following slow-wave phase. This lowers the specificity of the contrast between the slow-wave phases. Third, there is a discrepancy in auditory complexity and information density between the experimental and control conditions (pseudoword-word pairs vs. one single pseudoword). These differences can induce processing and EEG differences between conditions other than associative learning. Fourth, carry-over effects from the first to the second retrieval test are possible and may have produced the slight increase in retrieval accuracy from the first to the second retrieval.

Future studies might look into practical applications as well as the ethical problems (Stickgold et al., 2021) of acoustic stimulations during sleep. While the acquisition of new vocabulary is certainly best during waking, other forms of sleep-learning might yield better results, when learning is unconscious versus conscious. Sleep-learning might be exploited by entities with nefarious intentions (Stickgold et al., 2021). However, sleep-learning might also be put to good use, such as in psychotherapy. Because sleep-learned messages are processed and stored unconsciously, they circumvent conscious defence mechanisms (Arzi et al., 2014; Levy et al., 2014). This information might dispose the sleeper more readily towards behavioural change than traditional psychotherapy conducted in the waking state. Finally, sleep-learning may allow for the reactivation and the subsequent modification and reframing of unwanted memories without the need to consciously re-experience the stressful memories (He et al., 2015; Taschereau-Dumouchel et al., 2018a; Taschereau-Dumouchel et al., 2018b; Zhu et al., 2022).

Raw files of the behavioral and neuronal data are available at the Open Science Framework (OSF) repository (https://osf.io/mafw6/). Code for the closed-loop targeting of slow-wave phases can be found in Ruch et al. (2022) or at the OSF repository (https://osf.io/ecvq8/). Further requests should be directed to and will be fulfilled by the corresponding author Flavio J. Schmidig.

1. 1. Schmidig F
   2. Ruch S
   3. Henke K

   (2024) Open Science Framework

   ID mafw6. Episodic long-term memory formation during slow-wave sleep.

   https://osf.io/mafw6/

1. 1. Ruch S
   2. Schmidig F
   3. Knüsel L
   4. Henke K

   (2022) Open Science Framework

   ID ecvq8. Closed-loop modulation of local slow oscillations in human NREM sleep.

   https://osf.io/ecvq8

1. 1. Aarons L

   (1990) The bilingual‐dichotic method for learning a foreign‐language vocabulary

   Applied Cognitive Psychology 4:383–392.

   https://doi.org/10.1002/acp.2350040504

   • Google Scholar
2. 1. Abdellahi MEA
   2. Koopman ACM
   3. Treder MS
   4. Lewis PA

   (2023) Targeting targeted memory reactivation: characteristics of cued reactivation in sleep

   NeuroImage 266:119820.

   https://doi.org/10.1016/j.neuroimage.2022.119820

   • PubMed
   • Google Scholar
3. 1. Ai S
   2. Yin Y
   3. Chen Y
   4. Wang C
   5. Sun Y
   6. Tang X
   7. Lu L
   8. Zhu L
   9. Shi J

   (2018) Promoting subjective preferences in simple economic choices during nap

   eLife 7:e40583.

   https://doi.org/10.7554/eLife.40583

   • PubMed
   • Google Scholar
4. 1. Alnes SL
   2. Bächlin LZM
   3. Schindler K
   4. Tzovara A

   (2024) Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

   The European Journal of Neuroscience 59:822–841.

   https://doi.org/10.1111/ejn.16203

   • PubMed
   • Google Scholar
5. 1. Ameen MS
   2. Heib DPJ
   3. Blume C
   4. Schabus M

   (2022) The brain selectively tunes to unfamiliar voices during sleep

   The Journal of Neuroscience 42:1791–1803.

   https://doi.org/10.1523/JNEUROSCI.2524-20.2021

   • PubMed
   • Google Scholar
6. 1. Andrillon T
   2. Kouider S

   (2016) Implicit memory for words heard during sleep

   Neuroscience of Consciousness 2016:iw014.

   https://doi.org/10.1093/nc/niw014

   • PubMed
   • Google Scholar
7. 1. Andrillon T
   2. Poulsen AT
   3. Hansen LK
   4. Léger D
   5. Kouider S

   (2016) Neural markers of responsiveness to the environment in human sleep

   The Journal of Neuroscience 36:6583–6596.

   https://doi.org/10.1523/JNEUROSCI.0902-16.2016

   • PubMed
   • Google Scholar
8. 1. Andrillon T
   2. Léger D
   3. Kouider S

   (2017) Formation and suppression of acoustic memories during human sleep

   Nature Communications 8:179.

   https://doi.org/10.1038/s41467-017-00071-z

   • PubMed
   • Google Scholar
9. 1. Andrillon T
   2. Kouider S

   (2020) The vigilant sleeper: neural mechanisms of sensory (de)coupling during sleep

   Current Opinion in Physiology 15:47–59.

   https://doi.org/10.1016/j.cophys.2019.12.002

   • Google Scholar
10. 1. Antony JW
    2. Schönauer M
    3. Staresina BP
    4. Cairney SA

    (2019) Sleep spindles and memory reprocessing

    Trends in Neurosciences 42:1–3.

    https://doi.org/10.1016/j.tins.2018.09.012

    • PubMed
    • Google Scholar
11. 1. Arzi A
    2. Shedlesky L
    3. Ben-Shaul M
    4. Nasser K
    5. Oksenberg A
    6. Hairston IS
    7. Sobel N

    (2012) Humans can learn new information during sleep

    Nature Neuroscience 15:1460–1465.

    https://doi.org/10.1038/nn.3193

    • PubMed
    • Google Scholar
12. 1. Arzi A
    2. Holtzman Y
    3. Samnon P
    4. Eshel N
    5. Harel E
    6. Sobel N

    (2014) Olfactory aversive conditioning during sleep reduces cigarette-smoking behavior

    The Journal of Neuroscience 34:15382–15393.

    https://doi.org/10.1523/JNEUROSCI.2291-14.2014

    • PubMed
    • Google Scholar
13. 1. Ataei S
    2. Simo E
    3. Bergers M
    4. Schoch SF
    5. Axmacher N
    6. Dresler M

    (2023) Learning during sleep in humans: a historical review

    Sleep Medicine Reviews 72:101852.

    https://doi.org/10.1016/j.smrv.2023.101852

    • PubMed
    • Google Scholar
14. 1. Axmacher N
    2. Mormann F
    3. Fernández G
    4. Elger CE
    5. Fell J

    (2006) Memory formation by neuronal synchronization

    Brain Research Reviews 52:170–182.

    https://doi.org/10.1016/j.brainresrev.2006.01.007

    • PubMed
    • Google Scholar
15. 1. Bartram J
    2. Kahn MC
    3. Tuohy S
    4. Paulsen O
    5. Wilson T
    6. Mann EO

    (2017) Cortical Up states induce the selective weakening of subthreshold synaptic inputs

    Nature Communications 8:665.

    https://doi.org/10.1038/s41467-017-00748-5

    • PubMed
    • Google Scholar
16. 1. Bastuji H
    2. García-Larrea L

    (1999) Evoked potentials as a tool for the investigation of human sleep

    Sleep Medicine Reviews 3:23–45.

    https://doi.org/10.1016/s1087-0792(99)90012-6

    • PubMed
    • Google Scholar
17. 1. Berens P

    (2009) Circstat: a matlab toolbox for circular statistics

    Journal of Statistical Software 31:1–21.

    https://doi.org/10.18637/jss.v031.i10

    • Google Scholar
18. Book

    1. Berry RB
    2. Brooks R
    3. Gamaldo C
    4. Harding SM
    5. Lloyd RM
    6. Marcus CL
    7. Vaughn BV

    (2015)

    AASM Scoring Manual Version 2.2

    American Academy of Sleep Medicine.

    • Google Scholar
19. 1. Bliss TVP
    2. Collingridge GL

    (1993) A synaptic model of memory: long-term potentiation in the hippocampus

    Nature 361:31–39.

    https://doi.org/10.1038/361031a0

    • PubMed
    • Google Scholar
20. 1. Blume C
    2. Del Giudice R
    3. Lechinger J
    4. Wislowska M
    5. Heib DPJ
    6. Hoedlmoser K
    7. Schabus M

    (2017) Preferential processing of emotionally and self-relevant stimuli persists in unconscious N2 sleep

    Brain and Language 167:72–82.

    https://doi.org/10.1016/j.bandl.2016.02.004

    • PubMed
    • Google Scholar
21. 1. Blume C
    2. Del Giudice R
    3. Wislowska M
    4. Heib DPJ
    5. Schabus M

    (2018) Standing sentinel during human sleep: continued evaluation of environmental stimuli in the absence of consciousness

    NeuroImage 178:638–648.

    https://doi.org/10.1016/j.neuroimage.2018.05.056

    • PubMed
    • Google Scholar
22. 1. Brainard DH

    (1997)

    The psychophysics toolbox

    Spatial Vision 10:433–436.

    • PubMed
    • Google Scholar
23. 1. Brodbeck C
    2. Simon JZ

    (2022) Cortical tracking of voice pitch in the presence of multiple speakers depends on selective attention

    Frontiers in Neuroscience 16:828546.

    https://doi.org/10.3389/fnins.2022.828546

    • PubMed
    • Google Scholar
24. 1. Buysse DJ
    2. Reynolds CF
    3. Monk TH
    4. Berman SR
    5. Kupfer DJ

    (1989) The pittsburgh sleep quality index: a new instrument for psychiatric practice and research

    Psychiatry Research 28:193–213.

    https://doi.org/10.1016/0165-1781(89)90047-4

    • PubMed
    • Google Scholar
25. 1. Buzsáki G
    2. Tingley D

    (2018) Space and time: the hippocampus as a sequence generator

    Trends in Cognitive Sciences 22:853–869.

    https://doi.org/10.1016/j.tics.2018.07.006

    • PubMed
    • Google Scholar
26. 1. Cairney SA
    2. Guttesen AÁV
    3. El Marj N
    4. Staresina BP

    (2018) Memory consolidation is linked to spindle-mediated information processing during sleep

    Current Biology 28:948–954.

    https://doi.org/10.1016/j.cub.2018.01.087

    • PubMed
    • Google Scholar
27. 1. Casali AG
    2. Gosseries O
    3. Rosanova M
    4. Boly M
    5. Sarasso S
    6. Casali KR
    7. Casarotto S
    8. Bruno MA
    9. Laureys S
    10. Tononi G
    11. Massimini M

    (2013) A theoretically based index of consciousness independent of sensory processing and behavior

    Science Translational Medicine 5:198ra105.

    https://doi.org/10.1126/scitranslmed.3006294

    • PubMed
    • Google Scholar
28. 1. Chen Z
    2. Wilson MA

    (2017) Deciphering neural codes of memory during sleep

    Trends in Neurosciences 40:260–275.

    https://doi.org/10.1016/j.tins.2017.03.005

    • PubMed
    • Google Scholar
29. Book

    1. Cohen NJ
    2. Eichenbaum H

    (1993)

    Memory, Amnesia, and the Hippocampal System

    A Bradford Book.

    • Google Scholar
30. 1. Cox R
    2. Hofman WF
    3. de Boer M
    4. Talamini LM

    (2014a) Local sleep spindle modulations in relation to specific memory cues

    NeuroImage 99:103–110.

    https://doi.org/10.1016/j.neuroimage.2014.05.028

    • PubMed
    • Google Scholar
31. 1. Cox R
    2. Korjoukov I
    3. de Boer M
    4. Talamini LM

    (2014b) Sound asleep: processing and retention of slow oscillation phase-targeted stimuli

    PLOS ONE 9:e101567.

    https://doi.org/10.1371/journal.pone.0101567

    • PubMed
    • Google Scholar
32. 1. Dang-Vu TT
    2. Bonjean M
    3. Schabus M
    4. Boly M
    5. Darsaud A
    6. Desseilles M
    7. Degueldre C
    8. Balteau E
    9. Phillips C
    10. Luxen A
    11. Sejnowski TJ
    12. Maquet P

    (2011) Interplay between spontaneous and induced brain activity during human non-rapid eye movement sleep

    PNAS 108:15438–15443.

    https://doi.org/10.1073/pnas.1112503108

    • PubMed
    • Google Scholar
33. 1. de Lavilléon G
    2. Lacroix MM
    3. Rondi-Reig L
    4. Benchenane K

    (2015) Explicit memory creation during sleep demonstrates a causal role of place cells in navigation

    Nature Neuroscience 18:493–495.

    https://doi.org/10.1038/nn.3970

    • PubMed
    • Google Scholar
34. 1. Dement W
    2. Kleitman N

    (1957) The relation of eye movements during sleep to dream activity: an objective method for the study of dreaming

    Journal of Experimental Psychology 53:339–346.

    https://doi.org/10.1037/h0048189

    • PubMed
    • Google Scholar
35. 1. Denis D
    2. Schapiro AC
    3. Poskanzer C
    4. Bursal V
    5. Charon L
    6. Morgan A
    7. Stickgold R

    (2020) The roles of item exposure and visualization success in the consolidation of memories across wake and sleep

    Learning & Memory 27:451–456.

    https://doi.org/10.1101/lm.051383.120

    • PubMed
    • Google Scholar
36. 1. Destexhe A
    2. Hughes SW
    3. Rudolph M
    4. Crunelli V

    (2007) Are corticothalamic “up” states fragments of wakefulness?

    Trends in Neurosciences 30:334–342.

    https://doi.org/10.1016/j.tins.2007.04.006

    • PubMed
    • Google Scholar
37. 1. Dew ITZ
    2. Cabeza R

    (2011) The porous boundaries between explicit and implicit memory: behavioral and neural evidence

    Annals of the New York Academy of Sciences 1224:174–190.

    https://doi.org/10.1111/j.1749-6632.2010.05946.x

    • PubMed
    • Google Scholar
38. 1. Diba K
    2. Buzsáki G

    (2007) Forward and reverse hippocampal place-cell sequences during ripples

    Nature Neuroscience 10:1241–1242.

    https://doi.org/10.1038/nn1961

    • PubMed
    • Google Scholar
39. 1. Drosopoulos S
    2. Schulze C
    3. Fischer S
    4. Born J

    (2007) Sleep’s function in the spontaneous recovery and consolidation of memories

    Journal of Experimental Psychology. General 136:169–183.

    https://doi.org/10.1037/0096-3445.136.2.169

    • PubMed
    • Google Scholar
40. 1. Duss SB
    2. Reber TP
    3. Hänggi J
    4. Schwab S
    5. Wiest R
    6. Müri RM
    7. Brugger P
    8. Gutbrod K
    9. Henke K

    (2014) Unconscious relational encoding depends on hippocampus

    Brain 137:3355–3370.

    https://doi.org/10.1093/brain/awu270

    • PubMed
    • Google Scholar
41. 1. Duyck W
    2. Desmet T
    3. Verbeke LPC
    4. Brysbaert M

    (2004) Wordgen: a tool for word selection and nonword generation in Dutch, English, German, and French

    Behavior Research Methods, Instruments, & Computers 36:488–499.

    https://doi.org/10.3758/bf03195595

    • PubMed
    • Google Scholar
42. 1. Eban-Rothschild A
    2. Rothschild G
    3. Giardino WJ
    4. Jones JR
    5. de Lecea L

    (2016) VTA dopaminergic neurons regulate ethologically relevant sleep-wake behaviors

    Nature Neuroscience 19:1356–1366.

    https://doi.org/10.1038/nn.4377

    • PubMed
    • Google Scholar
43. 1. Ebbinghaus H

    (2013) Memory: a contribution to experimental psychology

    Annals of Neurosciences 20:155–156.

    https://doi.org/10.5214/ans.0972.7531.200408

    • Google Scholar
44. 1. Engert F
    2. Bonhoeffer T

    (1999) Dendritic spine changes associated with hippocampal long-term synaptic plasticity

    Nature 399:66–70.

    https://doi.org/10.1038/19978

    • PubMed
    • Google Scholar
45. 1. Fernández-Ruiz A
    2. Oliva A
    3. Nagy GA
    4. Maurer AP
    5. Berényi A
    6. Buzsáki G

    (2017) Entorhinal-ca3 dual-input control of spike timing in the hippocampus by theta-gamma coupling

    Neuron 93:1213–1226.

    https://doi.org/10.1016/j.neuron.2017.02.017

    • PubMed
    • Google Scholar
46. 1. Formby D

    (1967) Maternal recognition of infant’s cry

    Developmental Medicine & Child Neurology 9:293–298.

    https://doi.org/10.1111/j.1469-8749.1967.tb02271.x

    • Google Scholar
47. 1. Frankland PW
    2. O’Brien C
    3. Ohno M
    4. Kirkwood A
    5. Silva AJ

    (2001) Alpha-CaMKII-dependent plasticity in the cortex is required for permanent memory

    Nature 411:309–313.

    https://doi.org/10.1038/35077089

    • PubMed
    • Google Scholar
48. 1. Gabrieli JDE

    (1998) Cognitive neuroscience of human memory

    Annual Review of Psychology 49:87–115.

    https://doi.org/10.1146/annurev.psych.49.1.87

    • PubMed
    • Google Scholar
49. 1. Gent TC
    2. Bandarabadi M
    3. Herrera CG
    4. Adamantidis AR

    (2018) Thalamic dual control of sleep and wakefulness

    Nature Neuroscience 21:974–984.

    https://doi.org/10.1038/s41593-018-0164-7

    • PubMed
    • Google Scholar
50. 1. Göldi M
    2. van Poppel EAM
    3. Rasch B
    4. Schreiner T

    (2019) Increased neuronal signatures of targeted memory reactivation during slow-wave up states

    Scientific Reports 9:2715.

    https://doi.org/10.1038/s41598-019-39178-2

    • PubMed
    • Google Scholar
51. 1. Goto A
    2. Bota A
    3. Miya K
    4. Wang J
    5. Tsukamoto S
    6. Jiang X
    7. Hirai D
    8. Murayama M
    9. Matsuda T
    10. McHugh TJ
    11. Nagai T
    12. Hayashi Y

    (2021) Stepwise synaptic plasticity events drive the early phase of memory consolidation

    Science 374:857–863.

    https://doi.org/10.1126/science.abj9195

    • PubMed
    • Google Scholar
52. 1. Grandchamp R
    2. Delorme A

    (2011) Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials

    Frontiers in Psychology 2:236.

    https://doi.org/10.3389/fpsyg.2011.00236

    • PubMed
    • Google Scholar
53. 1. Hagoort P

    (2008) The fractionation of spoken language understanding by measuring electrical and magnetic brain signals

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363:1055–1069.

    https://doi.org/10.1098/rstb.2007.2159

    • PubMed
    • Google Scholar
54. 1. Halász P

    (2005) K-complex, a reactive eeg graphoelement of nrem sleep: an old chap in a new garment

    Sleep Medicine Reviews 9:391–412.

    https://doi.org/10.1016/j.smrv.2005.04.003

    • PubMed
    • Google Scholar
55. 1. Halász P

    (2016) The K-complex as a special reactive sleep slow wave: a theoretical update

    Sleep Medicine Reviews 29:34–40.

    https://doi.org/10.1016/j.smrv.2015.09.004

    • PubMed
    • Google Scholar
56. 1. Hannula DE
    2. Greene AJ

    (2012) The hippocampus reevaluated in unconscious learning and memory: at a tipping point?

    Frontiers in Human Neuroscience 6:80.

    https://doi.org/10.3389/fnhum.2012.00080

    • PubMed
    • Google Scholar
57. 1. Hardt O
    2. Nader K
    3. Nadel L

    (2013) Decay happens: the role of active forgetting in memory

    Trends in Cognitive Sciences 17:111–120.

    https://doi.org/10.1016/j.tics.2013.01.001

    • PubMed
    • Google Scholar
58. 1. He J
    2. Sun HQ
    3. Li SX
    4. Zhang WH
    5. Shi J
    6. Ai SZ
    7. Li Y
    8. Li XJ
    9. Tang XD
    10. Lu L

    (2015) Effect of conditioned stimulus exposure during slow wave sleep on fear memory extinction in humans

    Sleep 38:423–431.

    https://doi.org/10.5665/sleep.4502

    • PubMed
    • Google Scholar
59. Book

    1. Hebb D

    (1949) The Organization of Behavior: A Psychological Theory

    Wiley.

    https://doi.org/10.4324/9781410612403

    • Google Scholar
60. 1. Henke K
    2. Mondadori CRA
    3. Treyer V
    4. Nitsch RM
    5. Buck A
    6. Hock C

    (2003) Nonconscious formation and reactivation of semantic associations by way of the medial temporal lobe

    Neuropsychologia 41:863–876.

    https://doi.org/10.1016/s0028-3932(03)00035-6

    • PubMed
    • Google Scholar
61. 1. Henke K

    (2010) A model for memory systems based on processing modes rather than consciousness

    Nature Reviews. Neuroscience 11:523–532.

    https://doi.org/10.1038/nrn2850

    • PubMed
    • Google Scholar
62. 1. Henke K
    2. Reber TP
    3. Duss SB

    (2013) Integrating events across levels of consciousness

    Frontiers in Behavioral Neuroscience 7:68.

    https://doi.org/10.3389/fnbeh.2013.00068

    • PubMed
    • Google Scholar
63. 1. Hickok G
    2. Poeppel D

    (2007) The cortical organization of speech processing

    Nature Reviews. Neuroscience 8:393–402.

    https://doi.org/10.1038/nrn2113

    • PubMed
    • Google Scholar
64. 1. Hoddes E
    2. Zarcone V
    3. Smythe H
    4. Phillips R
    5. Dement WC

    (1973) Quantification of sleepiness: a new approach

    Psychophysiology 10:431–436.

    https://doi.org/10.1111/j.1469-8986.1973.tb00801.x

    • PubMed
    • Google Scholar
65. 1. Höhn C
    2. Hahn MA
    3. Lendner JD
    4. Hoedlmoser K

    (2023) Spectral slope and lempel-ziv complexity as robust markers of brain states during sleep and wakefulness

    Neuroscience 01:7390.

    https://doi.org/10.1101/2022.09.10.507390

    • Google Scholar
66. 1. Holeckova I
    2. Fischer C
    3. Giard MH
    4. Delpuech C
    5. Morlet D

    (2006) Brain responses to a subject’s own name uttered by a familiar voice

    Brain Research 1082:142–152.

    https://doi.org/10.1016/j.brainres.2006.01.089

    • PubMed
    • Google Scholar
67. 1. Ibáñez A
    2. López V
    3. Cornejo C

    (2006) ERPs and contextual semantic discrimination: degrees of congruence in wakefulness and sleep

    Brain and Language 98:264–275.

    https://doi.org/10.1016/j.bandl.2006.05.005

    • PubMed
    • Google Scholar
68. Book

    1. Iber C
    2. Ancoli-Israel S
    3. Chesson A
    4. Quan SF

    (2007)

    The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications

    American Academy of Sleep Medicine.

    • Google Scholar
69. Book

    1. Jacobsen E

    (1929)

    Progressive Relaxation

    Univ. of Chicago Press.

    • Google Scholar
70. 1. Kahana MJ
    2. Seelig D
    3. Madsen JR

    (2001) Theta returns

    Current Opinion in Neurobiology 11:739–744.

    https://doi.org/10.1016/s0959-4388(01)00278-1

    • PubMed
    • Google Scholar
71. 1. Kalra N
    2. Khakha D
    3. Satapathy S
    4. Dey S

    (2015)

    Journal of Psychosocial Research

    Impact of Jacobson Progressive Muscle Relaxation and Deep Breathing Exercises on Anxiety, Psychological Distress and Quality of Sleep of Hospitalized Older Adults 10:211–223.

    • Google Scholar
72. 1. Khanna A
    2. Pascual-Leone A
    3. Michel CM
    4. Farzan F

    (2015) Microstates in resting-state EEG: current status and future directions

    Neuroscience and Biobehavioral Reviews 49:105–113.

    https://doi.org/10.1016/j.neubiorev.2014.12.010

    • PubMed
    • Google Scholar
73. 1. Kimura D

    (1961) Cerebral dominance and the perception of verbal stimuli

    Canadian Journal of Psychology / Revue Canadienne de Psychologie 15:166–171.

    https://doi.org/10.1037/h0083219

    • Google Scholar
74. 1. Koroma M
    2. Elbaz M
    3. Léger D
    4. Kouider S

    (2022) Learning new vocabulary implicitly during sleep transfers with cross-modal generalization into wakefulness

    Frontiers in Neuroscience 16:801666.

    https://doi.org/10.3389/fnins.2022.801666

    • PubMed
    • Google Scholar
75. 1. Latchoumane CFV
    2. Ngo HVV
    3. Born J
    4. Shin HS

    (2017) Thalamic spindles promote memory formation during sleep through triple phase-locking of cortical, thalamic, and hippocampal rhythms

    Neuron 95:424–435.

    https://doi.org/10.1016/j.neuron.2017.06.025

    • PubMed
    • Google Scholar
76. 1. Lau ZJ
    2. Pham T
    3. Chen SHA
    4. Makowski D

    (2022) Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations

    The European Journal of Neuroscience 56:5047–5069.

    https://doi.org/10.1111/ejn.15800

    • PubMed
    • Google Scholar
77. 1. Laurino M
    2. Menicucci D
    3. Piarulli A
    4. Mastorci F
    5. Bedini R
    6. Allegrini P
    7. Gemignani A

    (2014) Disentangling different functional roles of evoked K-complex components: mapping the sleeping brain while quenching sensory processing

    NeuroImage 86:433–445.

    https://doi.org/10.1016/j.neuroimage.2013.10.030

    • PubMed
    • Google Scholar
78. 1. Laurino M
    2. Piarulli A
    3. Menicucci D
    4. Gemignani A

    (2019) Local gamma activity during non-rem sleep in the context of sensory evoked k-complexes

    Frontiers in Neuroscience 13:1094.

    https://doi.org/10.3389/fnins.2019.01094

    • PubMed
    • Google Scholar
79. 1. Levy BR
    2. Pilver C
    3. Chung PH
    4. Slade MD

    (2014) Subliminal strengthening: improving older individuals’ physical function over time with an implicit-age-stereotype intervention

    Psychological Science 25:2127–2135.

    https://doi.org/10.1177/0956797614551970

    • PubMed
    • Google Scholar
80. 1. Long NM
    2. Burke JF
    3. Kahana MJ

    (2014) Subsequent memory effect in intracranial and scalp EEG

    NeuroImage 84:488–494.

    https://doi.org/10.1016/j.neuroimage.2013.08.052

    • PubMed
    • Google Scholar
81. 1. Ma Y
    2. Shi W
    3. Peng CK
    4. Yang AC

    (2018) Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches

    Sleep Medicine Reviews 37:85–93.

    https://doi.org/10.1016/j.smrv.2017.01.003

    • PubMed
    • Google Scholar
82. 1. Maingret N
    2. Girardeau G
    3. Todorova R
    4. Goutierre M
    5. Zugaro M

    (2016) Hippocampo-cortical coupling mediates memory consolidation during sleep

    Nature Neuroscience 19:959–964.

    https://doi.org/10.1038/nn.4304

    • PubMed
    • Google Scholar
83. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
84. 1. Massimini M
    2. Ferrarelli F
    3. Sarasso S
    4. Tononi G

    (2012) Cortical mechanisms of loss of consciousness: insight from TMS/EEG studies

    Archives Italiennes de Biologie 150:44–55.

    https://doi.org/10.4449/aib.v150i2.1361

    • PubMed
    • Google Scholar
85. 1. McCormick DA
    2. Bal T

    (1994) Sensory gating mechanisms of the thalamus

    Current Opinion in Neurobiology 4:550–556.

    https://doi.org/10.1016/0959-4388(94)90056-6

    • PubMed
    • Google Scholar
86. 1. McHugh ML

    (2012) Interrater reliability: the kappa statistic

    Biochemia Medica 22:276–282.

    https://doi.org/10.11613/BM.2012.031

    • PubMed
    • Google Scholar
87. 1. Michel CM
    2. Koenig T

    (2018) Eeg microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review

    NeuroImage 180:577–593.

    https://doi.org/10.1016/j.neuroimage.2017.11.062

    • PubMed
    • Google Scholar
88. 1. Mölle M
    2. Marshall L
    3. Gais S
    4. Born J

    (2002) Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep

    The Journal of Neuroscience 22:10941–10947.

    https://doi.org/10.1523/JNEUROSCI.22-24-10941.2002

    • PubMed
    • Google Scholar
89. 1. Mölle M
    2. Eschenko O
    3. Gais S
    4. Sara SJ
    5. Born J

    (2009) The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats

    The European Journal of Neuroscience 29:1071–1081.

    https://doi.org/10.1111/j.1460-9568.2009.06654.x

    • PubMed
    • Google Scholar
90. 1. Mölle M
    2. Bergmann TO
    3. Marshall L
    4. Born J

    (2011) Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing

    Sleep 34:1411–1421.

    https://doi.org/10.5665/SLEEP.1290

    • PubMed
    • Google Scholar
91. Software

    1. Monge-Alvarez J

    (2024) Higuchi and Katz Fractal dimension measures, version 1.1.0.0

    MATLAB Central File Exchange.

    https://www.mathworks.com/matlabcentral/fileexchange/50290-higuchi-and-katz-fractal-dimension-measures
92. 1. Mormann F
    2. Fell J
    3. Axmacher N
    4. Weber B
    5. Lehnertz K
    6. Elger CE
    7. Fernández G

    (2005) Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continuous word recognition memory task

    Hippocampus 15:890–900.

    https://doi.org/10.1002/hipo.20117

    • PubMed
    • Google Scholar
93. 1. Moscovitch M

    (2008) The hippocampus as a “stupid,” domain-specific module: Implications for theories of recent and remote memory, and of imagination

    Canadian Journal of Experimental Psychology = Revue Canadienne de Psychologie Experimentale 62:62–79.

    https://doi.org/10.1037/1196-1961.62.1.62

    • PubMed
    • Google Scholar
94. 1. Moyne M
    2. Legendre G
    3. Arnal L
    4. Kumar S
    5. Sterpenich V
    6. Seeck M
    7. Grandjean D
    8. Schwartz S
    9. Vuilleumier P
    10. Domínguez-Borràs J

    (2022) Brain reactivity to emotion persists in NREM sleep and is associated with individual dream recall

    Cerebral Cortex Communications 3:tgac003.

    https://doi.org/10.1093/texcom/tgac003

    • PubMed
    • Google Scholar
95. 1. Muehlroth BE
    2. Sander MC
    3. Fandakova Y
    4. Grandy TH
    5. Rasch B
    6. Shing YL
    7. Werkle-Bergner M

    (2019) Precise slow oscillation-spindle coupling promotes memory consolidation in younger and older adults

    Scientific Reports 9:1940.

    https://doi.org/10.1038/s41598-018-36557-z

    • PubMed
    • Google Scholar
96. 1. Murre JMJ
    2. Dros J

    (2015) Replication and analysis of ebbinghaus’ forgetting curve

    PLOS ONE 10:e0120644.

    https://doi.org/10.1371/journal.pone.0120644

    • PubMed
    • Google Scholar
97. 1. Navarrete M
    2. Schneider J
    3. Ngo HVV
    4. Valderrama M
    5. Casson AJ
    6. Lewis PA

    (2020) Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults

    Sleep 43:zsz315.

    https://doi.org/10.1093/sleep/zsz315

    • PubMed
    • Google Scholar
98. 1. Ngo HVV
    2. Martinetz T
    3. Born J
    4. Mölle M

    (2013) Auditory closed-loop stimulation of the sleep slow oscillation enhances memory

    Neuron 78:545–553.

    https://doi.org/10.1016/j.neuron.2013.03.006

    • PubMed
    • Google Scholar
99. 1. Ngo HVV
    2. Staresina BP

    (2022) Shaping overnight consolidation via slow-oscillation closed-loop targeted memory reactivation

    PNAS 119:e2123428119.

    https://doi.org/10.1073/pnas.2123428119

    • PubMed
    • Google Scholar
100. 1. Niknazar H
     2. Malerba P
     3. Mednick SC

     (2023) Slow oscillations promote long range effective communication: The key for memory consolidation in a broken down network

     Neuroscience 01:8133.

     https://doi.org/10.1101/2022.04.13.488133

     • Google Scholar
101. 1. O’Reilly RC
     2. Rudy JW

     (2000) Computational principles of learning in the neocortex and hippocampus

     Hippocampus 10:389–397.

     https://doi.org/10.1002/1098-1063(2000)10:4<389::AID-HIPO5>3.0.CO;2-P

     • PubMed
     • Google Scholar
102. 1. O’Reilly RC
     2. Bhattacharyya R
     3. Howard MD
     4. Ketz N

     (2014) Complementary learning systems

     Cognitive Science 38:1229–1248.

     https://doi.org/10.1111/j.1551-6709.2011.01214.x

     • PubMed
     • Google Scholar
103. 1. Osipova D
     2. Takashima A
     3. Oostenveld R
     4. Fernández G
     5. Maris E
     6. Jensen O

     (2006) Theta and gamma oscillations predict encoding and retrieval of declarative memory

     The Journal of Neuroscience 26:7523–7531.

     https://doi.org/10.1523/JNEUROSCI.1948-06.2006

     • PubMed
     • Google Scholar
104. 1. Oswald I
     2. Taylor AM
     3. Treisman M

     (1960) Discriminative responses to stimulation during human sleep

     Brain 83:440–453.

     https://doi.org/10.1093/brain/83.3.440

     • PubMed
     • Google Scholar
105. 1. Pacozzi L
     2. Knüsel L
     3. Ruch S
     4. Henke K

     (2022) Inverse forgetting in unconscious episodic memory

     Scientific Reports 12:20595.

     https://doi.org/10.1038/s41598-022-25100-w

     • PubMed
     • Google Scholar
106. 1. Parbat D
     2. Chakraborty M

     (2021) A novel methodology to study the cognitive load induced eeg complexity changes: Chaos, fractal and entropy based approach

     Biomedical Signal Processing and Control 64:102277.

     https://doi.org/10.1016/j.bspc.2020.102277

     • Google Scholar
107. 1. Petzka M
     2. Charest I
     3. Balanos GM
     4. Staresina BP

     (2021) Does sleep-dependent consolidation favour weak memories?

     Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 134:65–75.

     https://doi.org/10.1016/j.cortex.2020.10.005

     • PubMed
     • Google Scholar
108. 1. Petzka M
     2. Chatburn A
     3. Charest I
     4. Balanos GM
     5. Staresina BP

     (2022) Sleep spindles track cortical learning patterns for memory consolidation

     Current Biology 32:2349–2356.

     https://doi.org/10.1016/j.cub.2022.04.045

     • PubMed
     • Google Scholar
109. 1. Pulvermüller F
     2. Shtyrov Y
     3. Hauk O

     (2009) Understanding in an instant: neurophysiological evidence for mechanistic language circuits in the brain

     Brain and Language 110:81–94.

     https://doi.org/10.1016/j.bandl.2008.12.001

     • PubMed
     • Google Scholar
110. 1. Quiroga RQ
     2. Reddy L
     3. Kreiman G
     4. Koch C
     5. Fried I

     (2005) Invariant visual representation by single neurons in the human brain

     Nature 435:1102–1107.

     https://doi.org/10.1038/nature03687

     • PubMed
     • Google Scholar
111. 1. Rabinovich Orlandi I
     2. Fullio CL
     3. Schroeder MN
     4. Giurfa M
     5. Ballarini F
     6. Moncada D

     (2020) Behavioral tagging underlies memory reconsolidation

     PNAS 117:18029–18036.

     https://doi.org/10.1073/pnas.2009517117

     • PubMed
     • Google Scholar
112. 1. Rasch B
     2. Born J

     (2013) About sleep’s role in memory

     Physiological Reviews 93:681–766.

     https://doi.org/10.1152/physrev.00032.2012

     • PubMed
     • Google Scholar
113. 1. Reber TP
     2. Luechinger R
     3. Boesiger P
     4. Henke K

     (2012) Unconscious relational inference recruits the hippocampus

     The Journal of Neuroscience 32:6138–6148.

     https://doi.org/10.1523/JNEUROSCI.5639-11.2012

     • PubMed
     • Google Scholar
114. 1. Renard Y
     2. Lotte F
     3. Gibert G
     4. Congedo M
     5. Maby E
     6. Delannoy V
     7. Bertrand O
     8. Lécuyer A

     (2010) Openvibe: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments

     Presence 19:35–53.

     https://doi.org/10.1162/pres.19.1.35

     • Google Scholar
115. 1. Riedner BA
     2. Hulse BK
     3. Murphy MJ
     4. Ferrarelli F
     5. Tononi G

     (2011) Temporal dynamics of cortical sources underlying spontaneous and peripherally evoked slow waves

     Progress in Brain Research 193:201–218.

     https://doi.org/10.1016/B978-0-444-53839-0.00013-2

     • PubMed
     • Google Scholar
116. 1. Ruby P
     2. Caclin A
     3. Boulet S
     4. Delpuech C
     5. Morlet D

     (2008) Odd sound processing in the sleeping brain

     Journal of Cognitive Neuroscience 20:296–311.

     https://doi.org/10.1162/jocn.2008.20023

     • PubMed
     • Google Scholar
117. 1. Ruch S
     2. Koenig T
     3. Mathis J
     4. Roth C
     5. Henke K

     (2014) Word encoding during sleep is suggested by correlations between word-evoked up-states and post-sleep semantic priming

     Frontiers in Psychology 5:1319.

     https://doi.org/10.3389/fpsyg.2014.01319

     • PubMed
     • Google Scholar
118. 1. Ruch S
     2. Henke K

     (2020) Learning during sleep: a dream comes true?

     Trends in Cognitive Sciences 24:170–172.

     https://doi.org/10.1016/j.tics.2019.12.007

     • Google Scholar
119. 1. Ruch S
     2. Schmidig FJ
     3. Knüsel L
     4. Henke K

     (2022) Closed-loop modulation of local slow oscillations in human NREM sleep

     NeuroImage 264:119682.

     https://doi.org/10.1016/j.neuroimage.2022.119682

     • PubMed
     • Google Scholar
120. 1. Sabri M
     2. De Lugt DR
     3. Campbell KB

     (2000) The mismatch negativity to frequency deviants during the transition from wakefulness to sleep

     Canadian Journal of Experimental Psychology = Revue Canadienne de Psychologie Experimentale 54:230–242.

     https://doi.org/10.1037/h0087343

     • PubMed
     • Google Scholar
121. 1. Sakaguchi M
     2. Hayashi Y

     (2012) Catching the engram: strategies to examine the memory trace

     Molecular Brain 5:32.

     https://doi.org/10.1186/1756-6606-5-32

     • PubMed
     • Google Scholar
122. 1. Schabus M
     2. Dang-Vu TT
     3. Heib DPJ
     4. Boly M
     5. Desseilles M
     6. Vandewalle G
     7. Schmidt C
     8. Albouy G
     9. Darsaud A
     10. Gais S
     11. Degueldre C
     12. Balteau E
     13. Phillips C
     14. Luxen A
     15. Maquet P

     (2012) THe fate of incoming stimuli during nrem sleep is determined by spindles and the phase of the slow oscillation

     Frontiers in Neurology 3:40.

     https://doi.org/10.3389/fneur.2012.00040

     • PubMed
     • Google Scholar
123. 1. Schacter DL

     (1998) Memory and awareness

     Science 280:59–60.

     https://doi.org/10.1126/science.280.5360.59

     • PubMed
     • Google Scholar
124. 1. Schechtman E
     2. Lampe A
     3. Wilson BJ
     4. Kwon E
     5. Anderson MC
     6. Paller KA

     (2021) Sleep reactivation did not boost suppression-induced forgetting

     Scientific Reports 11:1383.

     https://doi.org/10.1038/s41598-020-80671-w

     • PubMed
     • Google Scholar
125. 1. Schneider E
     2. Züst MA
     3. Wuethrich S
     4. Schmidig F
     5. Klöppel S
     6. Wiest R
     7. Ruch S
     8. Henke K

     (2021) Larger capacity for unconscious versus conscious episodic memory

     Current Biology 31:3551–3563.

     https://doi.org/10.1016/j.cub.2021.06.012

     • PubMed
     • Google Scholar
126. 1. Schreiner T
     2. Staudigl T

     (2020) Electrophysiological signatures of memory reactivation in humans

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 375:20190293.

     https://doi.org/10.1098/rstb.2019.0293

     • PubMed
     • Google Scholar
127. 1. Sheehan TC
     2. Sreekumar V
     3. Inati SK
     4. Zaghloul KA

     (2018) Signal complexity of human intracranial eeg tracks successful associative-memory formation across individuals

     The Journal of Neuroscience 38:1744–1755.

     https://doi.org/10.1523/JNEUROSCI.2389-17.2017

     • PubMed
     • Google Scholar
128. 1. Sidhu DM
     2. Pexman PM

     (2017) A prime example of the maluma/takete effect? Testing for sound symbolic priming

     Cognitive Science 41:1958–1987.

     https://doi.org/10.1111/cogs.12438

     • PubMed
     • Google Scholar
129. 1. Sirota A
     2. Buzsáki G

     (2005) Interaction between neocortical and hippocampal networks via slow oscillations

     Thalamus & Related Systems 3:245–259.

     https://doi.org/10.1017/S1472928807000258

     • PubMed
     • Google Scholar
130. 1. Skeide MA
     2. Friederici AD

     (2016) The ontogeny of the cortical language network

     Nature Reviews. Neuroscience 17:323–332.

     https://doi.org/10.1038/nrn.2016.23

     • PubMed
     • Google Scholar
131. 1. Squire LR
     2. Dede AJO

     (2015) Conscious and unconscious memory systems

     Cold Spring Harbor Perspectives in Biology 7:a021667.

     https://doi.org/10.1101/cshperspect.a021667

     • PubMed
     • Google Scholar
132. 1. Staresina BP
     2. Bergmann TO
     3. Bonnefond M
     4. van der Meij R
     5. Jensen O
     6. Deuker L
     7. Elger CE
     8. Axmacher N
     9. Fell J

     (2015) Hierarchical nesting of slow oscillations, spindles and ripples in the human hippocampus during sleep

     Nature Neuroscience 18:1679–1686.

     https://doi.org/10.1038/nn.4119

     • PubMed
     • Google Scholar
133. 1. Staresina BP
     2. Wimber M

     (2019) A neural chronometry of memory recall

     Trends in Cognitive Sciences 23:1071–1085.

     https://doi.org/10.1016/j.tics.2019.09.011

     • PubMed
     • Google Scholar
134. Website

     1. Stickgold R
     2. Zadra A
     3. Haar AJH

     (2021) Advertising in dreams is coming: now what? dream1074 engineering

     Accessed June 8, 2021.

     https://dxe.pubpub.org/pub/dreamadvertising/release/1
135. 1. Strauss M
     2. Sitt JD
     3. King JR
     4. Elbaz M
     5. Azizi L
     6. Buiatti M
     7. Naccache L
     8. van Wassenhove V
     9. Dehaene S

     (2015) Disruption of hierarchical predictive coding during sleep

     PNAS 112:E1353–E1362.

     https://doi.org/10.1073/pnas.1501026112

     • PubMed
     • Google Scholar
136. 1. Takeuchi T
     2. Duszkiewicz AJ
     3. Morris RGM

     (2014) The synaptic plasticity and memory hypothesis: encoding, storage and persistence

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130288.

     https://doi.org/10.1098/rstb.2013.0288

     • PubMed
     • Google Scholar
137. 1. Taschereau-Dumouchel V
     2. Cortese A
     3. Chiba T
     4. Knotts JD
     5. Kawato M
     6. Lau H

     (2018a) Towards an unconscious neural reinforcement intervention for common fears

     PNAS 115:3470–3475.

     https://doi.org/10.1073/pnas.1721572115

     • PubMed
     • Google Scholar
138. 1. Taschereau-Dumouchel V
     2. Liu KY
     3. Lau H

     (2018b) Unconscious psychological treatments for physiological survival circuits

     Current Opinion in Behavioral Sciences 24:62–68.

     https://doi.org/10.1016/j.cobeha.2018.04.010

     • PubMed
     • Google Scholar
139. 1. Tonegawa S
     2. Morrissey MD
     3. Kitamura T

     (2018) The role of engram cells in the systems consolidation of memory

     Nature Reviews. Neuroscience 19:485–498.

     https://doi.org/10.1038/s41583-018-0031-2

     • PubMed
     • Google Scholar
140. 1. Tononi G
     2. Cirelli C

     (2006) Sleep function and synaptic homeostasis

     Sleep Medicine Reviews 10:49–62.

     https://doi.org/10.1016/j.smrv.2005.05.002

     • PubMed
     • Google Scholar
141. 1. Tsien JZ
     2. Huerta PT
     3. Tonegawa S

     (1996) The essential role of hippocampal CA1 NMDA receptor-dependent synaptic plasticity in spatial memory

     Cell 87:1327–1338.

     https://doi.org/10.1016/s0092-8674(00)81827-9

     • PubMed
     • Google Scholar
142. 1. Tucker MA
     2. Fishbein W

     (2008) Enhancement of declarative memory performance following a daytime nap is contingent on strength of initial task acquisition

     Sleep 31:197–203.

     https://doi.org/10.1093/sleep/31.2.197

     • PubMed
     • Google Scholar
143. 1. Tulving E

     (2002) Episodic memory: from mind to brain

     Annual Review of Psychology 53:1–25.

     https://doi.org/10.1146/annurev.psych.53.100901.135114

     • PubMed
     • Google Scholar
144. 1. Türker B
     2. Musat EM
     3. Chabani E
     4. Fonteix-Galet A
     5. Maranci JB
     6. Wattiez N
     7. Pouget P
     8. Sitt J
     9. Naccache L
     10. Arnulf I
     11. Oudiette D

     (2023) Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of the external world during sleep

     Nature Neuroscience 26:1981–1993.

     https://doi.org/10.1038/s41593-023-01449-7

     • PubMed
     • Google Scholar
145. 1. Vyazovskiy VV
     2. Harris KD

     (2013) Sleep and the single neuron: the role of global slow oscillations in individual cell rest

     Nature Reviews. Neuroscience 14:443–451.

     https://doi.org/10.1038/nrn3494

     • PubMed
     • Google Scholar
146. 1. Waschke L
     2. Wöstmann M
     3. Obleser J

     (2017) States and traits of neural irregularity in the age-varying human brain

     Scientific Reports 7:17381.

     https://doi.org/10.1038/s41598-017-17766-4

     • PubMed
     • Google Scholar
147. 1. Waschke L
     2. Tune S
     3. Obleser J

     (2019) Local cortical desynchronization and pupil-linked arousal differentially shape brain states for optimal sensory performance

     eLife 8:e51501.

     https://doi.org/10.7554/eLife.51501

     • PubMed
     • Google Scholar
148. 1. Wuethrich S
     2. Hannula DE
     3. Mast FW
     4. Henke K

     (2018) Subliminal encoding and flexible retrieval of objects in scenes

     Hippocampus 28:633–643.

     https://doi.org/10.1002/hipo.22957

     • PubMed
     • Google Scholar
149. 1. Yoshida K
     2. Toyoizumi T

     (2023) Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep

     PNAS Nexus 2:gac286.

     https://doi.org/10.1093/pnasnexus/pgac286

     • PubMed
     • Google Scholar
150. Book

     1. Zar J

     (1999)

     Biostatistical Analysis

     Pearson Education India.

     • Google Scholar
151. 1. Zhu Z
     2. Anderson MC
     3. Wang Y

     (2022) Inducing forgetting of unwanted memories through subliminal reactivation

     Nature Communications 13:6496.

     https://doi.org/10.1038/s41467-022-34091-1

     • Google Scholar
152. 1. Züst MA
     2. Colella P
     3. Reber TP
     4. Vuilleumier P
     5. Hauf M
     6. Ruch S
     7. Henke K

     (2015) Hippocampus is place of interaction between unconscious and conscious memories

     PLOS ONE 10:e0122459.

     https://doi.org/10.1371/journal.pone.0122459

     • PubMed
     • Google Scholar
153. 1. Züst MA
     2. Ruch S
     3. Wiest R
     4. Henke K

     (2019) Implicit vocabulary learning during sleep is bound to slow-wave peaks

     Current Biology 29:541–553.

     https://doi.org/10.1016/j.cub.2018.12.038

     • PubMed
     • Google Scholar

Author details

1. Flavio J Schmidig

   Institute of Psychology, University of Bern, Bern, Switzerland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   For correspondence

   flavio.schmidig@unibe.ch

   Competing interests

   No competing interests declared

   Additional information

   Lead contact

   "This ORCID iD identifies the author of this article:" 0000-0001-5257-0038

2. Simon Ruch

   1. Institute of Psychology, University of Bern, Bern, Switzerland
   2. Faculty of Psychology, UniDistance Suisse, Brig, Switzerland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5796-4543

3. Katharina Henke

   Institute of Psychology, University of Bern, Bern, Switzerland

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7895-044X

• 1,464

  views

• 165

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.