Disruption of awake sharp-wave ripples does not affect memorization of locations in repeated-acquisition spatial memory tasks

Article activity feed

1. 

   Version published to 10.7554/elife.84004 on eLife

   Mar 26, 2024
2. 

   eLife

   Feb 27, 2024

   Author Response

   Reviewer #2 (Public Review):

   This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training …

   Author Response

   Reviewer #2 (Public Review):

   This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training phase, the animals were allowed to learn the maze structure and the rules governing these tasks for all these behavioral paradigms. Then, awake sharp-SWRs were disrupted as the animal performed these tasks (both during instruction and test phases) via an online detection system combined with closed-loop electrical stimulation of the ventral hippocampal commissure. Notably, this manipulation appeared not to affect performance in all three tasks, as determined using various behavioral parameters. Trials with no stimulation or delayed stimulation serve as controls. Thus, the authors conclude that awake SWRs are not involved in these short-term memory-guided behaviors. I do have a few comments that the authors should discuss or address:

   (1) This study adds to a large number of studies investigating the role of awake SWRs in spatial learning and memory tasks. The results of these previous studies are quite contradictory and range from awake SWRs are not crucial in guiding decisions at all to SWRs are only essential during task rule learning to SWRs do guide behavior. Could the authors comment on these seemingly contradictory results? Why are these experiments now the right ones?

   The reviewer is correct that there is a large body of literature investigating awake SWRs. Most commonly, interpretations about the role of SWRs and associated replay are made based on correlations of their occurrence with behavior. These correlations do, however, not necessarily indicate that SWRs contribute to a particular cognitive process. That is why interventional studies like ours are important to clarify the contribution of SWRs.

   The acquisition of a novel task involves a number of cognitive processes, including short- and long-term memory, building a map of the environment, exploration of the solution space and incorporating (non-)rewarding feedback. Based on available evidence, SWRs could contribute to many of these processes. Our experiments were designed to exclude the long-term memory aspect and focus on the memorization of locations on a short time-scale which as we now demonstrate is not dependent on SWRs. Since the use of short-term spatial memory is one of the possible explanations for the learning deficit seen by Jadhav et al. (2012) following SWR disruption in an alternation task, our results may also narrow down the exact contribution of SWR in these studies.

   (2) None of the experiments presented here test the role of replay. I suggest making this distinction in the paper and the title clear. As the results are presented now, is it possible that the SWR content is not affected sufficiently to have a behavioral effect or that there is a bias towards detecting specific SWRs, e.g., longer SWRs?

   The reviewer is right that our experiments do not say anything about replay directly. We adapted the text to make this distinction clear.

   We address the possibility that SWR content may not be disrupted sufficiently to cause a behavioral effect in response to recommendation 1.

   Reviewer #3 (Public Review):

   In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested below. The manuscript would also be enriched if the authors suggested alternative mechanisms for memory rehearsal. Finally, some claims of "we are first" seem inappropriate when compared to the previous literature.

   Major comments:

   How does one define short-term memory (STM) in rodents? The examples and papers cited in the first paragraphs refer mostly to human working memory tasks, from which it is known that a non- rehearsed STM lasts typically 20-30 seconds. Could the authors mention how this concept is translated to rodents? Could you clarify until what point memory is considered STM and what is the criteria to consider it has turned into long-term memory or when is it simply working memory or habit/skill?

   We agree with the reviewer that the definition of short-term memory is fluid and may differ between researchers and model systems. To avoid confusion, we reframed our study in a different context and hope that this makes the timeframes we are talking about clearer.

   Further, why should these tasks be classified as testing STM while Jadhav et al. tasks are working memory or as they now mention in this article rule learning?

   Note that short-term memory and working memory are closely related, but not identical, concepts. Whereas short-term memory refers to the retaining of information for a short period of time, working memory is generally considered to also include some manipulation of that information. Unfortunately, in the rodent literature, (spatial) working memory and short-term memory are often used interchangeably.

   Many (animal) spatial memory tasks do not test a single cognitive faculty, but likely involve a combination of short-term memory, working memory, and rule learning (among other abilities) to acquire or solve the task. As such, an unequivocal classification of behavioral tasks is not generally possible. For example, in the continuous version of the spatial alternation task used in Jadhav et al., animals may learn the rule “if I in the center arm and I came from the left goal arm, then I will next find reward in the right goal arm”. The execution of this rule would require maintaining in (short-term) memory the most recent visited goal arm. Alternatively, animals may learn the rule to turn left twice and right twice to successfully perform the task.

   One of our goals in our study was to attempt to isolate rule learning components and short-term memory components in our tasks (to be clear: we are not claiming that our tasks are pure short- term memory tasks).

   We have rewritten the introduction to reframe our study, which hopefully clarifies the points above.

   In humans, the retention of memory after a certain time is achieved by retrieving a long-term memory. How do we know if the considerable training the rats received has not allowed the use of a long-term memory strategy which allows the rats to perform well even in the absence of rehearsal (replay)? These are conceptual explanations that would help understand the key concept of STM in greater detail.

   Our experiments aimed to distinguish between the process of learning general task rules through training and the need to retain information specific to each trial or session. For example, in the NMTS task, the animals may have a long-term memory of the overall task design, but they cannot anticipate or recall in advance which specific arms will be baited in the instruction phase since they vary from one trial to another. Therefore, to complete a trial successfully, the animals must have formed some type of (short-term) memory of the instruction arms and/or of the arms that still need to be visited in the test phase. Although extended training may have resulted in a more optimized and less demanding strategy to memorize the necessary information, evidence in the literature indicates that even then (for this particular task), a functional hippocampus is required (Sasaki 2021). The question we address in our experiments is whether hippocampal SWRs (and by association, replay) are instrumental in the formation or maintenance of this memory, whether through rehearsal or other mechanisms. The rewritten introduction explains these concepts more clearly.

   Further, claims of "first" should be adjusted, since I do not see a large difference between the w (m) maze of Jadhav and these tasks. The main difference between the two projects would rather be that Jadhav tests when animals are still newer to the task while here overtrained animals are used. In Jadhav, it's unlikely that just rule learning is affected since the inbound component is not affected by disruption, which also tests rule learning. Therefore, it is still likely that the effect seen in Jadhav et al is a deficit in working memory/short-term memory. And here it is more likely, that no effect was seen since with overtrained animals other strategies (cortical, striatal, etc) were used. The authors should compare in more detail how overtrained animals were in these different projects as well as in the articles they cite for replay analysis.

   The training of the animals on the general task rules prior to SWR disruption manipulations is by design, as it better isolates the short-term memory demands required to solve the task in each trial/session. In our tasks, the rats are required to memorize a randomly chosen combination of goal arms on each day (MTS & SEQ task) or in every trial (NMTS task). Unlike the continuous alternation paradigm used by Jadhav et al. (2012), our tasks can not be solved using a stereotypical or habitual (striatal) strategy that is acquired through extended training. We can not exclude that the rats acquired an optimized and less cognitively demanding strategy that is mainly dependent on cortical structures outside the hippocampus, however evidence in the literature still indicates the requirement for a functional hippocampus (Sasaki, 2021; Okaichi and Oshima 1990; Blokland, Honig, and Raaijmakers, 1992).

   The reviewer is correct that the inbound component of the continuous alternation task in Jadhav et al. (2012) can be considered rule learning and was not affected by SWR disruption. However, we do not believe that this should be generalized to all rule learning and it is very well conceivable that SWRs contribute to the learning of more complex rules that also feature ambiguity (such as the outbound component in the continuous alternation task). We elaborate on these points in the discussion (lines 425-455).

   The main conclusion of the authors is that hippocampal replay is not the rehearsal mechanism expected in STM given that its disruption doesn't lead to behavioral changes. Could the authors hypothesize in their discussion what other neural mechanisms different from hippocampal replay may be involved in this rehearsal?

   Thank you for this suggestion. We added an extra paragraph speculating on this aspect (lines 499- 518).

   The discussion also lacks closure with respect to how the findings fit in the study of STM in human memory. This would make the article more interesting to a larger audience and highlight its translational aspect.

   We agree with the reviewer and added our insight to the discussion.

   The results describe deeply the behavioral performance of the rats and the validation of the ripple detection/disruption system. However, one important aspect missing is how the hippocampal activity and its encoding of space may be affected by the awake ripple disruption. The authors don't cite the work by Roux et al., Nature Neuroscience. 2017 where optogenetic stimulation of hippocampal neurons provided evidence that neuronal activity associated with awake hippocampal ripples during goal-directed behavior is required for both stabilizing and refining hippocampal place fields, while memory performance was not affected during ripple-locked stimulations compared to a ripple-delayed stimulation control (See supplementary Figure 7 of the mentioned article). I would like the authors to comment on their own findings and contrast them with those of Roux et al.

   We agree that it is interesting to include the results of Roux et al. in our discussion (lines 470 and 463-466).

   Line 64: Could the authors clarify what they mean by "indirect" causal evidence when discussing the contribution of papers by Jadhav, Igata, and Fernandez? Is it the fact that rodents' learning speed changed instead of showing a complete absence of learning? Or is it the fact that the disruption/prolongation is done on the hippocampal ripple and not strictly in the replay sequence?

   We apologize for the confusion and rewrote large parts of the introduction to clarify the contributions of the papers by Jadhav, Igata, and Fernandez and the difference with what our manipulations contribute. In the process, we removed the phrase ‘indirect causal evidence’.

   I would also highlight this latter difference, given that the above-mentioned authors describe their methodological approaches in terms of ripples and not in terms of replay content. For example, the use of "replay" instead of "ripple" in Line 61 results in methodological inaccurate terms such as replay disruption and replay prolongation.

   Thank you for pointing this out. We adapted the manuscript to always use ‘ripple’ or ‘sharp-wave ripple’ (SWR) when describing our results.

   Despite its apparent lack of statistical significance, the reported mean ripple detection rate during the trial and non-trial periods tend to be always higher in the disruption condition of all tasks by observing the median of the boxplots in Figure 1J, Figure 2H, and Figure 3J. It is worth investigating this further using the same linear regression method as Girardeau et al. Journal of Neuroscience, 2014 which may reduce the variability and allow comparing slopes of a cumulative number of ripples over time. This may reveal a compensatory homeostatic-like increase in the rate of ripples during the disrupted sessions, which may suggest a need for the ripple/replay occurrence in spite of it not having an effect on the rats' performance during the task.

   The reviewer makes an interesting observation and we appreciate the suggestion for further investigation. However, note that a clear trend for higher ripple rates in disruption trials/sessions is not present when comparing to non-stimulated control trials/session. Part of the variability in the observed ripple rates is likely due to the variability in the animals’ behavioral state (e.g., moving, pausing but alert, grooming, consuming reward) and the corresponding varying propensity for SWRs to occur. The behavioral variability makes application of the linear regression approach of Girardeau et al. (2014) not straightforward (note that Girardeau et al. looked at SWRs during sleep). For these reasons, we have decided to not further look into the potential disruption-induced increase of the SWR rate.

   In line 425, the authors report a median relative delay of 52.9 of their disruption system. Such a value would indicate that only around 47% of the ripple is being blocked. Is there any data from the authors or others that could reassure the reader that the 52.9% of the ripple that "leaks" is not enough for the replay phenomenon to occur? Considering the findings of Fernandez-Ruiz et al. 2019 on large-duration ripples, could the authors report the relative delay for both short and long ripples (>100 ms) separately?

   The reviewer is correct that the initial part (~35 ms) of SWRs remains intact, which is inherent to the online detection and disruption approach. In relative terms, a larger fraction of long SWRs is disrupted. As requested, we have adapted figure 4c to separately show the distribution of relative detection delays for long (duration >100ms) and short SWRs.

   As we and others have shown, the electrical stimulation temporarily suppresses spiking activity in CA1 and thus abruptly interferes with any ongoing replay, but any beginning of replay sequences before the stimulation will not be affected. Previous studies that use the same methodology to disrupt SWRs reported a behavioral performance deficit despite the detection delays (Michon et al. 2019; Girardeau et al. 2009; Jadhav et al. 2012). This suggests that the initial part of SWRs (and replay) is not sufficient to support the behavior. The delays in the current study are quantitatively similar to what we have reported before in Michon et al. (2019) and thus we are confident that we should have been able to observe a behavioral effect if present. We now elaborate on this topic in the Discussion (lines 489-498) .

   Line 494: The authors define long ripples as (>120 ms) but this doesn't coincide with the 100ms threshold from Fernandez Ruiz et al. 2019.

   Thank you for pointing this out, it is corrected in the text both in the Results (line 389) and Discussion (line 486).

   The online ripple detector used filtered the traces in the 135-255 Hz range. This is a narrower frequency range compared to online detectors used by Jadhav et al. 2012 (100-400 Hz) and Fernandez-Ruiz et al. 2019 (80-300 Hz). What motivated the use of this narrow range? Would the omittance of ripples below 135 Hz have implications in the results? Could the authors add to the supplement a figure similar to Figure 4B (FDR vs TPR) using a wider frequency range similar to the authors above in the offline detection of ripples?

   The frequency of hippocampal ripple oscillation in rat generally lies in the range of 160-225 Hz (Buzsaki, 1992). We have added a power spectrum in Figure 1d that confirms this frequency range in our experiments. Filters that include frequencies below this range (as in the studies referenced by the reviewer) likely also pass through high-frequency gamma oscillations, and filters that include frequencies above this range likely also pass through multi-unit spiking activity. The challenge for a real-time ripple detection system is to design a filter that has an acceptable trade-off between filtering in a specific (narrow) frequency range and introducing a long delay. In our study, we specifically designed a filter that is specific to the ripple frequency band and still has an acceptable low delay.

   It is unclear what criterion was used to train the rats in the NMTS task. Line 216 specifies a learning criterion of 80% fully correct trials in one session for three days in a row, while the methods in line 852 mention an average performance below 50% for at least three days in a row.

   Thank you for pointing this out. We corrected the learning criterium description in the results section (lines 108-110) to match the description in the Methods section.

   In the methods section, it is not mentioned if there was a specific region in the cortex where the tetrode was placed (Line 908).

   The detections in this tetrode were used to mark events as "false positives". The authors should be careful in line 933 when they make the statement "ripples are not present in the cortex". There have been recent publications that challenge this affirmation. See Khodagholy, Science. 2017, Nitzan, Nature Comm. 2020.

   Thank you for pointing this out. We have added the cortical region in the methods (line 882) and clarified that, as far as we know, no ripples in that part of the cortex (parietal associate cortex) have been described that are synchronous with hippocampal ripples.

   Read the original source
3. 

   eLife

   Feb 25, 2024

   eLife assessment

   This manuscript presents the lack of effect of closed-loop SWR disruption in guiding behavior and remembering the recent past in short-term memory tasks in rats. These negative results may have important theoretical and practical implications in the field of memory and learning. However, while SWR detection methods are carefully validated, the strength of evidence is incomplete and some additional controls are required.

   Read the original source
4. 

   eLife

   Feb 25, 2024

   Reviewer #1 (Public Review):

   This manuscript describes the results of closed-loop SWR disruption in rats experiencing a short-term memory task that they previously acquired successfully. The authors aim to show that SWRs are dispensable for STM tasks requiring multiple match-to-sample trial rules, single-trial non-match-to-sample rules, and spatial sequence memory. In all cases, the analysis and intervention were performed at the higher standards, providing a clear proof-of-principle of appropriate detection and the necessary control. I found the experiments well executed and analyzed. Results may help to advance our understanding of the role of awake SWRs in STM. However, since the results consist of a lack of evidence there is a need for some additional positive controls to fully support the main claim of the study.

   Read the original source
5. 

   eLife

   Feb 25, 2024

   Reviewer #2 (Public Review):

   This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training phase, the animals …

   Reviewer #2 (Public Review):

   This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training phase, the animals were allowed to learn the maze structure and the rules governing these tasks for all these behavioral paradigms. Then, awake sharp-SWRs were disrupted as the animal performed these tasks (both during instruction and test phases) via an online detection system combined with closed-loop electrical stimulation of the ventral hippocampal commissure. Notably, this manipulation appeared not to affect performance in all three tasks, as determined using various behavioral parameters. Trials with no stimulation or delayed stimulation serve as controls. Thus, the authors conclude that awake SWRs are not involved in these short-term memory-guided behaviors. I do have a few comments that the authors should discuss or address:

   (1) This study adds to a large number of studies investigating the role of awake SWRs in spatial learning and memory tasks. The results of these previous studies are quite contradictory and range from awake SWRs are not crucial in guiding decisions at all to SWRs are only essential during task rule learning to SWRs do guide behavior. Could the authors comment on these seemingly contradictory results? Why are these experiments now the right ones?
   (2) None of the experiments presented here test the role of replay. I suggest making this distinction in the paper and the title clear. As the results are presented now, is it possible that the SWR content is not affected sufficiently to have a behavioral effect or that there is a bias towards detecting specific SWRs, e.g., longer SWRs?

   Read the original source
6. 

   eLife

   Feb 25, 2024

   Reviewer #3 (Public Review):

   In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested …

   Reviewer #3 (Public Review):

   In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested below. The manuscript would also be enriched if the authors suggested alternative mechanisms for memory rehearsal. Finally, some claims of "we are first" seem inappropriate when compared to the previous literature.

   Major comments:

   How does one define short-term memory (STM) in rodents? The examples and papers cited in the first paragraphs refer mostly to human working memory tasks, from which it is known that a non-rehearsed STM lasts typically 20-30 seconds. Could the authors mention how this concept is translated to rodents? Could you clarify until what point memory is considered STM and what is the criteria to consider it has turned into long-term memory or when is it simply working memory or habit/skill? Further, why should these tasks be classified as testing STM while Jadhav et al. tasks are working memory or as they now mention in this article rule learning? In humans, the retention of memory after a certain time is achieved by retrieving a long-term memory. How do we know if the considerable training the rats received has not allowed the use of a long-term memory strategy which allows the rats to perform well even in the absence of rehearsal (replay)? These are conceptual explanations that would help understand the key concept of STM in greater detail.

   Further, claims of "first" should be adjusted, since I do not see a large difference between the w (m) maze of Jadhav and these tasks. The main difference between the two projects would rather be that Jadhav tests when animals are still newer to the task while here overtrained animals are used. In Jadhav, it's unlikely that just rule learning is affected since the inbound component is not affected by disruption, which also tests rule learning. Therefore, it is still likely that the effect seen in Jadhav et al is a deficit in working memory/short-term memory. And here it is more likely, that no effect was seen since with overtrained animals other strategies (cortical, striatal, etc) were used. The authors should compare in more detail how overtrained animals were in these different projects as well as in the articles they cite for replay analysis.

   The main conclusion of the authors is that hippocampal replay is not the rehearsal mechanism expected in STM given that its disruption doesn't lead to behavioral changes. Could the authors hypothesize in their discussion what other neural mechanisms different from hippocampal replay may be involved in this rehearsal? The discussion also lacks closure with respect to how the findings fit in the study of STM in human memory. This would make the article more interesting to a larger audience and highlight its translational aspect.

   The results describe deeply the behavioral performance of the rats and the validation of the ripple detection/disruption system. However, one important aspect missing is how the hippocampal activity and its encoding of space may be affected by the awake ripple disruption. The authors don't cite the work by Roux et al., Nature Neuroscience. 2017 where optogenetic stimulation of hippocampal neurons provided evidence that neuronal activity associated with awake hippocampal ripples during goal-directed behavior is required for both stabilizing and refining hippocampal place fields, while memory performance was not affected during ripple-locked stimulations compared to a ripple-delayed stimulation control (See supplementary Figure 7 of the mentioned article). I would like the authors to comment on their own findings and contrast them with those of Roux et al.

   Line 64: Could the authors clarify what they mean by "indirect" causal evidence when discussing the contribution of papers by Jadhav, Igata, and Fernandez? Is it the fact that rodents' learning speed changed instead of showing a complete absence of learning? Or is it the fact that the disruption/prolongation is done on the hippocampal ripple and not strictly in the replay sequence? I would also highlight this latter difference, given that the above-mentioned authors describe their methodological approaches in terms of ripples and not in terms of replay content. For example, the use of "replay" instead of "ripple" in Line 61 results in methodological inaccurate terms such as replay disruption and replay prolongation.

   Despite its apparent lack of statistical significance, the reported mean ripple detection rate during the trial and non-trial periods tend to be always higher in the disruption condition of all tasks by observing the median of the boxplots in Figure 1J, Figure 2H, and Figure 3J. It is worth investigating this further using the same linear regression method as Girardeau et al. Journal of Neuroscience, 2014 which may reduce the variability and allow comparing slopes of a cumulative number of ripples over time. This may reveal a compensatory homeostatic-like increase in the rate of ripples during the disrupted sessions, which may suggest a need for the ripple/replay occurrence in spite of it not having an effect on the rats' performance during the task.

   In line 425, the authors report a median relative delay of 52.9 of their disruption system. Such a value would indicate that only around 47% of the ripple is being blocked. Is there any data from the authors or others that could reassure the reader that the 52.9% of the ripple that "leaks" is not enough for the replay phenomenon to occur? Considering the findings of Fernandez-Ruiz et al. 2019 on large-duration ripples, could the authors report the relative delay for both short and long ripples (>100 ms) separately? Line 494: The authors define long ripples as (>120 ms) but this doesn't coincide with the 100ms threshold from Fernandez Ruiz et al. 2019.

   The online ripple detector used filtered the traces in the 135-255 Hz range. This is a narrower frequency range compared to online detectors used by Jadhav et al. 2012 (100-400 Hz) and Fernandez-Ruiz et al. 2019 (80-300 Hz). What motivated the use of this narrow range? Would the omittance of ripples below 135 Hz have implications in the results? Could the authors add to the supplement a figure similar to Figure 4B (FDR vs TPR) using a wider frequency range similar to the authors above in the offline detection of ripples?

   It is unclear what criterion was used to train the rats in the NMTS task. Line 216 specifies a learning criterion of 80% fully correct trials in one session for three days in a row, while the methods in line 852 mention an average performance below 50% for at least three days in a row.

   In the methods section, it is not mentioned if there was a specific region in the cortex where the tetrode was placed (Line 908). The detections in this tetrode were used to mark events as "false positives". The authors should be careful in line 933 when they make the statement "ripples are not present in the cortex". There have been recent publications that challenge this affirmation. See Khodagholy, Science. 2017, Nitzan, Nature Comm. 2020.

   Read the original source
7. 

   Version published to 10.1101/2022.11.03.514989v1 on bioRxiv

   Nov 3, 2022