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

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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).

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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.

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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.

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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

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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

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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.

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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

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