Hippocampal-neocortical interactions around hippocampal ripples play an important role in memory processes (Buzsáki, 2015; Buzsáki, 1989; Qin et al., 1997; Schwindel and McNaughton, 2011). The functional role of such interactions is believed to be brain state-dependent such that they are involved in memory consolidation during non-rapid eye movement (NREM) sleep, while they are implicated in memory-guided behavior such as planning and memory retrieval in waking state (Ólafsdóttir et al., 2018; Roumis and Frank, 2015; Tang and Jadhav, 2019). This state-dependent functional dichotomy poses a question: how does spatiotemporal dynamics of hippocampal-neocortical network interactions differ in the two states?

There are pronounced differences in the neocortical activity patterns between NREM sleep and quite wakefulness. The most prominent difference is the near absence of so-called slow-oscillations (SO) during wakefulness. SO, a quasi-synchronous ≤ 1 Hz rhythmic fluctuation observed in local-field potential (LFP) and EEG recordings throughout the neocortex during NREM sleep, is partly correlated with hippocampal ripples, and recent memory reactivation in cortex is strongly locked to ripples (Benchenane et al., 2010; Kaefer et al., 2020; Peyrache et al., 2009; Rothschild et al., 2016). However, given the near absence of SO in wakefulness (Tang et al., 2017), it is not straightforward to extrapolate from sleep to wakefulness, although a few studies have shown that the proportion of neurons whose spiking activity is suppressed around hippocampal ripples is significantly higher in wakefulness compared with sleep (Jadhav et al., 2016; Tang et al., 2017).

In the present study, we extended the previous results by imaging the activity of a large portion of the dorsal neocortical mantle in awake mice, with concurrent LFP and multi-unit activity (MUA) recording from the pyramidal layer of the dorsal CA1. Wide-field glutamate and voltage recording were used to capture the excitatory synaptic input and the membrane potential fluctuations across neocortical regions and to correlate them with the occurrence of hippocampal ripples. A sharp contrast in the peri-ripple neocortical activity between the awake and sleep states was observed. To further elaborate on this contrast, we used the two-photon calcium imaging to focus on the agranular retrosplenial cortex (aRSC) whose glutamate and voltage activity patterns were different from the rest of the imaged regions. Our results suggest that inhibition is more pronounced in peri-ripple neocortical activity in awake than sleep states.

To study peri-ripple activity across neocortical regions in the awake state, we utilized three imaging modalities to shed light on different aspects of the problem at hand. First, to capture the internal dynamics of neocortical regions, wide-field voltage imaging with voltage indicator (butterfly1.2; voltage-sensitive fluorescent protein [VSFP] mice) expressed in the excitatory neurons of the neocortical layers II/III was used (Figure 1Aii). Second, to capture the excitatory input to the neocortical regions, wide-field imaging with intensity-based glutamate-sensing fluorescent reporter (iGluSnFR; iGlu-Ras mice) indicator expressed in the excitatory neurons of the neocortical layers II/III was used (Figure 1Ai). Last, to estimate the spiking output of neocortical neurons, two-photon calcium imaging of the aRSC superficial layers in Thy1-GCamp mice was conducted (Figure 1Aiii). In addition, to compare the peri-ripple glutamatergic transmission in superficial versus deep neocortical layers, wide-field imaging of iGluSnFR activity in all neocortical layers (iGlu-EMX mice) was conducted. In all the imaging modality experiments, concurrent LFP and MUA recordings from pyramidal layer of the CA1 subfield of the dorsal hippocampus was performed, and the hippocampal LFP was used to detect ripples (Figure 1B). Moreover, in three out of four sets of the experiments, electromyography (EMG) from the neck muscles was conducted to monitor the animals’ movements. During the recordings, the mice were placed on a stationary platform in all the experiments to increase the probability of occurrence of motionless periods during which ripples would emerge. In general, the animals did not move right before the ripples; however, they sometimes tend to move after ripples occurred (Figure 1—figure supplement 1). Hence, to remove the potential role of body movement on the brain optical signals, ripples with EMG tone around them (±500 ms) were excluded (0.2847±0.1576; mean proportion ±std; n=19 animals). Then, to extract the overall neocortical activity around ripples, the activity, captured by the three modalities, was aligned with respect to the timestamp of the ripple centers (largest trough of ripples) and averaged (Figure 1c). In addition, to evaluate the coordination between hippocampal and neocortical activity, the ensemble-wise correlation coefficient of the hippocampal MUA and the activity of neocortical regions were calculated (see Methods).

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Population membrane voltage significantly dropped during awake ripples in the neocortical superficial layers

The ripple event-triggered averaged neocortical membrane voltage showed a fast hyperpolarization right after the ripple centers (Figure 2Ai, Bi-iii). These peri-ripple voltage signals in the awake state were in sharp contrast with what had been reported in sleep where a membrane depolarization dominates (Karimi Abadchi et al., 2020 Figure 2—figure supplement 1). There was significant regional variation in the hyperpolarization pattern, with aRSC showing the strongest reduction of amplitude (Figure 2B). This phenomenon was consistent in all the six animals used for this set of experiments (Figure 2—figure supplement 2A). In addition, we did not find a significant correlation between the post-ripple hyperpolarization in aRSC and ripples amplitude nor duration (Figure 2—figure supplements 5 and 6; Fernández-Ruiz et al., 2019; Nitzan et al., 2022). However, the average peri-ripple aRSC voltage activity showed a larger pre-ripple depolarization and a delayed post-ripple hyperpolarization around bundled than single ripples (Figure 2—figure supplement 6B). The pre-ripple larger depolarization might signal the occurrence of a bundled ripple (similar to the larger pre-bundled- than pre-single-ripple deactivation observed during sleep [Karimi Abadchi et al., 2020]).

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The rate of reduction of voltage was fastest in aRSC in five out of six of the animals (Figure 2—figure supplement 2B). Moreover, we observed a pre-ripple elevation of voltage which was also strongest in aRSC in five out of six animals (Figure 2Biii; Figure 2—figure supplement 2C). Lastly, the ensemble-wise correlation coefficients averaged across VSFP animals revealed a period (~0–100 ms) of enhanced coordination between hippocampal MUA and aRSC voltage activity which was absent for the somatosensory regions (Figure 2Biv-vi; Figure 2—figure supplement 7). Importantly, a fine-scale (<70 ms; Figure 2—figure supplement 7B) temporal lead/lag relationship should not be inferred from these results mainly because the temporal resolution of the two signals, one electrophysiological, and one optical, are not the same.

aRSC neurons show opposite patterns of peri-ripple modulation

Due to the hyperpolarization of membrane potential in superficial layers of aRSC and the delayed glutamate elevation in the region (Figure 2—figure supplement 3C the onset time of RSC activation is larger than zero; Figure 3A), aRSC neurons may not fire at all during awake ripples. To address this question, we performed two-photon calcium imaging of the neurons in layers II/III of aRSC in Thy1-GCamp mice, despite the fact that the calcium signal is biased toward the burst of and not individual spikes (Huang et al., 2021). Peri-ripple averaging of single-cell calcium traces was performed, and the average traces of neurons over the interval −500 ms to +500 ms were clustered into two clusters using the k-means algorithm with correlation coefficient as the similarity metric. Second was the optimum number of clusters according to the silhouette and Calinski-Harabasz criteria. This analysis revealed that there are at least two equally sized subpopulations of neurons in aRSC; one whose calcium activity is elevated and one whose calcium activity is suppressed during and right after awake ripples (Figure 3Bi). Notably, in the ~1 s-long interval before the ripple centers, the calcium activity of elevated and suppressed sub-populations was suppressed and elevated, respectively (Figure 3Bii). The pre-ripple modulation of the two sub-populations is consistent with the excitatory and inhibitory ramps observed in Chambers et al., 2022. These results show that, despite the presence of significant reduction in population membrane voltage in aRSC, a substantially large sub-population of neurons increases their calcium activity during awake ripples. However, the timing of their calcium activity does not match that of the elevation in glutamate signal (increase in calcium precedes glutamate signal elevation). To make our results more comparable with those of electrophysiological studies, we deconvolved the calcium traces and tested for the significance of the modulation of each neuron by comparing its mean peri-ripple deconvolved trace with a neuron-specific shuffled distribution (see the Methods section for details). We found 8.46%±3 (mean ± std across 11 mice) of neurons were significantly modulated over the interval (0, 200 ms), 81.08%±8.91 (mean ± std across 11 mice) of which were up-modulated, and the rest were down-modulated (Figure 3—figure supplement 1). The observation that the elevation of calcium signal in up-modulated (activated) sub-population precedes that of glutamate activity in aRSC led us to ask whether the observed glutamate signal consists of components whose timings match those of the observed reduction in the voltage signal and the increase in the calcium activity. To address this question, we performed singular-value decomposition on the concatenated stack of individual peri-ripple glutamate activity chunks. This method decomposed the stack into components with specific spatial (Figure 3C upper row) and temporal modes. Then, for each component, the corresponding temporal mode was chunked around individual ripples, aligned, and averaged (Figure 3C lower row). Notably, the first component showed a global post-ripple elevation of glutamate activity whose amplitude was an order of magnitude larger than that in other components and explained 83.11%±6.75 (mean ± std across four iGluSnFR Ras mice). Other components, on the other hand, showed a mixture of elevation and reduction across neocortical regions. These patterns were similar across all the animals. We combined all the components with mixed patterns of modulation (components 2–100), explaining the rest of the remaining variance in the data (~17%), and reconstructed the mean peri-ripple glutamate activity across neocortical regions (Figure 3Di–iii). We observed that the mean peri-ripple glutamate activity in aRSC was decomposed into two specific patterns of post-ripple modulation, positive (Figure 3Dii; red signal; reconstructed from component 1) and negative (blue signal; reconstructed from components 2–100). Interestingly, other regions did not show the negative pattern of modulation (Figure 3Diii). In addition, the timing of the post-ripple negatively modulated glutamate signal in aRSC matched that of the voltage activity in aRSC (Figure 3E), which suggests that one of the factors involved in the reduction of voltage in aRSC could be the reduction of endogenous and/or exogenous excitatory glutamatergic input to the region. Also, the onset time of the positively modulated glutamate signal in aRSC was earlier than that of the original signal, which matches better with the timing of aRSC neurons’ calcium activity presented in Figure 3B. Lastly, the ensemble-wise correlation coefficients averaged across iGluSnFR-Ras animals revealed a period (~0–100 ms) of enhanced coordination between hippocampal MUA and negatively modulated (but not positively modulated) aRSC glutamate activity (reconstructed from components 2–100) which was absent for sensory regions (Figure 3 Div-vii; Figure 3—figure supplement 2). These results suggest that the glutamate activity could potentially be a multiplex of separate components.

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The peri-ripple glutamatergic activity is layer-dependent

Given different hypothesized functions for superficial and deep layers in association with cortices (Burke et al., 2005; McNaughton, 2010), we asked whether the patterns of peri-ripple glutamate activity are layer-dependent. To address this question, we conducted wide-field optical imaging with concurrent CA1 LFP/MUA recording in EMX iGluSnFR mice with glutamate indicators expressed in excitatory neurons across all the neocortical layers (as opposed to Ras mice with only superficial layer expression). Qualitatively, the mean peri-ripple glutamate activity and ensemble-wise correlation coefficients in EMX mice (Figure 4A–B) did not differ from that in Ras mice (Figure 2Aii, C). However, the activity in EMX mice seemed to be shifted to an earlier time (compare Figure 2Ciii and Figure 4Biii). To probe the potential differences in glutamate activity in Ras and EMX mice, we compared the result of the singular value decomposition (SVD) analysis. The spatial and temporal modes associated with different components were similar in these two strains. Moreover, the reconstructed signals using first and 2–100 components showed the same pattern of positive and negative modulations, respectively (Figure 4Ci–ii). The ensemble-wise correlation coefficients averaged across iGluSnFR-EMX animals revealed a period (~0–100 ms) of enhanced coordination between hippocampal MUA and negatively modulated (but not positively modulated) aRSC glutamate activity (reconstructed from components 2–100) which was absent for sensory regions (Figure 4Ciii–vi; Figure 4—figure supplement 1).

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Moreover, we did not find a statistically significant difference in the amplitude and slope of neither positively nor negatively modulated signals between Ras and EMX groups. Additionally, although the animal-wise average of positively modulated aRSC signal showed earlier onset time (Figure 4Di), the statistical comparison between the two groups did not reach the significance threshold of 0.05 (Figure 4Dii). However, the onset time of negatively modulated signal in aRSC was significantly earlier in EMX than that in Ras group (Figure 4E). We also estimated the glutamate activity of the deep layers of aRSC by subtracting a scaled version of the Ras from the EMX signal (the black trace in Figure 4D–Ei; b $\mathrm{l}\mathrm{a}\mathrm{c}\mathrm{k}\mathrm{}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{e}\mathrm{}=\mathrm{}\frac{\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{e}\mathrm{}–\mathrm{}0.5\mathrm{*}\mathrm{b}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{e}}{0.5}$). The scaling was performed to accommodate for the potential amount of variance in the superficial and deep layers explained in the EMX signal (Newton et al., 2021). The estimated positively and negatively modulated glutamate signals from the deep layers of aRSC showed a shift to an earlier time (to the left). Furthermore, the estimated negatively modulated signal (black trace in Figure 4Ei) showed a noticeable rebound with a peak around 200 ms after the ripple centers. All in all, these results suggest that deep neocortical layers in aRSC receive glutamatergic modulation earlier than superficial layers do.

In this study, we investigated the peri-ripple activity of the neocortex during the awake state. We utilized cell-type-specific glutamate, voltage, and calcium imaging to characterize the interactions of glutamate fluctuations, membrane potential, and calcium activity (as a proxy of the spiking activity) in excitatory neurons in several neocortical regions, especially in aRSC. In this context, synaptic input (glutamate) and membrane voltage fluctuations are considered from the perspective of the dendritic trees of either layer II-III (Ras) or layer II-III and V (EMX), occupying the majority of the volume of the superficial layers of the neocortex (Chemla and Chavane, 2010b; Chemla and Chavane, 2010a). Thus, even self-excitation (i.e. the excitation of the dendrites of a region by the axonal projections originating from the same region) is seen as input in this framework. Output is considered from the perspective of spiking-related Ca2⁺ activity in the superficial pyramidal cells.

A major observation was a reduction in post-awake-SWR membrane voltage of pyramidal cells with the strongest and fastest hyperpolarization in aRSC. The reduction of membrane voltage could be due to a reduction of the glutamatergic drive, an increase in gabaergic inhibition, or occurrence of a local awake-down-state after a portion of ripples (Luczak et al., 2013; McGinley et al., 2015; Petersen et al., 2003). We monitored the glutamate delivered to pyramidal cells to test for the first possibility. The mean peri-awake-SWR glutamate concentration did not show a reduction and instead showed a delayed elevation. Three possibilities could explain the delayed elevation of the glutamate signal in aRSC: (1) glutamate is released from pre-synaptic terminals with a delay with respect to the ripple times, (2) glutamate is pre-synaptically released on time but post-synaptically sensed with a delay with respect to the ripple times (~75 ms; Figure 3Aii–iii), and (3) glutamate is released and sensed with no delay from pre- and post-synaptic, respectively, but the glutamate signal is a mixture (e.g. additive) of components with different patterns of modulation. The first possibility is unlikely given the observed pre-ripple elevation of calcium transient in a proportion of aRSC neurons (Figure 3B). The second option is likely but with a delay in the order of ~15 ms not ~75 ms as the rise-to-half-peak-time of the iGluSnFR sensor is ~15 ms (Marvin et al., 2013). Therefore, the third option is the most probable one given that the glutamate activity, imaged in the regions other than aRSC, begins to elevate during or before the ripple times (Figure 2Ciii; Figure 2—figure supplement 3C), and thus the same phenomenon could occur in aRSC as well.

To test whether the third option could be realized in the brain, SVD analysis was performed, and it revealed two patterns of modulation, that is, negatively and positively modulated components. The motivation for investigating the idea of multiplexed glutamate transmission was further fueled by the fact that a significant subpopulation of neurons in aRSC increased their calcium activity despite the significant reduction in voltage as well as seemingly delayed (relative to the SWR centers) glutamate activity in the region. SVD decomposed the potentially multiplexed glutamate signal in aRSC and recovered two main patterns of modulation. Notably, the timing of these two patterns matched those of voltage reduction and neural firing (measured by calcium activity) in aRSC. Hence, the results of the SVD analysis provide proof of principle for the feasibility of the third option. It is worthy of note that SVD is agnostic to excitation and inhibition, and it simply captures the maximum amount of variance in the data. Thus, the two patterns of modulations, which resulted from the application of SVD, may or may not reflect brain mechanisms. However, the coincidence of the timing of these two patterns with those of the voltage reduction and calcium activity in aRSC suggests that the SVD output, to some extent, reflects truly different neuronal processes (e.g. see Yamawaki et al., 2019a).

Another possible factor in post-awake-ripple voltage reduction could be that a proportion of hippocampal ripples occurs before neocortical awake down-states. To test this hypothesis, we triggered the ripple power signal by the troughs (as proxies of the awake down-states) and peaks (as proxies of the awake up-states) of the voltage signals, captured from different neocortical regions, during periods of high ripple activity when the probability of neocortical synchronization is highest (McGinley et al., 2015; Nitzan et al., 2020). According to this analysis, the ripple power was, on average, highest before troughs of aRSC voltage signal than before those of other regions (Figure 2—figure supplement 4). On the other hand, the ripple power, on average, was not higher after the peaks of aRSC voltage signal than after those of other regions (Figure 2—figure supplement 4). This observation supports the hypothesis that a local awake down-state could occur in aRSC after occurrence of a portion of hippocampal ripples. However, a recent work (Chambers et al., 2022) reported that, out of 33 aRSC neurons whose membrane potentials were recorded, only 1 showed up-/down-states transitions (bimodal membrane potential distribution). Still, a portion (10 out of 30) of the remaining unimodal neurons showed an abrupt post-ripple hyperpolarization. In addition, they reported a modest post-ripple modulation of aRSC neurons’ membrane potential (~20% of the up/down-states transition range). Hence, these results suggest that the post-ripple aRSC hyperpolarization is not necessarily the result of the awake-down-states in aRSC.

The elevated correlation between the peri-ripple hippocampal MUA and aRSC voltage activity, seen in the region (0, 100) × (0, 100) ms of the correlation function (Figure 2Biv), was an interesting observation. One possible way of interpreting this result comes as follows. Let x₁(t) and x₂(t) be two random processes (RPs) with mean functions m₁(t) and m₂(t), representing the peri-ripple activity in two brain areas. Note that mean functions are not RPs. It could be assumed that x₁(t) and x₂(t) would be modeled as x₁(t)=m₁(t)+e₁(t) and x₂1=m₂(t)+e₂(t) where e₁ and e₂ are two RPs with zero mean at any time point, capturing the ripple-to-ripple variability of the peri-ripple activity in the respective region. For any given time point 2=t₀, x₁(t₀), x₂(t₀), e₁(t₀), and e₂(t₀) are random variables, and therefore, it makes sense to calculate the correlation coefficient between x₁(t₁) and x₂(t₂) for any pair of time points (t₁, t₂). It is straightforward to check that the correlation coefficient of x₁(t₁) and x₂(t₂), which is called ensemble-wise correlation at (t₁, t₂) in this work, is the same as that of e₁(t₁) and e₂(t₂). Therefore, the ensemble-wise correlation represents the statistical relationship between the ripple-to-ripple variability in the peri-ripple activity of two brain areas. That is why it is sometimes called “noise correlation” (Abbott and Dayan, 1999; Zohary et al., 1994). The existence of a significant noise correlation between peri-ripple activities of two brain areas can stem from different underlying factors including direct synaptic interactions between the two areas, having a common input from a third area (Shadlen and Newsome, 1998), and the influence of the brain global fluctuations (Ecker et al., 2014) like the slow-wave activity.

Our results show that (1) there is a significant noise correlation in the region (0, 100) × (0, 100) ms of the correlation function between the peri-ripple hippocampal MUA and aRSC (and not the other imaged neocortical regions’) voltage activity; (2) the correlation function peaks around the point (75, 75) ms (Figure 2Biv) when the mean activity in both aRSC and HPC MUA is decreasing (Figure 2Biii). Hence, it is plausible that a common source (e.g. the inhibitory circuitry of the hippocampus) underlies the reduction of activity in the two regions. If it was the case, it would be expected to have symmetry around the diagonal of the correlation function. It is because the diagonal of the correlation function represents the noise correlation between the two areas at the points (t₀, t₀) for all t₀. Thus, a skewness around the diagonal could be interpreted as a lead/lag relation between the activities of the two areas. To check this idea, we calculated an asymmetry index (Stringer et al., 2019). This analysis revealed that HPC MUA precedes aRSC voltage activity by <70 ms in the region (0, 100) × (0, 100) ms of the correlation function (Figure 2—figure supplement 7B).

Since the observed correlation asymmetry could be due to the fact that the temporal resolution of the voltage signal is lower than that of MUA, this observation does not necessarily vote against the common input hypothesis discussed above. In addition, this result cannot necessarily be interpreted as that the hippocampus drives the reductions of aRSC voltage activity. On the other hand, as we reported an elevation of EMG activity after a portion of ripples (Figure 1—figure supplement 1), the inputs from the motion-generating/planning areas could also contribute to the correlated variability between peri-ripple HPC MUA and aRSC voltage activity.

Given the presence of slow-wave activity during sleep that coordinates hippocampal-neocortical interactions, we asked whether there is a significant noise correlation between HPC MUA and aRSC voltage activity under a sleep-like state (i.e. urethane anesthesia). Interestingly, the ensemble-wise correlation analysis did not reveal a significant correlation between the two signals (Figure 2—figure supplement 1C). The lack of noise correlation between the two signals under a sleep-like state could be due to the absence of deliberate motion under anesthesia/sleep, different hippocampal-neocortical interactions during sleep versus wakefulness, or distinct neuromodulation of the hippocampal-neocortical system by modulatory systems in awake versus sleep states.

Even though the peri-awake-SWR glutamate signal was potentially found to be a mixture of rising and falling components, the amplitude (explained variance) of the rising component was larger than that of the falling component. This suggests that the reduction of the glutamate activity in aRSC is not the only factor contributing to the reduction of the voltage signal in the region. Therefore, by exclusion, we suggest that inhibitory input to the superficial layers of aRSC plays a significant role in the reduction of the voltage signal. Moreover, since the majority of the volume of the neocortical superficial layers is filled with dendritic trees (Chemla and Chavane, 2010b; Chemla and Chavane, 2010a), dendritic inhibition is probably the major inhibitory process reflected in the reduction of peri-awake-SWR voltage activity.

Another interesting result that came out of the application of SVD on the peri-awake-SWR glutamate activity was the observation that the negatively modulated component was present only in aRSC and not in other recorded regions, while the positively modulated component was present in all the recorded regions. This observation supports the hypothesis of the presence of peri-awake-SWR inhibition in the neocortex, because, even though the recorded neocortical regions, except aRSC, did not have a negatively modulated glutamate activity component, they still showed a reduction in their voltage activity.

Peri-ripple modulation of multiple brain regions, such as mediodorsal thalamic nucleus (Yang et al., 2019), ventral tegmental area (Gomperts et al., 2015), subiculum (Chrobak and Buzsáki, 1994; Nitzan et al., 2020), medial prefrontal (Jadhav et al., 2016; Peyrache et al., 2009; Siapas and Wilson, 1998), anterior cingulate (Wang and Ikemoto, 2016), retrosplenial (Alexander et al., 2018), and entorhinal cortices Isomura et al., 2006 have been observed. Moreover, our data show that multiple neocortical regions, such as the auditory and barrel cortices, express glutamate elevation before aRSC does. Since the majority of these regions project to aRSC, it is plausible that a peri-awake-SWR excitatory input to aRSC comes indirectly from the hippocampus, or even independently from the hippocampus, through these intermediate neocortical regions. In addition, because some aRSC neurons appear to start firing before the SWR peaks, it is also plausible that self-excitation may occur in aRSC. In addition, as awake SWRs are involved in planning, and planning could lead to the initiation of movement (Diba and Buzsáki, 2007), another potential source of excitation in aRSC could stem from the subcortical structures involved in motion generation. This idea is in line with the observation that an increased EMG tone was detected right after some of the ripples in this study (Figure 1—figure supplement 1).

According to the current literature, there are at least two potential sources for the peri-ripple inhibitory inputs to aRSC. One possibility is long-range inhibitory projections emanating from CA1. Although a majority of tracing studies have been focused on the presence of such projections in the granular RSC (gRSC) (Jinno et al., 2007; Miyashita and Rockland, 2007; Yamawaki et al., 2019b), it is plausible that they also exist in the agranular RSC (aRSC). The second option is feed-forward inhibition originated from CA1 or gRSC. This mechanism has been reported in gRSC where peri-ripple hippocampal excitatory input activates, via subiculum, the inhibitory interneurons in gRSC, which leads to suppression of firing of many pyramidal neurons (Nitzan et al., 2020; Opalka et al., 2020). Similarly, a recent work found that inhibitory interneurons in superficial layers of aRSC increase their firing during awake ripples which, in turn, could suppress the activity of some excitatory neurons (Chambers et al., 2022).

Thalamus is another source of axonal projections to aRSC (Van Groen and Wyss, 1992b). However, it is less likely that thalamic projections contribute to the peri-awake-ripple aRSC activity modulation because multiple studies have observed the suppression of the majority of thalamic neurons during awake ripples (Logothetis et al., 2012; Nitzan et al., 2022; Yang et al., 2019). Moreover, peri-awake-ripple suppression of thalamic axons projecting to the first layer of aRSC is reported (Chambers et al., 2022).

Even though the elevation of neuronal firing is dominant around sleep SWRs, suppression of neuronal firing seems to be a predominant pattern of neuronal modulation around awake ripples. For instance, suppression of neuronal firing around awake hippocampal ripples has been reported in the medial prefrontal cortex (Jadhav et al., 2016) and gRSC (Nitzan et al., 2022). Both of these regions are heavily involved in mnemonic processing, especially through replaying/reactivating the neural traces associated with previous experiences. Given that the fidelity of awake replays is higher than that of sleep ones (Karlsson and Frank, 2009; Tang et al., 2017), it could be reasonably speculated that the peri-ripple inhibition plays a role in this higher fidelity. It probably does so via increasing the signal-to-noise ratio by suppressing the interference of the non-mnemonic neuronal populations while the mnemonic representations are being replayed during ripples (Jadhav et al., 2016). Feed-forward inhibition is commonly believed to provide a normalization operation in memory retrieval, restricting pyramidal cell output to those cells whose weight vectors best match the input vector (McNaughton and Morris, 1987).

Our results suggest that dendrites of deep pyramidal neurons, arborized in the superficial layers of the neocortex, receive glutamatergic modulation earlier than those of the superficial ones. However, the results do not provide a mechanistic explanation of the phenomenon. It is possible that the observed layer-dependency of the glutamatergic modulation would partially result from the heterogeneity of the excitatory as well as inhibitory neurons across aRSC layers. But, the question is how this heterogeneity may lead to the above-mentioned layer-dependency to which our data does not provide an answer. It could be speculated that the difference in the dendritic morphology and firing type of different types of RSC excitatory neurons (Yousuf et al., 2020) or the difference in connectivity of different RSC layers with other brain regions would play a role (Sugar et al., 2011; Van Groen and Wyss, 1992a; Whitesell et al., 2021). This is a complicated problem and could only be resolved by conducting experiments specifically designed to address this problem.

The data used to obtain the results of this article have been deposited on Dryad and can be reached via https://doi.org/10.5061/dryad.8kprr4xrk.

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Author details

1. Javad Karimi Abadchi

   Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4175-7598

2. Zahra Rezaei

   Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Thomas Knöpfel

   1. Laboratory for Neuronal Circuit Dynamics, Imperial College London, London, United Kingdom
   2. Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

   Contribution

   Writing – review and editing, experimental models

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5718-0765

4. Bruce L McNaughton

   1. Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada
   2. Department of Neurobiology and Behavior, University of California, Irvine, United States

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

5. Majid H Mohajerani

   Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – review and editing

   For correspondence

   mohajerani@uleth.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0964-2977

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