Reviewer #3 (Public Review):

Summary:

This manuscript by Tao et al. reports on an effort to better specify the underlying interactions driving the effects of biodiversity on productivity in biodiversity experiments. The authors are especially concerned with the potential for competitive interactions to drive positive biodiversity-ecosystem functioning relationships by driving down the biomass of subdominant species. The authors suggest a new partitioning schema that utilizes a suite of partial density treatments to capture so-called competitive ability. While I agree with the authors that understanding the underlying drivers of biodiversity-ecosystem functioning relationships is valuable - I am unsure of the added value of this specific approach for several reasons.

Strengths:

I can find a lot of value in endeavouring to improve our understanding of how biodiversity-ecosystem functioning relationships arise. I agree with the authors that competition is not well integrated into the complementarity and selection effect and interrogating this is important.

Weaknesses:

(1) The authors start the introduction very narrowly and do not make clear why it is so important to understand the underlying mechanisms driving biodiversity-ecosystem functioning relationships until the end of the discussion.

(2) The authors criticize the existing framework for only incorporating positive interactions but this is an oversimplification of the existing framework in several ways:<br /> a. The existing partitioning scheme incorporates resource partitioning which is an effect of competition.<br /> b. The authors neglect the potential that negative feedback from species-specific pests and pathogens can also drive positive BEF and complementarity effects but is not a positive interaction, necessarily. This is discussed in Schnitzer et al. 2011, Maron et al. 2011, Hendriks et al. 2013, Barry et al. 2019, etc.<br /> c. Hector and Loreau (and many of the other citations listed) do not limit competition to SE because resource partitioning is a byproduct of competition.

(3) It is unclear how this new measure relates to the selection effect, in particular. I would suggest that the authors add a conceptual figure that shows some scenarios in which this metric would give a different answer than the traditional additive partition. The example that the authors use where a dominant species increases in biomass and the amount that it increases in biomass is greater than the amount of loss from it outcompeting a subdominant species is a general example often used for a selection effect when exactly would you see a difference between the two? :<br /> a. Just a note - I do think you should see a difference between the two if the species suffers from strong intraspecific competition and has therefore low monoculture biomass but this would tend to also be a very low-density monoculture in practice so there would potentially be little difference between a low density and high-density monoculture because the individuals in a high-density monoculture would die anyway. So I am not sure that in practice you would really see this difference even if partial density plots were incorporated.

(4) One of the tricky things about these endeavors is that they often pull on theory from two different subfields and use similar terminology to refer to different things. For example - in competition theory, facilitation often refers to a positive relative interaction index (this seems to be how the authors are interpreting this) while in the BEF world facilitation often refers to a set of concrete physical mechanisms like microclimate amelioration. The truth is that both of these subfields use net effects. The relative interaction index is also a net outcome as is the complementarity effect even if it is only a piece of the net biodiversity effect. Trying to combine these two subfields to come up with a new partitioning mechanism requires interrogating the underlying assumptions of both subfields which I do not see in this paper.

(5) The partial density treatment does not isolate competition in the way that the authors indicate. All of the interactions that the authors discuss are density-dependent including the mechanism that is not discussed (negative feedback from species-specific pests and pathogens). These partial density treatment effects therefore cannot simply be equated to competition as the authors indicate.:<br /> a. Additionally - the authors use mixture biomass as a stand-in for competitive ability in some cases but mixture biomass could also be determined by the degree to which a plant is facilitated in the mixture (for example).

(6) I found the literature citation to be a bit loose. For example, the authors state that the additive partition is used to separate positive interactions from competition (lines 70-76) and cite many papers but several of these (e.g. Barry et al. 2019) explicitly do not say this.

(7) The natural take-home message from this study is that it would be valuable for biodiversity experiments to include partial density treatments but I have a hard time seeing this as a valuable addition to the field for two reasons:<br /> a. In practice - adding in partial density treatments would not be feasible for the vast majority of experiments which are already often unfeasibly large to maintain.<br /> b. The density effect would likely only be valuable during the establishment phase of the experiment because species that are strongly limited by intraspecific competition will die in the full-density plots resulting in low-density monocultures. You can see this in many biodiversity experiments after the first years. Even though they are seeded (or rarely planted) at a certain density, the density after several years in many monocultures is quite low.

Reviewer #4 (Public Review):

Summary:

This manuscript claims to provide a new null hypothesis for testing the effects of biodiversity on ecosystem functioning. It reports that the strength of biodiversity effects changes when this different null hypothesis is used. This main result is rather inevitable. That is, one expects a different answer when using a different approach. The question then becomes whether the manuscript's null hypothesis is both new and an improvement on the null hypothesis that has been in use in recent decades.

Strengths:

In general, I appreciate studies like this that question whether we have been doing it all wrong and I encourage consideration of new approaches.

Weaknesses:

Despite many sweeping critiques of previous studies and bold claims of novelty made throughout the manuscript, I was unable to find new insights. The manuscript fails to place the study in the context of the long history of literature on competition and biodiversity and ecosystem functioning. The Introduction claims the new approach will address deficiencies of previous approaches, but after reading further I see no evidence that it addresses the limitations of previous approaches noted in the Introduction. Furthermore, the manuscript does not reproducibly describe the methods used to produce the results (e.g., in Table 1) and relies on simulations, claiming experimental data are not available when many experiments have already tested these ideas and not found support for them. Finally, it is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. I will elaborate on each of these points below.

The critiques of biodiversity experiments and existing additive partitioning methods are overstated, as is the extent to which this new approach addresses its limitations. For example, the critique that current biodiversity experiments cannot reveal the effects of species interactions (e.g., lines 37-39) isn't generally true, but it could be true if stated more specifically. That is, this statement is incorrect as written because comparisons of mixtures, where there are interspecific and intraspecific interactions, with monocultures, where there are only intraspecific interactions, certainly provide information about the effects of species interactions (interspecific interactions). These biodiversity experiments and existing additive partitioning approaches have limits, of course, for identifying the specific types of interactions (e.g., whether mediated by exploitative resource competition, apparent competition, or other types of interactions). However, the approach proposed in this manuscript gets no closer to identifying these specific mechanisms of species interactions. It has no ability to distinguish between resource and apparent competition, for example. Thus, the motivation and framing of the manuscript do not match what it provides. I believe the entire Introduction would need to be rewritten to clarify what gap in knowledge this proposed approach is addressing and what would be gained by filling this knowledge gap.

I recommend that the Introduction instead clarify how this study builds on and goes beyond many decades of literature considering how competition and biodiversity effects depend on density. This large literature is insufficiently addressed in this manuscript. This fails to give credit to previous studies considering these ideas and makes it unclear how this manuscript goes beyond the many previous related studies. For example, see papers and books written by de Wit, Harper, Vandermeer, Connolly, Schmid, and many others. Also, note that many biodiversity experiments have crossed diversity treatments with a density treatment and found no significant effects of density or interactions between density and diversity (e.g., Finn et al. 2013 Journal of Applied Ecology). Thus, claiming that these considerations of density are novel, without giving credit to the enormous number of previous studies considering this, is insufficient.

Replacement series designs emerged as a consensus for biodiversity experiments because they directly test a relevant null hypothesis. This is not to say that there are no other interesting null hypotheses or study designs, but one must acknowledge that many designs and analyses of biodiversity experiments have already been considered. For example, Schmid et al. reviewed these designs and analyses two decades ago (2002, chapter 6 in Loreau et al. 2002 OUP book) and the overwhelming consensus in recent decades has been to use a replacement series and test the corresponding null hypothesis.

It is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. Most biodiversity experiments and additive partitions have tested and quantified diversity effects against the null hypothesis that there is no difference between intraspecific and interspecific interactions. If there was no less competition and no more facilitation in mixtures than in monocultures, then there would be no positive diversity effects. Rejecting this null hypothesis is relevant when considering coexistence in ecology, overyielding in agronomy, and the consequences of biodiversity loss in conservation (e.g., Vandermeer 1981 Bioscience, Loreau 2010 Princeton Monograph). This manuscript proposes a different null hypothesis and it is not yet clear to me how it would be relevant to any of these ongoing discussions of changes in biodiversity.

The claim that all previous methods 'are not capable of quantifying changes in ecosystem productivity by species interactions and species or community level' is incorrect. As noted above, all approaches that compare mixtures, where there are interspecific interactions, to monocultures, where there are no species interactions, do this to some extent. By overstating the limitations of previous approaches, the manuscript fails to clearly identify what unique contribution it is offering, and how this builds on and goes beyond previous work.

The manuscript relies on simulations because it claims that current experiments are unable to test this, given that they have replacement series designs (lines 128-131). There are, however, dozens of experiments where the replacement series was repeated at multiple densities, which would allow a direct test of these ideas. In fact, these ideas have already been tested in these experiments and density effects were found to be nonsignificant (e.g., Finn et al. 2013).

It seems that the authors are primarily interested in trees planted at a fixed density, with no opportunity for changes in density, and thus only changes in the size of individuals (e.g., Fig. 1). In natural and experimental systems, realized density differs from the initial planted density, and survivorship of seedlings can depend on both intraspecific and interspecific interactions. Thus, the constrained conditions under which these ideas are explored in this manuscript seem narrow and far from the more complex reality where density is not fixed.

Additional detailed comments:

It is unclear to me which 'effects' are referred to on line 36. For example, are these diversity effects or just effects of competition? What is the response variable?

The usefulness of the approach is overstated on line 52. All partitioning approaches, including the new one proposed here, give the net result of many types of species interactions and thus cannot 'disentangle underlying mechanisms of species interactions.'

The weaknesses of previous approaches are overstated throughout the manuscript, including in lines 60-61. All approaches provide some, but not all insights. Sweeping statements that previous approaches are not effective, without clarifying what they can and can't do, is unhelpful and incorrect. Also, these statements imply that the approach proposed here addresses the limitations of these previous approaches. I don't yet see how it does so.

The definitions given for the CE and SE on line 71 are incorrect. Competition affects both terms and CE can be negative or have nothing to do with positive interactions, as noted in many of the papers cited.

The proposed approach does not address the limitations noted on lines 73 and 74.

The definition of positive interactions in lines 77 and 78 seems inconsistent with much of the literature, which instead focuses on facilitation or mutualism, rather than competition when describing positive interactions.

Throughout the manuscript, competition is often used interchangeably with resource competition (e.g., line 82) and complementarity is often attributed to resource partitioning (e.g., line 77). This ignores apparent competition and partitioning enemy-free niche space, which has been found to contribute to biodiversity effects in many studies.

In what sense are competitive interactions positive for competitive species (lines 82-83)? By definition, competition is an interaction that has a negative effect. Do you mean that interspecific competition is less than intraspecific competition? I am having a very difficult time following the logic.

Results are asserted on lines 93-95, but I cannot find the methods that produced these results. I am unable to evaluate the work without a repeatable description of the methods.

The description of the null hypothesis in the common additive partitioning approach on lines 145-146 is incorrect. In the null case, it does not assume that there are no interspecific interactions, but rather that interspecific and intraspecific interactions are equivalent.

eLife assessment

This valuable manuscript describes evidence of sex differences in specific corticostriatal projections during alcohol consumption, and this is noteworthy given the increasing rates/levels of drinking in females and the liability for Alcohol Use disorder. They provide solid evidence of the lateralisation of the activity of the circuit, but other evidence is incomplete, particularly with regard to its description of the drinking measure and how this relates to intoxication. The analyses of the histology data are not complete, and there are further inconsistencies that make it difficult to reconcile the photometry and behavioral data. The findings will be of partial interest to researchers investigating functional circuitry underlying alcohol-driven behaviors.

Reviewer #1 (Public Review):

Summary:

This paper uses a model of binge alcohol consumption in mice to examine how the behaviour and its control by a pathway between the anterior insular cortex (AIC) to the dorsolateral striatum (DLS) may differ between males and females. Photometry is used to measure the activity of AIC terminals in the DLS when animals are drinking and this activity seems to correspond to drink bouts in males but not females. The effects appear to be lateralized with inputs to the left DLS being of particular interest.

Strengths:

Increasing alcohol intake in females is of concern and the consequences for substance use disorder and brain health are not fully understood, so this is an area that needs further study. The attempt to link fine-grained drinking behaviour with neural activity has the potential to enrich our understanding of the neural basis of behaviour, beyond what can be gleaned from coarser measures of volumes consumed etc.

Weaknesses:

The introduction to the drinking in the dark (DID) paradigm is rather narrow in scope (starting line 47). This would be improved if the authors framed this in the context of other common intermittent access paradigms and gave due credit to important studies and authors that were responsible for the innovation in this area (particularly studies by Wise, 1973 and returned to popular use by Simms et al 2010 and related papers; e.g., Wise RA (1973). Voluntary ethanol intake in rats following exposure to ethanol on various schedules. Psychopharmacologia 29: 203-210; Simms, J., Bito-Onon, J., Chatterjee, S. et al. Long-Evans Rats Acquire Operant Self-Administration of 20% Ethanol Without Sucrose Fading. Neuropsychopharmacol 35, 1453-1463 (2010).) The original drinking in the dark demonstrations should also be referenced (Rhodes et al., 2005). Line 154 Theile & Navarro 2014 is a review and not the original demonstration.

When sex differences in alcohol intake are described, more care should be taken to be clear about whether this is in terms of volume (e.g. ml) or blood alcohol levels (BAC, or at least g/kg as a proxy measure). This distinction was often lost when lick responses were being considered. If licking is similar (assuming a single lick from a male and female brings in a similar volume?), this might mean males and females consume similar volumes, but females due to their smaller size would become more intoxicated so the implications of these details need far closer consideration. What is described as identical in one measure, is not in another.

No conclusions regarding the photometry results can be drawn based on the histology provided. Localization and quantification of viral expression are required at a minimum to verify the efficacy of the dual virus approach (the panel in Supplementary Figure 1 is very small and doesn't allow terminals to be seen, and there is no quantification). Whether these might differ by sex is also necessary before we can be confident about any sex differences in neural activity.

While the authors have some previous data on the AIC to DLS pathway, there are many brain regions and pathways impacted by alcohol and so the focus on this one in particular was not strongly justified. Since photometry is really an observational method, it's important to note that no causal link between activity in the pathway and drinking has been established here.

It would be helpful if the authors could further explain whether their modified lickometers actually measure individual licks. While in some systems contact with the tongue closes a circuit which is recorded, the interruption of a photobeam was used here. It's not clear to me whether the nose close to the spout would be sufficient to interrupt that beam, or whether a tongue protrusion is required. This detail is important for understanding how the photometry data is linked to behaviour. The temporal resolution of the GCaMP signal is likely not good enough to capture individual links but I think more caution or detail in the discussion of the correspondence of these events is required.

Even if the pattern of drinking differs between males and females, the use of the word "strategy" implies a cognitive process that was never described or measured.

Reviewer #3 (Public Review):

Summary:

In this manuscript by Haggerty and Atwood, the authors use a repeated binge drinking paradigm to assess how water and ethanol intake changes in male in female mice as well as measure changes in anterior insular cortex to dorsolateral striatum terminal activity using fiber photometry. They find that overall, males and females have similar overall water and ethanol intake, but females appear to be more efficient alcohol drinkers. Using fiber photometry, they show that the anterior insular cortex (AIC) to dorsolateral striatum projections (DLS) projections have sex, fluid, and lateralization differences. The male left circuit was most robust when aligned to ethanol drinking, and water was somewhat less robust. Male right, and female and left and right, had essentially no change in photometry activity. To some degree, the changes in terminal activity appear to be related to fluid exposure over time, as well as within-session differences in trial-by-trial intake. Overall, the authors provide an exhaustive analysis of the behavioral and photometric data, thus providing the scientific community with a rich information set to continue to study this interesting circuit. However, although the analysis is impressive, there are a few inconsistencies regarding specific measures (e.g., AUC, duration of licking) that do not quite fit together across analytic domains. This does not reduce the rigor of the work, but it does somewhat limit the interpretability of the data, at least within the scope of this single manuscript.

Strengths:

- The authors use high-resolution licking data to characterize ingestive behaviors.<br /> - The authors account for a variety of important variables, such as fluid type, brain lateralization, and sex.<br /> - The authors provide a nice discussion on how this data fits with other data, both from their laboratory and others'.<br /> - The lateralization discovery is particularly novel.

Weaknesses:

- The volume of data and number of variables provided makes it difficult to find a cohesive link between data sets. This limits interpretability.<br /> - The authors describe a clear sex difference in the photometry circuit activity. However, I am curious about whether female mice that drink more similarly to males (e.g., less efficiently?) also show increased activity in the left circuit, similar to males. Oppositely, do very efficient males show weaker calcium activity in the circuit? Ultimately, I am curious about how the circuit activity maps to the behaviors described in Figures 1 and 2.<br /> - What does the change in water-drinking calcium imaging across time in males mean? Especially considering that alcohol-related signals do not seem to change much over time, I am not sure what it means to have water drinking change.

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eLife assessment

Here the authors present a useful extension of their previous method to cluster neuronal activity into cell assemblies (groups of neurons with correlated activity). The authors provide solid evidence that this method can identify temporal dynamics of neuronal clusters in sample simulated data, and they show how this method can be applied to whole-brain zebrafish data.

Reviewer #1 (Public Review):

Summary:

Understanding large-scale neural activity remains a formidable challenge in neuroscience. While several methods have been proposed to discover the assemblies from such large-scale recordings, most previous studies do not explicitly model the temporal dynamics. This study is an attempt to uncover the temporal dynamics of assemblies using a tool that has been established in other domains.

The authors previously introduced the compositional Restricted Boltzmann Machine (cRBM) to identify neuron assemblies in zebrafish brain activity. Building upon this, they now employ the Recurrent Temporal Restricted Boltzmann Machine (RTRBM) to elucidate the temporal dynamics within these assemblies. By introducing recurrent connections between hidden units, RTRBM could retrieve neural assemblies and their temporal dynamics from simulated and zebrafish brain data.

Strengths:

The RTRBM has been previously used in other domains. Training in the model has been already established. This study is an application of such a model to neuroscience. Overall, the paper is well-structured and the methodology is robust, the analysis is solid to support the authors' claim.

Weaknesses:

The overall degree of advance is very limited. The performance improvement by RTRBM compared to their cRBM is marginal, and insights into assembly dynamics are limited.

(1) The biological insights from this method are constrained. Though the aim is to unravel neural ensemble dynamics, the paper lacks in-depth discussion on how this method enhances our understanding of zebrafish neural dynamics. For example, the dynamics of assemblies can be analyzed using various tools such as dimensionality reduction methods once we have identified them using cRBM. What information can we gain by knowing the effective recurrent connection between them? It would be more convincing to show this in real data.

(2) Despite the increased complexity of RTRBM over cRBM, performance improvement is minimal. Accuracy enhancements, less than 1% in synthetic and zebrafish data, are underwhelming (Figure 2G and Figure 4B). Predictive performance evaluation on real neural activity would enhance model assessment. Including predicted and measured neural activity traces could aid readers in evaluating model efficacy.

Reviewer #2 (Public Review):

Summary:

In this work, the authors propose an extension to some of the last author's previous work, where a compositional restricted Boltzmann machine was considered as a generative model of neuron-assembly interaction. They augment this model by recurrent connections between the Boltzmann machine's hidden units, which allow them to explicitly account for temporal dynamics of the assembly activity. Since their model formulation does not allow the training towards a compositional phase (as in the previous model), they employ a transfer learning approach according to which they initialise their model with a weight matrix that was pre-trained using the earlier model so as to essentially start the actually training in a compositional phase. Finally, they test this model on synthetic and actual data of whole-brain light-sheet-microscopy recordings of spontaneous activity from the brain of larval zebrafish.

Strengths:

This work introduces a new model for neural assembly activity. Importantly, being able to capture temporal assembly dynamics is an interesting feature that goes beyond many existing models. While this work clearly focuses on the method (or the model) itself, it opens up an avenue for experimental research where it will be interesting to see if one can obtain any biologically meaningful insights considering these temporal dynamics when one is able to, for instance, relate them to development or behaviour.

Weaknesses:

For most of the work, the authors present their RTRBM model as an improvement over the earlier cRBM model. Yet, when considering synthetic data, they actually seem to compare with a "standard" RBM model. This seems odd considering the overall narrative, and it is not clear why they chose to do that. Also, in that case, was the RTRBM model initialised with the cRBM weight matrix?

A few claims made throughout the work are slightly too enthusiastic and not really supported by the data shown. For instance, when the authors refer to the clusters shown in Figure 3D as "spatially localized", this seems like a stretch, specifically in view of clusters 1, 3, and 4. Moreover, when they describe the predictive performance of their model as "close to optimal" when the down-sampling factor coincided with the interaction time scale, it seems a bit exaggerated given that it was more or less as close to the upper bound as it was to the lower bound.

When discussing the data statistics, the authors quote correlation values in the main text. However, these do not match the correlation values in the figure to which they seem to belong. Now, it seems that in the main text, they consider the Pearson correlation, whereas in the corresponding figure, it is the Spearman correlation. This is very confusing, and it is not really clear as to why the authors chose to do so.

Finally, when discussing the fact that the RTRBM model outperforms the cRBM model, the authors state it does so for different moments and in different numbers of cases (fish). It would be very interesting to know whether these are the same fish or always different fish.

Reviewer #3 (Public Review):

With ever-growing datasets, it becomes more challenging to extract useful information from such a large amount of data. For that, developing better dimensionality reduction/clustering methods can be very important to make sense of analyzed data. This is especially true for neuroscience where new experimental advances allow the recording of an unprecedented number of neurons. Here the authors make a step to help with neuronal analyses by proposing a new method to identify groups of neurons with similar activity dynamics. I did not notice any obvious problems with data analyses here, however, the presented manuscript has a few weaknesses:

(1) Because this manuscript is written as an extension of previous work by the same authors (van der Plas et al., eLife, 2023), thus to fully understand this paper it is required to read first the previous paper, as authors often refer to their previous work for details. Similarly, to understand the functional significance of identified here neuronal assemblies, it is needed to go to look at the previous paper.

(2) The problem of discovering clusters in data with temporal dynamics is not unique to neuroscience. Therefore, the authors should also discuss other previously proposed methods and how they compare to the presented here RTRBM method. Similarly, there are other methods using neural networks for discovering clusters (assemblies) (e.g. t-SNE: van der Maaten & Hinton 2008, Hippocluster: Chalmers et al. 2023, etc), which should be discussed to give better background information for the readers.

(3) The above point to better describe other methods is especially important because the performance of the presented here method is not that much better than previous work. For example, RTRBM outperforms the cRBM only on ~4 out of 8 fish datasets. Moreover, as the authors nicely described in the Limitations section this method currently can only work on a single time scale and clusters have to be estimated first with the previous cRBM method. Thus, having an overview of other methods which could be used for similar analyses would be helpful.

not open to disagreement; indisputable: unarguable proof of conspiracy

a person, especially a politician, who strives to appeal to ordinary people who feel that their concerns are disregarded by established elite groups: he ran as a populist on an anti-corruption platform.

a generally accepted theory or belief: conventional wisdom has it that a book should never be judged by its cover

a current score or amount

Reviewer #1 (Public Review):

Summary

A novel statistical model of neural population activity called the Random Projection model has been recently proposed. Not only is this model accurate, efficient, and scalable, but also is naturally implemented as a shallow neural network. This work proposes a new class of RP model called the reshaped RP model. Inheriting the virtue of the original RP model, the proposed model is more accurate and efficient than the original, as well as compatible with various biological constraints. In particular, the authors have demonstrated that normalizing the total synaptic input in the reshaped model has a homeostatic effect on the firing rates of the neurons, resulting in even more efficient representations with equivalent computational accuracy. These results suggest that synaptic normalization contributes to synaptic homeostasis as well as efficiency in neural encoding.

Strengths<br /> This paper demonstrates that the accuracy and efficiency of the random projection models can be improved by extending the model with reshaped projections. Furthermore, it broadens the applicability of the model under biological constraints of synaptic regularization. It also suggests the advantage of the sparse connectivity structure over the fully connected model for modeling spiking statistics. In summary, this work successfully integrates two different elements, statistical modeling of the spikes and synaptic homeostasis in a single biologically plausible neural network model. The authors logically demonstrate their arguments with clear visual presentations and well-structured text, facilitating an unambiguous understanding for readers.

Weaknesses<br /> It would be helpful if the following issues about the major claims of the manuscript could be expanded and/or clarified:

(1) We find it interesting that the reshaped model showed decreased firing rates of the projection neurons. We note that maximizing the entropy <-ln p(x)> with a regularizing term -\lambda <\sum _i f(x_i)>, which reflects the mean firing rate, results in \lambda _i = \lambda for all i in the Boltzmann distribution. In other words, in addition to the homeostatic effect of synaptic normalization which is shown in Figures 3B-D, setting all \lambda_i = 1 itself might have a homeostatic effect on the firing rates. It would be better if the contribution of these two homeostatic effects be separated. One suggestion is to verify the homeostatic effect of synaptic normalization by changing the value of \lambda.

(2) As far as we understand, \theta_i (thresholds of the neurons) are fixed to 1 in the article. Optimizing the neural threshold as well as synaptic weights is a natural procedure (both biologically and engineeringly), and can easily be computed by a similar expression to that of a_ij (equation 3). Do the results still hold when changing \theta _i is allowed as well? For example,

a. If \theta _i becomes larger, the mean firing rates will decrease. Does the backprop model still have higher firing rates than the reshaped model when \theta _i are also optimized?

b. Changing \theta _i affects the dynamic range of the projection neurons, thus could modify the effect of synaptic constraints. In particular, does it affect the performance of the bounded model (relative to the homeostatic input models)?

(3) In Figure 1, the authors claim that the reshaped RP model outperforms the RP model. This improved performance might be partly because the reshaped RP model has more parameters to be optimized than the RP model. Indeed, let the number of projections N and the in-degree of the projections K, then the RP model and the reshaped RP model have N and KN parameters, respectively. Does the reshaped model still outperform the original one when only (randomly chosen) N weights (out of a_ij) are allowed to be optimized and the rest is fixed? (or, does it still outperform the original model with the same number of optimized parameters (i.e. N/K neurons)?)

(4) In Figure 2, the authors have demonstrated that the homeostatic synaptic normalization outperforms the bounded model when the allowed synaptic cost is small. One possible hypothesis for explaining this fact is that the optimal solution lies in the region where only a small number of |a_ij| is large and the rest is near 0. If it is possible to verify this idea by, for example, exhibiting the distribution of a_ij after optimization, it would help the readers to better understand the mechanism behind the superiority of the homeostatic input model.

(5) In Figures 5D and 5E, the authors present how different reshaping constraints result in different learning processes ("rotation"). We find these results quite intriguing, but it would help the readers understand them if there is more explanation or interpretation. For example,

a. In the "Reshape - Hom. circuit 4.0" plot (Fig 5D, upper-left), the rotation angle between the two models is almost always the same. This is reasonable since the Homeostatic Circuit model is the least constrained model and could be almost irrelevant to the optimization process. Is there any similar interpretation to the other 3 plots of Figure 5D?

b. In Figure 5E, is there any intuitive explanation for why the three models take minimum rotation angle at similar global synaptic cost (~0.3)?

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eLife assessment

The paper characterized a specific defect in the spatial working memory of mice with a deficit in a protein called Rac1. Rac1 inhibition was limited to the presynaptic compartment of neurons, which is significant because past work has inhibited both pre- and postsynaptic compartments. The study also identified potential effectors of Rac1. The work is important for these reasons, and the strength of the evidence is exceptional.

Reviewer #1 (Public Review):

- A summary of what the authors were trying to achieve:

The authors focused on Rac1, one of the most extensively studied members of the Ras superfamily of small GTPases, an intracellular signal transducer that remodels actin and phosphorylation signaling networks. They performed an extensive series of behavioral tests and found a striking result of selectively inhibiting presynaptic Rac1. Previous studies have made the claim that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory. Rac1 was known to modulate both pre- and postsynaptic plasticity. What was missing was selective manipulation of Rac1 function at either pre- or postsynaptic loci. Kim, Soderling, and colleagues showed that following the expression of a genetically encoded Rac1-inhibitor at presynaptic terminals, spatial working memory is selectively impaired. In contrast, Rac1 inhibition at postsynaptic sites spared the spatial working memory but affected longer-term cognitive processes.

- An account of the major strengths and weaknesses of the methods and results:

This paper is part of an ambitious research trajectory, presented in multiple rigorous studies, that combines hypothesis-free fishing for candidate signal transduction elements with precise testing of physiological and behavioral outcomes. Each of these arenas has challenges and pitfalls. This paper contains punchlines in both behavioral and cell biological areas. The effect of presynaptic Rac1 inhibition on short-term behavioral memory was convincingly demonstrated with three different behavioral tests, including a quite striking result on delayed non-matching to place task. I found the claim of a specific effect on working memory more convincing here than in previous work. On the other hand, the authors sought to clarify the presynaptic regulatory mechanisms, leveraging new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. They identified particular serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins that became enriched with active Rac1. They argued that phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. They found changes in the distribution and morphology of synaptic vesicles following presynaptic Rac1 inhibition. They also report a postsynaptic consequence, a slightly increased spine cross-sectional area.

- An appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

The selective agent is the Rac1-inhibiting polypeptide W56; W56 is fused to a protein with specific subcellular localizations in neurons. Hedrick, Yasuda, et al., 2016 showed that this kind of strategy enabled a spatially targeted inhibitory effect. Collaborating with Yasuda, O'Neil in Soderling's group previously reported that Rac1 negatively regulates synaptic vesicle replenishment at both excitatory and inhibitory synapses.

In the current study by Kim et al., the goal is to interfere with Rac1 function in vivo. Once again, as in O'Neil, the functional intervention was to virally express a W56 peptide, fused to synapsin, a protein with specific subcellular localization-in this case presynaptic. The key control was to compare the effect of W56 with a scrambled sequence (Scr) in the negative control group. As verification of presynaptic efficacy, Kim found that W56-pre makes vesicles larger and further from the active zone without changing overall bouton morphology. Fresh fishing with MassSpec suggests that presynaptic vesicle proteins are affected.

I am convinced that the presynaptic Rac1 function was successfully tweaked and that this had an effect on working memory tested with 5 s intertrial intervals, in a time range where the field is hard-pressed to find robust cell biological mechanisms for memory storage. (Ion channel dynamics are an alternative, but the focus here was on cytoskeletal, not plasma membrane proteins). What was missing was a direct index of vesicle dynamics or an explanation of why a hypothetical alteration in vesicle dynamics shows up as a change in vesicle size or location. The summarizing scheme is necessarily vague; it lacks specific details about how the effect on working memory occurs, or whether it involves excitatory as opposed to inhibitory nerve terminals.

- A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community:

This study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.

An outside observer might appreciate evidence that clearly shows that pivotal cytoskeletal cell biology is not the exclusive monopoly of either side of the synaptic cleft.

- Any additional context you think would help readers interpret or understand the significance of the work:

--Overall, it shows off the art of combining fishing with causal experiments, parallel to Steve Marx's work on L-type calcium channel modulation (Nature).

--Multiple mutations associated with human neurodevelopmental and psychiatric disorders involve genes that encode regulators of the synaptic cytoskeleton. A major, unresolved question is how the disruption of specific actin filament structures leads to the onset and progression of complex synaptic and behavioral phenotypes.

--The formation of long actin filaments along the axon's longitudinal axis is relevant to the sharing of synaptic vesicles amongst multiple boutons in so-called vesicle superpools (Chenouard & Tsien, NatComm)

Reviewer #2 (Public Review):

Summary:

The paper described a behavioural characterisation of mice with presynaptically-inhibited Rac1 in the hippocampus. This is followed by a BioID and phosphoproteomic analysis of Rac1, highlighting potential downstream effectors of active or non-active Rac1 and potential downstream phosphorylated targets.

Strengths:

An original molecular approach that has been established in a previous paper by the authors (PMID 34269176) to block Rac1 function exclusively at the presynapse is now utilised to characterise a link between presynaptic dysfunction and mouse behavior. The experiments and the data well-support the conclusion that the function of Rac1 has distinct outcomes on mouse behavior, depending on its site of action.

Weaknesses:

A main limitation of the study is that it lacks physiological and biochemical analysis to follow up on hits identified in a BioID and phosphoprotemic analysis of presynaptic active and non-active Rac1 variants.

Reviewer #1 (Public Review):

Hippocampal place cells display a sequence of firing activities when the animal travels through a spatial trajectory at a behavioral time scale of seconds to tens of seconds. Interestingly, parts of the firing sequence also occur at a much shorter time scale: ~120 ms within individual cycles of theta oscillation. These so-called theta sequences are originally thought to naturally result from the phenomenon of theta phase precession. However, there is evidence that theta sequences do not always occur even when theta phase precession is present, for example, during the early experience of a novel maze. The question is then how they emerge with experience (theta sequence development). This study presents evidence that a special group of place cells, those tuned to fast-gamma oscillations, may play a key role in theta sequence development.

The authors analyzed place cells, LFPs, and theta sequences as rats traveled a circular maze in repeated laps. They found that a group of place cells were significantly tuned to a particular phase of fast-gamma (FG-cells), in contrast to others that did not show such tunning (NFG-cells). The authors then omitted FG-cells or the same number of NFG-cells, in their algorithm of theta sequence detection and found that the quality of theta sequences, quantified by a weighted correlation, was worse with the FG-cell omission, compared to that with the NFG-cell omission, during later laps, but not during early laps. What made the FG-cells special for theta sequences? The authors found that FG-cells, but not NFG-cells, displayed phase recession to slow-gamma (25 - 45 Hz) oscillations (within theta cycles) during early laps (both FG- and NFG-cells showed slow-gamma phase precession during later laps). Overall, the authors conclude that FG-cells contribute to theta sequence development through slow-gamma phase precession during early laps.

How theta sequences are formed and developed during experience is an important question, because these sequences have been implicated in several cognitive functions of place cells, including memory-guided spatial navigation. The identification of FG-cells in this study is straightforward. Evidence is also presented for the role of these cells in theta sequence development. However, given several concerns elaborated below, whether the evidence is sufficiently strong for the conclusion needs further clarification, perhaps, in future studies.

(1) The results in Figure 3 and Figure 8 seems contradictory. In Figure 8, all theta sequences displayed a seemingly significant weighted correlation (above 0) even in early laps, which was mostly due to FG-cell sequences but not NFG-cell sequences (correlation for NFG-sequences appeared below 0). However, in Figure 3H, omitting FG-cells and omitting NFG-cells did not produce significant differences in the correlation. Conversely, FG-cell and NFG-cell sequences were similar in later laps in Figure 8 (NFG-cell sequences appeared even better than FG-cell sequences), yet omitting NFG-cells produced a better correlation than omitting FG-cells. This confusion may be related to how "FG-cell-dominant sequences" were defined, which is unclear in the manuscript. Nevertheless, the different results are not easy to understand.

(2) The different contributions between FG-cells and NFG-cells to theta sequences are supposed not to be caused by their different firing properties (Figure 5). However, Figure 5D and E showed a large effect size (Cohen's D = 07, 0.8), although not significant (P = 0.09, 0.06). But the seemingly non-significant P values could be simply due to smaller N's (~20). In other parts of the manuscript, the effect sizes were comparable or even smaller (e.g. D = 0.5 in Figure 7B), but interpreted as positive results: P values were significant with large N's (~480 in Fig. 7B). Drawing a conclusion purely based on a P value while N is large often renders the conclusion only statistical, with unclear physical meaning. Although this is common in neuroscience publications, it makes more sense to at least make multiple inferences using similar sample sizes in the same study.

(3) In supplementary Figure 2 - S2, FG-cells displayed stronger theta phase precession than NFG-cells, which could be a major reason why FG-cells impacted theta sequences more than NFG cells. Although factors other than theta phase precession may contribute to or interfere with theta sequences, stronger theta phase precession itself (without the interference of other factors), by definition, can lead to stronger theta sequences.

(4) The slow-gamma phase precession of FG-cells during early laps is supposed to mediate or contribute to the emergence of theta sequences during late laps (Figure 1). The logic of this model is unclear. The slow-gamma phase precession was present in both early and late laps for FG-cells, but only present in late laps for NFG-cells. It seems more straightforward to hypothesize that the difference in theta sequences between early and later laps is due to the difference in slow-gamma phase precession of NFG cells between early and late laps. Although this is not necessarily the case, the argument presented in the manuscript is not easy to follow.

(5) There are several questions on the description of methods, which could be addressed to clarify or strengthen the conclusions.

(i) Were the identified fast- and slow-gamma episodes mutually exclusive?

(ii) Was the task novel when the data were acquired? How many days (from the 1st day of the task) were included in the analysis? When the development of the theta sequence was mentioned, did it mean the development in a novel environment, in a novel task, or purely in a sense of early laps (Lap 1, 2) on each day?

(iii) How were the animals' behavioral parameters equalized between early and later laps? For example, speed or head direction could potentially produce the differences in theta sequences.

Reviewer #2 (Public Review):

This manuscript addresses an important question that has not yet been solved in the field, what is the contribution of different gamma oscillatory inputs to the development of "theta sequences" in the hippocampal CA1 region? Theta sequences have received much attention due to their proposed roles in encoding short-term behavioral predictions, mediating synaptic plasticity, and guiding flexible decision-making. Gamma oscillations in CA1 offer a readout of different inputs to this region and have been proposed to synchronize neuronal assemblies and modulate spike timing and temporal coding. However, the interactions between these two important phenomena have not been sufficiently investigated. The authors conducted place cell and local field potential (LFP) recordings in the CA1 region of rats running on a circular track. They then analyzed the phase locking of place cell spikes to slow and fast gamma rhythms, the evolution of theta sequences during behavior, and the interaction between these two phenomena. They found that place cells with the strongest modulation by fast gamma oscillations were the most important contributors to the early development of theta sequences and that they also displayed a faster form of phase precession within slow gamma cycles nested with theta. The results reported are interesting and support the main conclusions of the authors. However, the manuscript needs significant improvement in several aspects regarding data analysis, description of both experimental and analytical methods, and alternative interpretations, as I detail below.

• The experimental paradigm and recordings should be explained at the beginning of the Results section. Right now, there is no description whatsoever which makes it harder to understand the design of the study.

• An important issue that needs to be addressed is the very small fraction of CA1 cells phased-locked to slow gamma rhythms (3.7%). This fraction is much lower than in many previous studies, that typically report it in the range of 20-50 %. However, this discrepancy is not discussed by the authors. This needs to be explained and additional analysis considered. One analysis that I would suggest, although there are also other valid approaches, is to, instead of just analyzing the phase locking in two discrete frequency bands, compute the phase locking will all LFP frequencies from 25-100 Hz. This will offer a more comprehensive and unbiased view of the gamma modulation of place cell firing. Alternative metrics to mean vector length that is less sensitive to firing rates, such as pairwise phase consistency index (Vinck et a., Neuroimage, 2010), could be implemented. This may reveal whether the low fraction of phase-locked cells could be due to a low number of spikes entering the analysis.

• From the methods, it is not clear to me whether the reference LFP channel was consistently selected to be a different one that where the spikes analyzed were taken. This is the better practice to reduce the contribution of spike leakage that could substantially inflate the coupling with faster gamma frequencies. These analyses need to be described in more detail.

• The initial framework of the authors of classifying cells into fast gamma and not fast gamma modulated implies a bimodality that may be artificial. The authors should discuss the nuances and limitations of this framework. For example, several previous work has shown that the same place cell can couple to different gamma oscillations (e.g., Lastoczni et al., Neuron, 2016; Fernandez-Ruiz et al., Neuron, 2017; Sharif et al., Neuron,2021).

• It would be useful to provide a more thorough characterization of the physiological properties of FG and NFG cells, as this distinction is the basis of the paper. Only very little characterization of some place cell properties is provided in Figure 5. Important characteristics that should be very feasible to compare include average firing rate, burstiness, estimated location within the layer (i.e., deep vs superficial sublayers) and along the transverse axis (i.e., proximal vs distal), theta oscillation frequency, phase precession metrics (given their fundamental relationship with theta sequences), etc.

• It is not clear to me how the analysis in Figure 6 was performed. In Figure 6B I would think that the grey line should connect with the bottom white dot in the third panel, which would be the interpretation of the results.

Reviewer #3 (Public Review):

[Editors' note: This review contains many criticisms that apply to the whole sub-field of slow/fast gamma oscillations in the hippocampus, as opposed to this particular paper. In the editors' view, these comments are beyond the scope of any single paper. However, they represent a view that, if true, should contextualise the interpretation of this paper and all papers in the sub-field. In doing so, they highlight an ongoing debate within the broader field.]

Summary:

The authors aimed to elucidate the role of dynamic gamma modulation in the development of hippocampal theta sequences, utilizing the traditional framework of "two gammas," a slow and a fast rhythm. This framework is currently being challenged, necessitating further analyses to establish and secure the assumed premises before substantiating the claims made in the present article.

The results are too preliminary and need to integrate contemporary literature. New analyses are required to address these concerns. However, by addressing these issues, it may be possible to produce an impactful manuscript.

I. Introduction<br /> Within the introduction, multiple broad assertions are conveyed that serve as the premise for the research. However, equally important citations that are not mentioned potentially contradict the ideas that serve as the foundation. Instances of these are described below:

(1) Are there multiple gammas? The authors launched the study on the premise that two different gamma bands are communicated from CA3 and the entorhinal cortex. However, recent literature suggests otherwise, offering that the slow gamma component may be related to theta harmonics:

From a review by Etter, Carmichael and Williams (2023)<br /> "Gamma-based coherence has been a prominent model for communication across the hippocampal-entorhinal circuit and has classically focused on slow and fast gamma oscillations originating in CA3 and medial entorhinal cortex, respectively. These two distinct gammas are then hypothesized to be integrated into hippocampal CA1 with theta oscillations on a cycle-to-cycle basis (Colgin et al., 2009; Schomburg et al., 2014). This would suggest that theta oscillations in CA1 could serve to partition temporal windows that enable the integration of inputs from these upstream regions using alternating gamma waves (Vinck et al., 2023). However, these models have largely been based on correlations between shifting CA3 and medial entorhinal cortex to CA1 coherence in theta and gamma bands. In vivo, excitatory inputs from the entorhinal cortex to the dentate gyrus are most coherent in the theta band, while gamma oscillations would be generated locally from presumed local inhibitory inputs (Pernía-Andrade and Jonas, 2014). This predominance of theta over gamma coherence has also been reported between hippocampal CA1 and the medial entorhinal cortex (Zhou et al., 2022). Another potential pitfall in the communication-through-coherence hypothesis is that theta oscillations harmonics could overlap with higher frequency bands (Czurkó et al., 1999; Terrazas et al., 2005), including slow gamma (Petersen and Buzsáki, 2020). The asymmetry of theta oscillations (Belluscio et al., 2012) can lead to harmonics that extend into the slow gamma range (Scheffer-Teixeira and Tort, 2016), which may lead to a misattribution as to the origin of slow-gamma coherence and the degree of spike modulation in the gamma range during movement (Zhou et al., 2019)."

And from Benjamin Griffiths and Ole Jensen (2023)<br /> "That said, in both rodent and human studies, measurements of 'slow' gamma oscillations may be susceptible to distortion by theta harmonics [53], meaning open questions remain about what can be attributed to 'slow' gamma oscillations and what is attributable to theta."

This second statement should be heavily considered as it is from one of the original authors who reported the existence of slow gamma.

Yet another instance from Schomburg, Fernández-Ruiz, Mizuseki, Berényi, Anastassiou, Christof Koch, and Buzsáki (2014):<br /> "Note that modulation from 20-30 Hz may not be related to gamma activity but, instead, reflect timing relationships with non-sinusoidal features of theta waves (Belluscio et al., 2012) and/or the 3rd theta harmonic."

One of this manuscript's authors is Fernández-Ruiz, a contemporary proponent of the multiple gamma theory. Thus, the modulation to slow gamma offered in the present manuscript may actually be related to theta harmonics.

With the above emphasis from proponents of the slow/fast gamma theory on disambiguating harmonics from slow gamma, our first suggestion to the authors is that they A) address these statements (citing the work of these authors in their manuscript) and B) demonstrably quantify theta harmonics in relation to slow gamma prior to making assertions of phase relationships (methodological suggestions below). As the frequency of theta harmonics can extend as high as 56 Hz (PMID: 32297752), overlapping with the slow gamma range defined here (25-45 Hz), it will be important to establish an approach that decouples the two phenomena using an approach other than an arbitrary frequency boundary.

(2) Can gammas be segregated into different lamina of the hippocampus? This idea appears to be foundational in the premise of the research but is also undergoing revision.

As discussed by Etter et al. above, the initial theory of gamma routing was launched on coherence values. However, the values reported by Colgin et al. (2009) lean more towards incoherence (a value of 0) rather than coherence (1), suggesting a weak to negligible interaction. Nevertheless, this theory is coupled with the idea that the different gamma frequencies are exclusive to the specific lamina of the hippocampus.

Recently, Deschamps et al. (2024) suggested a broader, more nuanced understanding of gamma oscillations than previously thought, emphasizing their wide range and variability across hippocampal layers. This perspective challenges the traditional dichotomy of gamma sub-bands (e.g., slow vs. medium gamma) and their associated cognitive functions based on a more rigid classification according to frequency and phase relative to the theta rhythm. Moreover, they observed all frequencies across all layers.

Similarly, the current source density plots from Belluscio et al. (2012) suggest that SG and FG can be observed in both the radiatum and lacunosum-moleculare.

Therefore, if the initial coherence values are weak to negligible and both slow and fast gamma are observed in all layers of the hippocampus, can the different gammas be exclusively related to either anatomical inputs or psychological functions (as done in the present manuscript)? Do these observations challenge the authors' premise of their research? At the least, please discuss.

(3) Do place cells, phase precession, and theta sequences require input from afferent regions? It is offered in the introduction that "Fast gamma (~65-100Hz), associated with the input from the medial entorhinal cortex, is thought to rapidly encode ongoing novel information in the context (Fernandez-Ruiz et al., 2021; Kemere, Carr, Karlsson, & Frank, 2013; Zheng et al., 2016)".

CA1 place fields remain fairly intact following MEC inactivation include Ipshita Zutshi, Manuel Valero, Antonio Fernández-Ruiz , and György Buzsáki (2022)- "CA1 place cells and assemblies persist despite combined mEC and CA3 silencing" and from Hadas E Sloin, Lidor Spivak, Amir Levi, Roni Gattegno, Shirly Someck, Eran Stark (2024) - "These findings are incompatible with precession models based on inheritance, dual-input, spreading activation, inhibition-excitation summation, or somato-dendritic competition. Thus, a precession generator resides locally within CA1."

These publications, at the least, challenge the inheritance model by which the afferent input controls CA1 place field spike timing. The research premise offered by the authors is couched in the logic of inheritance, when the effect that the authors are observing could be governed by local intrinsic activity (e.g., phase precession and gamma are locally generated, and the attribution to routed input is perhaps erroneous). Certainly, it is worth discussing these manuscripts in the context of the present manuscript.

II. Results

(1) Figure 2-<br /> a. There is a bit of a puzzle here that should be discussed. If slow and fast frequencies modulate 25% of neurons, how can these rhythms serve as mechanisms of communication/support psychological functions? For instance, if fast gamma is engaged in rapid encoding (line 72) and slow gamma is related to the integration processing of learned information (line 84), and these are functions of the hippocampus, then why do these rhythms modulate so few cells? Is this to say 75% of CA1 neurons do not listen to CA3 or MEC input?

b. Figure 2. It is hard to know if the mean vector lengths presented are large or small. Moreover, one can expect to find significance due to chance. For instance, it is challenging to find a frequency in which modulation strength is zero (please see Figure 4 of PMID: 30428340 or Figure 7 of PMID: 31324673).

i. Please construct the histograms of Mean Vector Length as in the above papers, using 1 Hz filter steps from 1-120Hz and include it as part of Figure 2 (i.e., calculate the mean vector length for the filtered LFP in steps of 1-2 Hz, 2-3 Hz, 3-4 Hz,... etc). This should help the authors portray the amount of modulation these neurons have relative to the theta rhythm and other frequencies. If the theta mean vector length is higher, should it be considered the primary modulatory influence of these neurons (with slow and fast gammas as a minor influence)?

ii. It is possible to infer a neuron's degree of oscillatory modulation without using the LFP. For instance, one can create an ISI histogram as done in Figure 1 here (https://www.biorxiv.org/content/10.1101/2021.09.20.461152v3.full.pdf+html; "Distinct ground state and activated state modes of firing in forebrain neurons"). The reciprocal of the ISI values would be "instantaneous spike frequency". In favor of the Douchamps et al. (2024) results, the figure of the BioRXiV paper implies that there is a single gamma frequency modulate as there is only a single bump in the ISIs in the 10^-1.5 to 10^-2 range. Therefore, to vet the slow gamma results and the premise of two gammas offered in the introduction, it would be worth including this analysis as part of Figure 2.

c. There are some things generally concerning about Figure 2.

i. First, the raw trace does not seem to have clear theta epochs (it is challenging to ascertain the start and end of a theta cycle). Certainly, it would be worth highlighting the relationship between theta and the gammas and picking a nice theta epoch.

ii. Also, in panel A, there looks to be a declining amplitude relationship between the raw, fast, and slow gamma traces, assuming that the scale bars represent 100uV in all three traces. The raw trace is significantly larger than the fast gamma. However, this relationship does not seem to be the case in panel B (in which both the raw and unfiltered examples of slow and fast gamma appear to be equal; the right panels of B suggest that fast gamma is larger than slow, appearing to contradict the A= 1/f organization of the power spectral density). Please explain as to why this occurs. Including the power spectral density (see below) should resolve some of this.

iii. Within the example of spiking to phase in the left side of Panel B (fast gamma example)- the neuron appears to fire near the trough twice, near the peak twice, and somewhere in between once. A similar relationship is observed for the slow gamma epoch. One would conclude from these plots that the interaction of the neuron with the two rhythms is the same. However, the mean vector lengths and histograms below these plots suggest a different story in which the neuron is modulated by FG but not SG. Please reconcile this.

iv. For calculating the MVL, it seems that the number of spikes that the neuron fires would play a significant role. Working towards our next point, there may be a bias of finding a relationship if there are too few spikes (spurious clustering due to sparse data) and/or higher coupling values for higher firing rate cells (cells with higher firing rates will clearly show a relationship), forming a sort of inverse Yerkes-Dodson curve. Also, without understanding the magnitude of the MVL relative to other frequencies, it may be that these values are indeed larger than zero, but not biologically significant.

- Please provide a scatter plot of Neuron MVL versus the Neuron's Firing Rate for 1) theta (7-9 Hz), 2) slow gamma, and 3) fast gamma, along with their line of best fit.

- Please run a shuffle control where the LFP trace is shifted by random values between 125-1000ms and recalculate the MVL for theta, slow, and fast gamma. Often, these shuffle controls are done between 100-1000 times (see cross-correlation analyses of Fujisawa, Buzsaki et al.).

- To establish that firing rate does not play a role in uncovering modulation, it would be worth conducting a spike number control, reducing the number of spikes per cell so that they are all equal before calculating the phase plots/MVL.

(2) Something that I anticipated to see addressed in the manuscript was the study from Grosmark and Buzsaki (2016): "Cell assembly sequences during learning are "replayed" during hippocampal ripples and contribute to the consolidation of episodic memories. However, neuronal sequences may also reflect preexisting dynamics. We report that sequences of place-cell firing in a novel environment are formed from a combination of the contributions of a rigid, predominantly fast-firing subset of pyramidal neurons with low spatial specificity and limited change across sleep-experience-sleep and a slow-firing plastic subset. Slow-firing cells, rather than fast-firing cells, gained high place specificity during exploration, elevated their association with ripples, and showed increased bursting and temporal coactivation during postexperience sleep. Thus, slow- and fast-firing neurons, although forming a continuous distribution, have different coding and plastic properties."

My concern is that much of the reported results in the present manuscript appear to recapitulate the observations of Grosmark and Buzsaki, but without accounting for differences in firing rate. A parsimonious alternative explanation for what is observed in the present manuscript is that high firing rate neurons, more integrated into the local network and orchestrating local gamma activity (PING), exhibit more coupling to theta and gamma. In this alternative perspective, it's not something special about how the neurons are entrained to the routed fast gamma, but that the higher firing rate neurons are better able to engage and entrain their local interneurons and, thus modulate local gamma. However, this interpretation challenges the discussion around the importance of fast gamma routed from the MEC.

a. Please integrate the Grosmark & Buzsaki paper into the discussion.

b. Also, please provide data that refutes or supports the alternative hypothesis in which the high firing rate cells are just more gamma modulated as they orchestrate local gamma activity through monosynaptic connections with local interneurons (e.g., Marshall et al., 2002, Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge). Otherwise, the attribution to a MEC routed fast gamma routing seems tenuous.<br /> c. It is mentioned that fast-spiking interneurons were removed from the analysis. It would be worth including these cells, calculating the MVL in 1 Hz increments as well as the reciprocal of their ISIs (described above).

(3) Methods - Spectral decomposition and Theta Harmonics.

a. It is challenging to interpret the exact parameters that the authors used for their multi-taper analysis in the methods (lines 516-526). Tallon-Baudry et al., (1997; Oscillatory γ-Band (30-70 Hz) Activity Induced by a Visual Search Task in Humans) discuss a time-frequency trade-off where frequency resolution changes with different temporal windows of analysis. This trade-off between time and frequency resolution is well known as the uncertainty principle of signal analysis, transcending all decomposition methods. It is not only a function of wavelet or FFT, and multi-tapers do not directly address this. (The multitaper method, by using multiple specially designed tapers -like the Slepian sequences- smooths the spectrum. This smoothing doesn't eliminate leakage but distributes its impact across multiple estimates). Given the brevity of methods and the issues of theta harmonics as offered above, it is worth including some benchmark trace testing for the multi-taper as part of the supplemental figures.

i. Please spectrally decompose an asymmetric 8 Hz sawtooth wave showing the trace and the related power spectral density using the multiple taper method discussed in the methods.

ii. Please also do the same for an elliptical oscillation (perfectly symmetrical waves, but also capable of casting harmonics). Matlab code on how to generate this time series is provided below:<br /> A = 1; % Amplitude<br /> T = 1/8; % Period corresponding to 8 Hz frequency<br /> omega = 2*pi/T; % Angular frequency<br /> C = 1; % Wave speed<br /> m = 0.9; % Modulus for the elliptic function (0<br /> x = linspace(0, 2*pi, 1000); % temporal domain<br /> t = 0; % Time instant

% Calculate B based on frequency and speed<br /> B = sqrt(omega/C);

% Cnoidal wave equation using the Jacobi elliptic function<br /> u = A .* ellipj(B.*(x - C*t), m).^2;

% Plotting the cnoidal wave<br /> figure;<br /> plot(x./max(x), u);<br /> title('8 Hz Cnoidal Wave');<br /> xlabel('time (x)');<br /> ylabel('Wave amplitude (u)');<br /> grid on;

The Symbolic Math Toolbox needs to be installed and accessible in your MATLAB environment to use ellipj. Otherwise, I trust that, rather than plotting a periodic orbit around a circle (sin wave) the authors can trace the movement around an ellipse with significant eccentricity (the distance between the two foci should be twice the distance between the co-vertices).

iii. Line 522: "The power spectra across running speeds and absolute power spectrum (both results were not shown)...". Given the potential complications of multi-taper discussed above, and as each convolution further removes one from the raw data, it would be the most transparent, simple, and straightforward to provide power spectra using the simple fft.m code in Matlab (We imagine that the authors will agree that the results should be robust against different spectral decomposition methods. Otherwise, it is concerning that the results depend on the algorithm implemented and should be discussed. If gamma transience is a concern, the authors should trigger to 2-second epochs in which slow/fast gamma exceeds 3-7 std. dev. above the mean, comparing those resulting power spectra to 2-second epochs with ripples - also a transient event). The time series should be at least 2 seconds in length (to avoid spectral leakage issues and the issues discussed in Talon-Baudry et al., 1997 above).

Please show the unmolested power spectra (Y-axis units in mV2/Hz, X-axis units as Hz) as a function of running speed (increments of 5 cm/s) for each animal. I imagine three of these PSDs for 3 of the animals will appear in supplemental methods while one will serve as a nice manuscript figure. With this plot, please highlight the regions that the authors are describing as theta, slow, and fast gamma. Also, any issues should be addressed should there be notable differences in power across animals or tetrodes (issues with locations along proximal-distal CA1 in terms of MEC/LEC input and using a local reference electrode are discussed below).

iv. Schomberg and colleagues (2014) suggested that the modulation of neurons in the slow gamma range could be related to theta harmonics (see above). Harmonics can often extend in a near infinite as they regress into the 1/f background (contributing to power, but without a peak above the power spectral density slope), making arbitrary frequency limits inappropriate. Therefore, in order to support the analyses and assertions regarding slow gamma, it seems necessary to calculate a "theta harmonic/slow gamma ratio". Aru et al. (2015; Untangling cross-frequency coupling in neuroscience) offer that: " The presence of harmonics in the signal should be tested by a bicoherence analysis and its contribution to CFC should be discussed." Please test both the synthetic signals above and the raw LFP, using temporal windows of greater than 4 seconds (again, the large window optimizes for frequency resolution in the time-frequency trade-off) to calculate the bicoherence. As harmonics are integers of theta coupled to itself and slow gamma is also coupled to theta, a nice illustration and contribution to the field would be a method that uses the bispectrum to isolate and create a "slow gamma/harmonic" ratio.

(4) I appreciate the inclusion of the histology for the 4 animals. Knerim and colleagues describe a difference in MEC projection along the proximal-distal axis of the CA1 region (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866456/)- "There are also differences in their direct projections along the transverse axis of CA1, as the LEC innervates the region of CA1 closer to the subiculum (distal CA1), whereas the MEC innervates the region of CA1 closer to CA2 and CA3 (proximal CA1)" From the histology, it looks like some of the electrodes are in the part of CA1 that would be dominated by LEC input while a few are closer to where the MEC would project.

a. How do the authors control for these differences in projections? Wouldn't this change whether or not fast gamma is observed in CA1?

b. I am only aware of one manuscript that describes slow gamma in the LEC which appeared in contrast to fast gamma from the MEC (https://www.science.org/doi/10.1126/science.abf3119). One would surmise that the authors in the present manuscript would have varying levels of fast gamma in their CA1 recordings depending on the location of the electrodes in the Proximal-distal axis, to the extent that some of the more medial tetrodes may need to be excluded (as they should not have fast gamma, rather they should be exclusively dominated by slow gamma). Alternatively, the authors may find that there is equal fast gamma power across the entire proximal-distal axis. However, this would pose a significant challenge to the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz et al. and require reconciliation/discussion.

c. Is there a difference in neuron modulation to these frequencies based on electrode location in CA1?

(5) Given a comment in the discussion (see below), it will be worth exploring changes in theta, theta harmonic, slow gamma, and fast gamma power with running speed as no changes were observed with theta sequences or lap number versus. Notably, Czurko et al., report an increase in theta and harmonic power with running speed (1999) while Ahmed and Mehta (2012) report a similar effect for gamma.

a. Please determine if the oscillations change in power and frequency of the rhythms discussed above change with running speed using the same parameters applied in the present manuscript. The specific concern is that how the authors calculate running speed is not sensitive enough to evaluate changes.

b. It is astounding that animals ran as fast as they did in what appears to be the first lap (Figure 3F), especially as rats' natural proclivity is thigmotaxis and inquisitive exploration in novel environments. Can the authors expand on why they believe their rats ran so quickly on the first lap in a novel environment and how to replicate this? Also, please include the individual values for each animal on the same plot.

c. Can the authors explain how the statistics on line 169 (F(4,44)) work? Specifically, it is challenging to determine how the degrees of freedom were calculated in this case and throughout if there were only 4 animals (reported in methods) over 5 laps (depicted in Figure 3F. Given line 439, it looks like trials and laps are used synonymously). Four animals over 5 laps should have a DOF of 16.

(6) Throughout the manuscript, I am concerned about an inflation of statistical power. For example on line 162, F(2,4844). The large degrees of freedom indicate that the sample size was theta sequences or a number of cells. Since multiple observations were obtained from the same animal, the statistical assumption of independence is violated. Therefore, the stats need to be conducted using a nested model as described in Aarts et al. (2014; https://pubmed.ncbi.nlm.nih.gov/24671065/). A statistical consult may be warranted.

(7) It is stated that one tetrode served as a quiet recording reference. The "quiet" part is an assumption when often, theta and gamma can be volume conducted to the cortex (e.g., Sirota et al., 2008; This is often why laboratories that study hippocampal rhythms use the cerebellum for the differential recording electrode and not an electrode in the corpus callosum). Generally, high frequencies propagate as well as low frequencies in the extracellular milieu (https://www.eneuro.org/content/4/1/ENEURO.0291-16.2016). For transparency, the authors should include a limitation paragraph in their discussion that describes how their local tetrode reference may be inadvertently diminishing and/or distorting the signal that they are trying to isolate. Otherwise, it would be worth hearing an explanation as to how the author's approach avoids this issue.

Apologetically, this review is already getting long. Moreover, I have substantial concerns that should be resolved prior to delving into the remainder of the analyses. e.g., the analyses related to Figure 3-5 assert that FG cells are important for sequences. However, the relationship to gamma may be secondary to either their relationship to theta or, based on the Grosmark and Buzsaki paper, it may just be a phenomenon coupled to the fast-firing cells (fast-firing cells showing higher gamma modulation due to a local PING dynamic). Moreover, the observation of slow gamma is being challenged as theta harmonics, even by the major proponents of the slow/fast gamma theory. Therefore, the report of slow gamma precession would come as an unsurprising extension should they be revealed to be theta harmonics (however, no control for harmonics was implemented; suggestions were made above). Following these amendments, I would be grateful for the opportunity to provide further feedback.

III. Discussion.

a. Line 330- it was offered that fast gamma encodes information while slow gamma integrates in the introduction. However, in a task such as circular track running (from the methods, it appears that there is no new information to be acquired within a trial), one would guess that after the first few laps, slow gamma would be the dominant rhythm. Therefore, one must wonder why there are so few neurons modulated by slow gamma (~3.7%).

b. Line 375: The authors contend that: "...slow gamma, related to information compression, was also required to modulate fast gamma phase-locked cells during sequence development. We replicated the results of slow gamma phase precession at the ensemble level (Zheng et al., 2016), and furthermore observed it at late development, but not early development, of theta sequences." In relation to the idea that slow gamma may be coupled to - if not a distorted representation of - theta harmonics, it has been observed that there are changes in theta relative to novelty.

i. A. Jeewajee, C. Lever, S. Burton, J. O'Keefe, and N. Burgess (2008) report a decrease in theta frequency in novel circumstances that disappears with increasing familiarity.

ii. One could surmise that this change in frequency is associated with alterations in theta harmonics (observed here as slow gamma), challenging the author's interpretation.

iii. Therefore, the authors have a compelling opportunity to replicate the results of Jeewajee et al., characterizing changes of theta along with the development of slow gamma precession, as the environment becomes familiar. It will become important to demonstrate, using bicoherence as offered by Aru et al., how slow gamma can be disambiguated from theta harmonics. Specifically, we anticipate that the authors will be able to quantify A) theta harmonics (the number, and their respective frequencies and amplitudes), B) the frequency and amplitude of slow gamma, and C) how they can be quantitatively decoupled. Through this, their discussion of oscillatory changes with novelty-familiarity will garner a significant impact.

c. Broadly, it is interesting that the authors emphasize the gamma frequency throughout the discussion. Given that the power spectral density of the Local Field Potential (LFP) exhibits a log-log relationship between amplitude and frequency, as described by Buzsáki (2005) in "Rhythms of the Brain," and considering that the LFP is primarily generated through synaptic transmembrane currents (Buzsáki et al., 2012), it seems parsimonious to consider that the bulk of synaptic activity occurs at lower frequencies (e.g., theta). Since synaptic transmission represents the most direct form of inter-regional communication, one might wonder why gamma (characterized by lower amplitude rhythms) is esteemed so highly compared to the higher amplitude theta rhythm. Why isn't the theta rhythm, instead, regarded as the primary mode of communication across brain regions? A discussion exploring this question would be beneficial.

eLife assessment

This study provides important information about the formation of ribbon synapses in mouse cochlear hair cells, which facilitate the temporally-precise transmission of acoustic information to the auditory nerve. Live-cell imaging provides compelling evidence that ribbon precursor volume is dynamically modified by fission and fusion events on microtubules, but some of the other evidence included, particularly in relation to the directed transport of these precursors to the hair cell active zone is incomplete. These findings will be of interest to neuroscientists studying synapse formation and function and should inspire further research into the molecular basis for synaptic ribbon maturation.

Reviewer #1 (Public Review):

Summary

The manuscript by Voorn and collaborators aims at deciphering the microtubule-dependent ribbon formation in mouse hair cells. Using STED/confocal imaging, pharmacology tools, and mouse mutant, the group of Christian Vogl convincingly demonstrated that ribbon, the organelle that tethers vesicles at the hair cell synapse, results from the fusion and fission of ribbon precursors, moving along the microtubule network. This study goes hand in hand with a complementary paper (Hussain et al.) showing similar findings in zebrafish hair cells.

Strengths

This study demonstrated i) the motion of ribbons precursors along the microtubules, ii) ribbons precursors undergo multiple cycles of fusion-fission events and iii) kinesin Kif1a is critical for synaptic maturation. The results are solid and the images are mesmeric.

Weaknesses

As stated by the authors in the discussion, the mechanism underlying the threshold shift in the Kif1a mutant is unclear and may not be solely attributed to the reduction of the ribbon volume.

Impact

The synaptogenesis in the auditory sensory cell remains still elusive. Here, this study shows a high plasticity in the synaptogenesis. Indeed, the formation of the synaptic organelle is a dynamic process consisting of several rounds of fusion-fission of presynaptic elements. This study will undoubtedly boost a new line of research aimed at identifying the specific molecular determinants that target ribbon precursors to the synapse and govern the fusion-fission process.

Reviewer #2 (Public Review):

Summary

This manuscript makes use of live cell imaging to look at aggregates of the synaptic ribbon protein ribeye to explore synapse formation in an organotypic culture system. The authors find that microtubule disruption influences the motion of a subset of ribeye spots and changes to ribbon volume. Disruption of the microtubule motor is also found to change ribeye motion and ribbon volume, albeit in the opposite direction. Together these results support a role for microtubule-based transport in synapse assembly.

Strengths

(1) The use of the in vitro imaging approach provides a method for high-quality live cell imaging in a mammalian preparation.

(2) The data characterizing the movement of Ribeye in the cochlea is new and exciting.

(3) The role of motors in the delivery of Ribeye to the synapse had never been established. The effects of nocodozole on directional asymmetry for the subset of slow-moving particles are convincing, though it is unclear to this reviewer how frequently these objects undergo directed motion.

(4) The effect of Kif1a on ribbon size is an interesting finding that doesn't rely on overexpression and supports the importance of motors on the delivery of ribeye to the synapse.

Weaknesses

(1) The analysis leaves unclear what fraction of ribeye spots make use of active transport mechanisms. The authors make the claim that 54% underwent targeted transport because fits of their MSD vs time were best-fit by an exponent >1. This overstates the reliability of this approach. Purely diffusive motion will not always fit perfectly with an exponent of exactly 1 and one would expect roughly to have to have greater than 1 and half less than one, which is what they observe. In point of fact, truly directed transport should have an exponent near 2 (Figure 2F), which only a handful of spots seem to exhibit. I should also note that none of the examples look like those that are typically associated with directed motion.

(2) The imaging approach makes use of viral expression using a non-Ribeye promoter. This overexpression approach will likely exaggerate the number of ribeye spots and could saturate binding to other proteins or other factors. Also, the promoters aren't under the control of feedback mechanisms that would typically turn off expression at the appropriate time.

(3) The effect of Kif1A removal on the ABR threshold is very unlikely to be due to ribbon size. Complete removal of the ribbon only has a modest effect on the ABR threshold, so these modest reductions in size are unlikely to contribute much.

(4) Fusion and fission of small aggregates are difficult to resolve with light microscopy and the examples provided in Figure 3 are indistinguishable from two spots that happen to be too close to each other to resolve.

5) The "slight left shift" in the velocity distribution in Figure 5C does not look significant. Is it?

6) Nocodozole and elimination of Kif1a have opposite effects on ribbon volume, which might point to alternative roles for the microtubules.

Reviewer #3 (Public Review):

Summary

In this study, the authors addressed the question of how synaptic ribbons-specialized, electron-dense presynaptic structures-are formed from ribbon precursors in sensory hair cells. Specifically, the authors evaluated whether molecular motor-driven, microtubule-based transport plays a role in the directed transport of ribbon precursors to the active zone of cochlear hair cells and assessed whether there was a specific role for the microtubule motor Kinesin Family Member 1A (Kif1a). Using live imaging of cochlear explants and fixed images of both mature and developing cochlea, they provide evidence that ribbon precursors are actively transported on microtubules, that ribbon precursor volume is dynamically modified by fission and fusion events on microtubules, and that Kif1a plays a role in synaptic ribbon maturation.

Strengths

Overall, the data presented in this study support that the fission and fusion of ribbon precursors are dependent on microtubule-based translocation, and this dynamic assembly of precursors may involve Kif1a. Live-imaging data and analysis provide strong evidence for microtubule-based transport contributing to dynamic fission-fusion events of ribbon precursors. Further, fixed image analysis of Kif1a mutants supports that it plays a key role in synaptic ribbon maturation.

Weaknesses

While the authors clearly established the polarity and stability of microtubules in hair cells, they did not assess the net direction of putative slow microtubule-based movement (i.e. the ratios of plus to minus end-directed travel) in their analysis of ribbon precursor displacement. This information is critical in establishing a role for microtubule-based transport in localizing ribbon precursors to the active zones in the basolateral region of hair cells to form presynaptic ribbons. In addition, the discussion section did not elaborate on what is known about the coordination of molecular motor proteins during microtubule-based transport nor did it effectively incorporate the interpretation of the results with what has been described in previous studies on intracellular transport and the roles of Kif1a in synaptic vesicle precursor trafficking.

Résumé de la vidéo [00:00:02][^1^][1] - [00:44:59][^2^][2]:

La vidéo présente une discussion approfondie sur la réforme de l'enseignement professionnel en France, ses implications, et l'impact sur les élèves et les enseignants. Laure, sociologue et directrice d'une école d'ingénieurs, partage son expertise sur l'histoire de l'enseignement professionnel, les objectifs de la réforme actuelle, et les défis qu'elle pose.

Points forts: + [00:00:02][^3^][3] Contexte de la réforme * Importance de comprendre les enjeux et conséquences * Invitation de Laure, experte en enseignement professionnel + [00:01:50][^4^][4] Histoire de l'enseignement professionnel * Création pour sortir les enfants des usines * Évolution depuis le 19e siècle jusqu'à la loi Astier et le CAP + [00:06:21][^5^][5] Objectifs politiques récents * 80% d'une classe d'âge au baccalauréat * Création du baccalauréat professionnel en 1985 + [00:10:49][^6^][6] Analyse de la réforme actuelle * Focus sur l'apprentissage et l'insertion professionnelle * Risques de creuser les inégalités et de servir l'économie plutôt que l'émancipation + [00:17:01][^7^][7] Inégalités dans l'accès aux stages de qualité * Influence du réseau familial sur la qualité des stages * Réforme favorisant les étudiants les mieux dotés + [00:23:19][^8^][8] Conséquences pour l'enseignement et les enseignants * Risque de remise en cause du collège unique * Importance du collectif pour agir et préserver l'émancipation

On second though, I think it could be fine if a generative model is used.

eLife assessment

This valuable study investigates the brain representations of Braille letters in blind participants and provides convincing evidence using EEG and fMRI that the decoding of letter identity across the reading hand takes place in the visual cortex. The evidence supporting the claims of the authors is solid, although the inclusion of a sighted control group and additional analyses would have strengthened the study. The work will be of interest to neuroscientists working on brain plasticity.

Reviewer #1 (Public Review):

Summary:

The researchers examined how individuals who were born blind or lost their vision early in life process information, specifically focusing on the decoding of Braille characters. They explored the transition of Braille character information from tactile sensory inputs, based on which hand was used for reading, to perceptual representations that are not dependent on the reading hand.

They identified tactile sensory representations in areas responsible for touch processing and perceptual representations in brain regions typically involved in visual reading, with the lateral occipital complex serving as a pivotal "hinge" region between them.

In terms of temporal information processing, they discovered that tactile sensory representations occur prior to cognitive-perceptual representations. The researchers suggest that this pattern indicates that even in situations of significant brain adaptability, there is a consistent chronological progression from sensory to cognitive processing.

Strengths:

By combining fMRI and EEG, and focusing on the diagnostic case of Braille reading, the paper provides an integrated view of the transformation processing from sensation to perception in the visually deprived brain. Such a multimodal approach is still rare in the study of human brain plasticity and allows us to discern the nature of information processing in blind people's early visual cortex, as well as the time course of information processing in a situation of significant brain adaptability.

Weaknesses:

The lack of a sighted control group limits the interpretations of the results in terms of profound cortical reorganization, or simple unmasking of the architectural potentials already present in the normally developing brain. Moreover, the conclusions regarding the behavioral relevance of the sensory and perceptual representations in the putatively reorganized brain are limited due to the behavioral measurements adopted.

Reviewer #2 (Public Review):

Summary:

Haupt and colleagues performed a well-designed study to test the spatial and temporal gradient of perceiving braille letters in blind individuals. Using cross-hand decoding of the read letters, and comparing it to the decoding of the read letter for each hand, they defined perceptual and sensory responses. Then they compared where (using fMRI) and when (using EEG) these were decodable. Using fMRI, they showed that low-level tactile responses specific to each hand are decodable from the primary and secondary somatosensory cortex as well as from IPS subregions, the insula, and LOC. In contrast, more abstract representations of the braille letter independent from the reading hand were decodable from several visual ROIs, LOC, VWFA, and surprisingly also EVC. Using a parallel EEG design, they showed that sensory hand-specific responses emerge in time before perceptual braille letter representations. Last, they used RSA to show that the behavioral similarity of the letter pairs correlates to the neural signal of both fMRI (for the perceptual decoding, in visual and ventral ROIs) and EEG (for both sensory and perceptual decoding).

Strengths:

This is a very well-designed study and it is analyzed well. The writing clearly describes the analyses and results. Overall, the study provides convincing evidence from EEG and fMRI that the decoding of letter identity across the reading hand occurs in the visual cortex in blindness. Further, it addresses important questions about the visual cortex hierarchy in blindness (whether it parallels that of the sighted brain or is inverted) and its link to braille reading.

Weaknesses:

Although I have some comments and requests for clarification about the details of the methods, my main comment is that the manuscript could benefit from expanding its discussion. Specifically, I'd appreciate the authors drawing clearer theoretical conclusions about what this data suggests about the direction of information flow in the reorganized visual system in blindness, the role VWFA plays in blindness (revised from the original sighted role or similar to it?), how information arrives to the visual cortex, and what the authors' predictions would be if a parallel experiment would be carried out in sighted people (is this a multisensory recruitment or reorganization?). The data has the potential to speak to a lot of questions about the scope of brain plasticity, and that would interest broad audiences.

To aid in drawing even more concrete conclusions about the flow of information, I suggest that the authors also add at least another early visual ROI to plot more clearly whether EVC's response to braille letters arrives there through an inverted cortical hierarchy, intermediate stages from VWFA, or directly, as found in the sighted brain for spoken language.

Similarly, it may be informative to look specifically at the occipital electrodes' time differences between decoding for the different parameters and their correlation to behavior.

Regarding the methods, further detail on the ability to read with both hands equally and any residual vision of the participants would be helpful.

面试包括：1. 向非本专业的人解释自己的研究，用具体例子讲述自己研究方法的优势，未来五年的学术计划 2. 做学术演讲，以及担当学术演讲的听众。

the phrase "play faculty-lounge games" refers to engaging in the political, social, and sometimes petty activities that can occur in academic settings. This might include gossiping, forming cliques, maneuvering for power or status, and focusing on personal advancement rather than genuine intellectual or educational goals.

ChatGPT

| və(ʊ)ˈkeɪʃn |

noun

a strong feeling of suitability for a particular career or occupation: not all of us have a vocation to be nurses or doctors.

• a person's employment or main occupation, especially regarded as worthy and requiring dedication: her vocation as a poet.

vacation | vəˈkeɪʃn | 和 vocation 读音上可能一样，vocation 也可以读作 | vəˈkeɪʃn |。为了区分 vocation 还是读作 | vəʊˈkeɪʃn | 吧。

a system, process, department, etc. that operates in isolation from others

diversity, equity, and inclusion.

The phrase "toad-eat-dog" in this sentence is a play on the more familiar expression "dog-eat-dog," which describes a highly competitive and ruthless environment where people are willing to do whatever it takes to succeed, often at the expense of others.

By using "toad-eat-dog," the author adds a humorous twist while still conveying the intense competitiveness and cutthroat nature of academia. The imagery of a toad, typically seen as less aggressive and more vulnerable than a dog, eating a dog underscores the surprising and harsh realities of the academic world. This playful alteration of the phrase highlights the unexpected and sometimes brutal challenges faced by individuals in academic job markets.

ChatGPT

| ˈprɒvəst | noun

1 British English the head of certain university colleges, especially at Oxford or Cambridge, and public schools.

• North American English a senior administrative officer in certain universities.

| ˌpɛdəˈɡɒdʒɪkl, ˌpɛdəˈɡɒɡɪkl |

adjective

relating to teaching: innovative pedagogical methods.

"Literate scientists" refers to scientists who are well-versed in the humanities, while "numerate poets" refers to poets who are comfortable with numerical and scientific concepts.

Multiple Languages of Understanding: This phrase highlights the ability to think and communicate across different disciplines and areas of knowledge.

Instinctive: This implies that the judgment comes naturally and quickly, without the need for prolonged deliberation. It is a kind of intuition that has been developed through experience and education.

Just: This means that the judgment is fair, unbiased, and morally right. It emphasizes the ethical dimension of decision-making.

Estimate: This refers to the judgment or assessment itself. It involves evaluating and weighing different aspects of a situation to form an opinion.

a group of people with a shared characteristic: a cohort of civil servants patiently drafting legislation.

| məˈtrɪkjʊleɪt |

verb

1 [no object] be enrolled at a college or university: they had recently matriculated as undergraduates at Jesus College.

• [with object] admit (a student) to membership of a college or university: he was matriculated at Balliol College, Oxford.

sequester | sɪˈkwɛstə |

verb [with object]

1 isolate or hide away: she is sequestered in deepest Dorset | the artist sequestered himself in his studio for two years.

adjective

1 relating to the logical discussion of ideas and opinions: dialectical ingenuity.

2 concerned with or acting through opposing forces: a dialectical opposition between artistic translation and transcription.

eLife assessment

This valuable study uses recently developed EEG analysis methods to investigate spatial distractor suppression in a combined visual search/working memory task. While the reported results are convincing, the combined task design leaves open alternative interpretations than those currently discussed in the manuscript, potentially limiting the generalisability of the findings to other task settings. The study will be of interest to cognitive neuroscientists and psychologists working on visual attention and memory.

Reviewer #1 (Public Review):

Summary:

The authors tested whether learning to suppress (ignore) salient distractors (e.g., a lone colored nontarget item) via statistical regularities (e.g., the distractor is more likely to appear in one location than any other) was proactive (prior to paying attention to the distractor) or reactive (only after first attending the distractor) in nature. To test between proactive and reactive suppression the authors relied on a recently developed and novel technique designed to "ping" the brain's hidden priority map using EEG inverted encoding models. Essentially, a neutral stimulus is presented to stimulate the brain, resulting in activity on a priority map which can be decoded and used to argue when this stimulation occurred (prior to or after attending to a distracting item). The authors found evidence that despite learning to suppress the high probability distractor location, the suppression was reactive, not proactive in nature.

Overall, the manuscript is well-written, tests a timely question, and provides novel insight into a long-standing debate concerning distractor suppression.

Strengths (in no particular order):

(1) The manuscript is well-written, clear, and concise (especially given the complexities of the method and analyses).

(2) The presentation of the logic and results is mostly clear and relatively easy to digest.

(3) This question concerning whether location-based distractor suppression is proactive or reactive in nature is a timely question.

(4) The use of the novel "pinging" technique is interesting and provides new insight into this particularly thorny debate over the mechanisms of distractor suppression.

Weaknesses (in no particular order):

(1) The authors tend to make overly bold claims without either A) mentioning the opposing claim(s) or B) citing the opposing theoretical positions. Further, the authors have neglected relevant findings regarding this specific debate between proactive and reactive suppression.

(2) The authors should be more careful in setting up the debate by clearly defining the terms, especially proactive and reactive suppression which have recently been defined and were more ambiguously defined here.

(3) There were some methodological choices that should be further justified, such as the choice of stimuli (e.g., sizes, colors, etc.).

(4) The figures are often difficult to process. For example, the time courses are so far zoomed out (i.e., 0, 500, 100 ms with no other tick marks) that it makes it difficult to assess the timing of many of the patterns of data. Also, there is a lot of baseline period noise which complicates the interpretations of the data of interest.

(5) Sometimes the authors fail to connect to the extant literature (e.g., by connecting to the ERP components, such as the N2pc and PD components, used to argue for or against proactive suppression) or when they do, overreach with claims (e.g., arguing suppression is reactive or feature-blind more generally).

Reviewer #2 (Public Review):

Summary:

The authors investigate the mechanisms supporting learning to suppress distractors at predictable locations, focusing on proactive suppression mechanisms manifesting before the onset of a distractor. They used EEG and inverted encoding models (IEM). The experimental paradigm alternates between a visual search task and a spatial memory task, followed by a placeholder screen acting as a 'ping' stimulus -i.e., a stimulus to reveal how learned distractor suppression affects hidden priority maps. Behaviorally, their results align with the effects of statistical learning on distractor suppression. Contrary to the proactive suppression hypothesis, which predicts reduced memory-specific tuning of neural representations at the expected distractor location, their IEM results indicate increased tuning at the high-probability distractor location following the placeholder and prior to the onset of the search display.

Strengths:

Overall, the manuscript is well-written and clear, and the research question is relevant and timely, given the ongoing debate on the roles of proactive and reactive components in distractor processing. The use of a secondary task and EEG/IEM to provide a direct assessment of hidden priority maps in anticipation of a distractor is, in principle, a clever approach. The study also provides behavioral results supporting prior literature on distractor suppression at high-probability locations.

Weaknesses:

(1) At a conceptual level, I understand the debate and opposing views, but I wonder whether it might be more comprehensive to present also the possibility that both proactive and reactive stages contribute to distractor suppression. For instance, anticipatory mechanisms (proactive) may involve expectations and signals that anticipate the expected distractor features, whereas reactive mechanisms contribute to the suppression and disengagement of attention.

(2) The authors focus on hidden priority maps in pre-distractor time windows, arguing that the results challenge a simple proactive view of distractor suppression. However, they do not provide evidence that reactive mechanisms are at play or related to the pinging effects found in the present paradigm. Is there a relationship between the tuning strength of CTF at the high-probability distractor location and the actual ability to suppress the distractor (e.g., behavioral performance)? Is there a relationship between CTF tuning and post-distractor ERP measures of distractor processing? While these may not be the original research questions, they emerge naturally and I believe should be discussed or noted as limitations.

(3) How do the authors ensure that the increased tuning (which appears more as a half-split or hemifield effect rather than gradual fine-grained tuning, as shown in Figure 5) is not a byproduct of the dual-task paradigm used, rather than a general characteristic of learned attentional suppression? For example, the additional memory task and the repeated experience with the high-probability distractor at the specific location might have led to longer-lasting and more finely-tuned traces for memory items at that location compared to others.

(4) It is unclear how IEM was performed on total vs. evoked power, compared to typical approaches of running it on single trials or pseudo-trials.

(5) Following on point 1. What is the rationale for relating decreased (but not increased) tuning of CTF to proactive suppression? Could it be that proactive suppression requires anticipatory tuning towards the expected feature to implement suppression? In other terms, better 'tuning' does not necessarily imply a higher signal amplitude and could be observable even under signal suppression. The authors should comment on this and clarify.

Minor:

(1) In the Word file I reviewed, there are minor formatting issues, such as missing spaces, which should be double-checked.

(2) Would the authors predict that proactive mechanisms are not involved in other forms of attention learning involving distractor suppression, such as habituation?

(3) A clear description in the Methods section of how individual CTFs for each location were derived would help in understanding the procedure.

(4) Why specifically 1024 resampling iterations?

Reviewer #3 (Public Review):

Summary:

In this experiment, the authors use a probe method along with time-frequency analyses to ascertain the attentional priority map prior to a visual search display in which one location is more likely to contain a salient distractor.  The main finding is that neural responses to the probe indicate that the high probability location is attended, rather than suppressed, prior to the search display onset.  The authors conclude that suppression of distractors at high-probability locations is a result of reactive, rather than proactive, suppression.

Strengths:

This was a creative approach to a difficult and important question about attention.  The use of this "pinging" method to assess the attentional priority map has a lot of potential value for a number of questions related to attention and visual search. Here as well, the authors have used it to address a question about distractor suppression that has been the subject of competing theories for many years in the field. The paper is well-written, and the authors have done a good job placing their data in the larger context of recent findings in the field.

Weaknesses:

The link between the memory task and the search task could be explored in greater detail. For example, how might attentional priority maps change because of the need to hold a location in working memory? This might limit the generalizability of these findings. There could be more analysis of behavioral data to address this question. In addition, the authors could explore the role that intertrial repetition plays in the attentional priority map as these factors necessarily differ between conditions in the current design. Finally, the explanation of the CTF analyses in the results could be written more clearly for readers who are less familiar with this specific approach (which has not been used in this field much previously).

eLife assessment

This important work uses in vivo foveal cone-resolved imaging and simultaneous microscopic photostimulation to investigate the relationship between ocular drift - eye movements long thought to be random - and visual acuity. The surprising result is that ocular drift is systematic - causing the object to move to the center of the cone mosaic over the course of each perceptual trial. The tools used to reach this conclusion are state-of-the-art and the evidence presented is convincing. This work advances our understanding of the visuomotor system and the interplay of anatomy, oculomotor behavior, and visual acuity.

Reviewer #1 (Public Review):

Summary:

This paper investigates the relationship between ocular drift - eye movements long thought to be random - and visual acuity. This is a fundamental issue for how vision works. The work uses adaptive optics retinal imaging to monitor eye movements and where a target object is in the cone photoreceptor array. The surprising result is that ocular drift is systematic - causing the object to move to the center of the cone mosaic over the course of each perceptual trial. The tools used to reach this conclusion are state-of-the-art and the evidence presented is convincing.

Strengths

The central question of the paper is interesting, as far as I know, it has not been answered in past work, and the approaches employed in this work are appropriate and provide clear answers.

The central finding - that ocular drift is not a completely random process - is important and has a broad impact on how we think about the relationship between eye movements and visual perception.

The presentation is quite nice: the figures clearly illustrate key points and have a nice mix of primary and analyzed data, and the writing (with one important exception) is generally clear.

Weaknesses

The handling of the Nyquist limit is confusing throughout the paper and could be improved. It is not clear (at least to me) how the Nyquist limit applies to the specific task considered. I think of the Nyquist limit as saying that spatial frequencies above a certain cutoff set by the cone spacing are being aliased and cannot be disambiguated from the structure at a lower spatial frequency. In other words, there is a limit to the spatial frequency content that can be uniquely represented by discrete cone sampling locations. Acuity beyond that limit is certainly possible with a stationary image - e.g. a line will set up a distribution of responses in the cones that it covers, and without noise, an arbitrarily small displacement of the line would change the distribution of cone responses in a way that could be resolved. This is an important point because it relates to whether some kind of active sampling or movement of the detectors is needed to explain the spatial resolution results in the paper. This issue comes up in the introduction, results, and discussion. It arises in particular in the two Discussion paragraphs starting on line 343.

One question that came up as I read the paper was whether the eye movement parameters depend on the size of the E. In other words, to what extent is ocular drift tuned to specific behavioral tasks?

Reviewer #2 (Public Review):

Summary:

In this work, Witten et al. assess visual acuity, cone density, and fixational behavior in the central foveal region in a large number of subjects.

This work elegantly presents a number of important findings, and I can see this becoming a landmark work in the field. First, it shows that acuity is determined by the cone mosaic, hence, subjects characterized by higher cone densities show higher acuity in diffraction-limited settings. Second, it shows that humans can achieve higher visual resolution than what is dictated by cone sampling, suggesting that this is likely the result of fixational drift, which constantly moves the stimuli over the cone mosaic. Third, the study reports a correlation between the amplitude of fixational motion and acuity, namely, subjects with smaller drifts have higher acuities and higher cone density. Fourth, it is shown that humans tend to move the fixated object toward the region of higher cone density in the retina, lending further support to the idea that drift is not a random process, but is likely controlled. This is a beautiful and unique work that furthers our understanding of the visuomotor system and the interplay of anatomy, oculomotor behavior, and visual acuity.

Strengths:

The work is rigorously conducted, it uses state-of-the-art technology to record fixational eye movements while imaging the central fovea at high resolution and examines exactly where the viewed stimulus falls on individuals' foveal cone mosaic with respect to different anatomical landmarks in this region. The figures are clear and nicely packaged. It is important to emphasize that this study is a real tour-de-force in which the authors collected a massive amount of data on 20 subjects. This is particularly remarkable considering how challenging it is to run psychophysics experiments using this sophisticated technology. Most of the studies using psychophysics with AO are, indeed, limited to a few subjects. Therefore, this work shows a unique set of data, filling a gap in the literature.

Weaknesses:

No major weakness was noted, but data analysis could be further improved by examining drift instantaneous direction rather than start-point-end-point direction, and by adding a statistical quantification of the difference in direction tuning between the three anatomical landmarks considered.

Reviewer #3 (Public Review):

Summary:

The manuscript by Witten et al., titled "Sub-cone visual resolution by active, adaptive sampling in the human foveola," aims to investigate the link between acuity thresholds (and hyperacuity) and retinal sampling. Specifically, using in vivo foveal cone-resolved imaging and simultaneous microscopic photostimulation, the researchers examined visual acuity thresholds in 16 volunteers and correlated them with each individual's retinal sampling capacity and the characteristics of ocular drift.

First, the authors found that although visual acuity was highly correlated with the individual spatial arrangement of cones, for all participants, visual resolution exceeded the Nyquist sampling limit - a well-known phenomenon in the literature called hyperacuity.

Thus, the researchers hypothesized that this increase in acuity, which could not be explained in terms of spatial encoding mechanisms, might result from exploiting the spatiotemporal characteristics of visual input, which is continuously modulated over time by eye movements even during so-called fixations (e.g., ocular drift).

Authors reported a correlation between subjects, between acuity threshold and drift amplitude, suggesting that the visual system benefits from transforming spatial input into a spatiotemporal flow. Finally, they showed that drift, contrary to the traditional view of it as random involuntary movement, appears to exhibit directionality: drift tends to move stimuli to higher cone density areas, therefore enhancing visual resolution.

Strengths:

The work is of broad interest, the methods are clear, and the results are solid.

Weaknesses:

Literature (1/2): The authors do not appear to be aware of an important paper published in 2023 by Lin et al. (https://doi.org/10.1016/j.cub.2023.03.026), which nicely demonstrates that (i) ocular drifts are under cognitive influence, and (ii) specific task knowledge influences the dominant orientation of these ocular drifts even in the absence of visual information. The results of this article are particularly relevant and should be discussed in light of the findings of the current experiment.

Literature (2/2): The hypothesis that hyperacuity is attributable to ocular movements has been proposed by other authors and should be cited and discussed (e.g., https://doi.org/10.3389/fncom.2012.00089, https://doi.org/10.1016/s0896-6273(01)00466-4).

Drift Dynamic Characterization: The drift is primarily characterized as the "concatenated vector sum of all frame-wise motion vectors within the 500 ms stimulus duration.". To better compare with other studies investigating the link between drift dynamics and visual acuity (e.g., Clark et al., 2022), it would be interesting to analyze the drift-diffusion constant, which might be the parameter most capable of describing the dynamic characteristics of drift.

Possible inconsistencies: Binocular differences are not expected based on the hypothesis; the authors may speculate a bit more about this. Additionally, the fact that hyperacuity does not occur with longer infrared wavelengths but the drift dynamics do not vary between the two conditions is interesting and should be discussed more thoroughly.

As a Suggestion: can the authors predict the accuracy of individual participants in single trials just by looking at the drift dynamics?

Résumé de la vidéo [00:00:06][^1^][1] - [00:39:58][^2^][2]:

Cette vidéo est un webinaire de Sportsregions sur les adhésions, présenté en juin 2024. Elle explique comment gérer les adhésions en ligne via la plateforme SP région, en mettant l'accent sur la préparation du site, l'organisation du processus d'adhésion, l'ouverture des adhésions et le suivi des demandes. Le présentateur détaille les étapes pour activer les saisons, définir les catégories d'âge, créer des équipes, préparer les formulaires d'adhésion, ajouter des produits d'adhésion, définir les moyens de paiement et gérer les adhésions reçues.

Points forts: + [00:00:06][^3^][3] Introduction au webinaire * Présentation du but et du déroulement du webinaire * Annonce d'une session de questions-réponses à la fin + [00:01:33][^4^][4] Gestion des adhésions en ligne * Avantages de gérer les adhésions en ligne * Gain de temps et centralisation des informations + [00:03:32][^5^][5] Préparation du site pour les adhésions * Activation des saisons et définition des catégories d'âge * Création des équipes et préparation des formulaires + [00:13:11][^6^][6] Organisation du processus d'adhésion * Construction du formulaire avec champs principaux et complémentaires * Ajout de produits d'adhésion et gestion des moyens de paiement + [00:25:06][^7^][7] Exemple de formulaire complexe * Présentation d'un formulaire plus détaillé avec des exemples concrets * Variété des options et personnalisation des produits d'adhésion + [00:39:20][^8^][8] Session de questions-réponses * Invitation aux participants à poser des questions supplémentaires * Précision sur le support technique pour les cas spécifiques

Understanding the Risks and Steps to Clear HSTS Settings in Chrome and Firefox

Reviewer #1 (Public Review):

O'Neill et al. have developed a software analysis application, miniML, that enables the quantification of electrophysiological events. They utilize a supervised deep learned-based method to optimize the software. miniML is able to quantify and standardize the analyses of miniature events, using both voltage and current clamp electrophysiology, as well as optically driven events using iGluSnFR3, in a variety of preparations, including in the cerebellum, calyx of held, Golgi cell, human iPSC cultures, zebrafish, and Drosophila. The software appears to be flexible, in that users are able to hone and adapt the software to new preparations and events. Importantly, miniML is an open-source software free for researchers to use and enables users to adapt new features using Python.

Overall this new software has the potential to become widely used in the field and an asset to researchers. However, the authors fail to discuss or even cite a similar analysis tool recently developed (SimplyFire), and determine how miniML performs relative to this platform. There are a handful of additional suggestions to make miniML more user-friendly, and of broad utility to a variety of researchers, as well as some suggestions to further validate and strengthen areas of the manuscript:

(1) miniML relative to existing analysis methods: There is a major omission in this study, in that a similar open source, Python-based software package for event detection of synaptic events appears to be completely ignored. Earlier this year, another group published SimplyFire in eNeuro (Mori et al., 2024; doi: 10.1523/eneuro.0326-23.2023). Obviously, this previous study needs to be discussed and ideally compared to miniML to determine if SimplyFire is superior or similar in utility, and to underscore differences in approach and accuracy.

(2) The manuscript should comment on whether miniML works equally well to quantify current clamp events (voltage; e.g. EPSP/mEPSPs) compared to voltage clamp (currents, EPSC/mEPSCs), which the manuscript highlights. Are rise and decay time constants calculated for each event similarly?

(3) The interface and capabilities of miniML appear quite similar to Mini Analysis, the free software that many in the field currently use. While the ability and flexibility for users to adapt and adjust miniML for their own uses/needs using Python programming is a clear potential advantage, can the authors comment, or better yet, demonstrate, whether there is any advantage for researchers to use miniML over Mini Analysis or SimplyFire if they just need the standard analyses?

(4) Additional utilities for miniML: The authors show miniML can quantify miniature electrophysiological events both current and voltage clamp, as well as optical glutamate transients using iGluSnFR. As the authors mention in the discussion, the same approach could, in principle, be used to quantify evoked (EPSC/EPSP) events using electrophysiology, Ca2+ events (using GCaMP), and AP waveforms using voltage indicators like ASAP4. While I don't think it is reasonable to ask the authors to generate any new experimental data, it would be great to see how miniML performs when analysing data from these approaches, particularly to quantify evoked synaptic events and/or Ca2+ (ideally postsynaptic Ca2+ signals from miniature events, as the Drosophila NMJ have developed nice approaches).

Reviewer #2 (Public Review):

Summary:

This paper presents miniML as a supervised method for the detection of spontaneous synaptic events. Recordings of such events are typically of low SNR, where state-of-the-art methods are prone to high false positive rates. Unlike current methods, training miniML requires neither prior knowledge of the kinetics of events nor the tuning of parameters/thresholds.

The proposed method comprises four convolutional networks, followed by a bi-directional LSTM and a final fully connected layer which outputs a decision event/no event per time window. A sliding window is used when applying miniML to a temporal signal, followed by an additional estimation of events' time stamps. miniML outperforms current methods for simulated events superimposed on real data (with no events) and presents compelling results for real data across experimental paradigms and species.

Strengths:

The authors present a pipeline for benchmarking based on simulated events superimposed on real data (with no events). Compared to five other state-of-the-art methods, miniML leads to the highest detection rates and is most robust to specific choices of threshold values for fast or slow kinetics. A major strength of miniML is the ability to use it for different datasets. For this purpose, the CNN part of the model is held fixed and the subsequent networks are trained to adapt to the new data. This Transfer Learning (TL) strategy reduces computation time significantly and more importantly, it allows for using a substantially smaller data set (compared to training a full model) which is crucial as training is supervised (i.e. uses labeled examples).

Weaknesses:

The authors do not indicate how the specific configuration of miniML was set, i.e. number of CNNs, units, LSTM, etc. Please provide further information regarding these design choices, whether they were based on similar models or if chosen based on performance.

The data for the benchmark system was augmented with equal amounts of segments with/without events. Data augmentation was undoubtedly crucial for successful training.

(1) Does a balanced dataset reflect the natural occurrence of events in real data? Could the authors provide more information regarding this matter?

(2) Please provide a more detailed description of this process as it would serve users aiming to use this method for other sub-fields.

The benchmarking pipeline is indeed valuable and the results are compelling. However, the authors do not provide comparative results for miniML for real data (Figures 4-8). TL does not apply to the other methods. In my opinion, presenting the performance of other methods, trained using the smaller dataset would be convincing of the modularity and applicability of the proposed approach.

Impact:

Accurate detection of synaptic events is crucial for the study of neural function. miniML has a great potential to become a valuable tool for this purpose as it yields highly accurate detection rates, it is robust, and is relatively easily adaptable to different experimental setups.

Additional comments:

Line 73: the authors describe miniML as "parameter-free". Indeed, miniML does not require the selection of pulse shape, rise/fall time, or tuning of a threshold value. Still, I would not call it "parameter-free" as there are many parameters to tune, starting with the number of CNNs, and number of units through the parameters of the NNs. A more accurate description would be that as an AI-based method, the parameters of miniML are learned via training rather than tuned by the user.

Line 302: the authors describe miniML as "threshold-independent". The output trace of the model has an extremely high SNR so a threshold of 0.5 typically works. Since a threshold is needed to determine the time stamps of events, I think a better description would be "robust to threshold choice".

Reviewer #3 (Public Review):

miniML as a novel supervised deep learning-based method for detecting and analyzing spontaneous synaptic events. The authors demonstrate the advantages of using their methods in comparison with previous approaches. The possibility to train the architecture on different tasks using transfer learning approaches is also an added value of the work. There are some technical aspects that would be worth clarifying in the manuscript:

(1) LSTM Layer Justification: Please provide a detailed explanation for the inclusion of the LSTM layer in the miniML architecture. What specific benefits does the LSTM layer offer in the context of synaptic event detection?

(2) Temporal Resolution: Can you elaborate on the reasons behind the lower temporal resolution of the output? Understanding whether this is due to specific design choices in the model, data preprocessing, or post-processing will clarify the nature of this limitation and its impact on the analysis.

(3) Architecture optimization: how was the architecture CNN+LSTM optimized in terms of a number of CNN layers and size?

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Résumé de la vidéo [00:00:05][^1^][1] - [00:21:15][^2^][2]:

Cette vidéo présente une conférence de Giovanny Lau sur l'amélioration de la mémoire de travail par la métacognition. Il explore les modèles de mémoire de travail, la fragilité et les limites de celle-ci, et comment les jugements métacognitifs peuvent influencer la performance de la mémoire.

Points forts: + [00:00:16][^3^][3] Introduction de Giovanny Lau * Présentation de son parcours académique * Annonce du sujet de la thèse sur la mémoire de travail + [00:00:50][^4^][4] Définition de la mémoire de travail * Explication des différents modèles et de l'importance de la mémoire de travail * Discussion sur la fragilité et les limites de la mémoire de travail + [00:04:08][^5^][5] Métacognition et mémoire de travail * Présentation du modèle conceptuel de la métacognition * Importance des jugements métacognitifs dans l'évaluation de la mémoire de travail + [00:07:31][^6^][6] Expériences et résultats * Description des expériences menées pour étudier l'impact des jugements métacognitifs * Présentation des résultats montrant l'amélioration de la mémoire de travail grâce à la métacognition + [00:14:14][^7^][7] Analyse des positions sérielles * Exploration des effets des jugements métacognitifs sur différentes positions sérielles * Hypothèses sur la manière dont les jugements métacognitifs pourraient aider à allouer les ressources + [00:19:53][^8^][8] Implications théoriques et pratiques * Discussion sur les implications pour la compréhension de la mémoire de travail * Suggestions pour l'intégration de tâches métacognitives dans les programmes éducatifs

Résumé de la vidéo [00:00:04][^1^][1] - [00:16:38][^2^][2]:

La vidéo explore la perception des qualités restauratrices des environnements naturels et urbains à travers le prisme de la vie humaine, en se concentrant sur les liens sociaux, la présence, la peur et la sécurité émotionnelle. Elle souligne l'importance des relations interpersonnelles et de la théorie de l'attachement dans le développement de l'individu.

Points forts: + [00:00:12][^3^][3] Les liens sociaux * L'importance des expériences et des rencontres * L'influence des interactions sur notre bien-être * La notion d'interdépendance et de connexion sociale + [00:02:21][^4^][4] La présence et l'attention * La force de la présence dans les relations parent-enfant * L'impact de la grossesse psychique sur la parentalité * La nécessité d'être pleinement présent dans la vie + [00:07:36][^5^][5] La peur et la sécurité émotionnelle * La peur comme émotion influente dans nos vies * La régulation des peurs pour une vie épanouie * Le besoin fondamental de sécurité émotionnelle + [00:11:50][^6^][6] La théorie de l'attachement * L'importance des premières années de vie dans la construction des relations * L'impact de la qualité des liens et de l'environnement sur le développement * La sécurité émotionnelle comme enjeu sociétal et individuel

Résumé de la vidéo [00:00:00][^1^][1] - [00:47:58][^2^][2]:

Esther Duflo présente une expérimentation en Inde sur l'application des mathématiques dans l'éducation et la vie réelle. Elle discute des défis de l'enseignement au collège et au lycée, en particulier dans les pays pauvres, et explore la différence entre les compétences mathématiques abstraites et leur application pratique sur les marchés.

Points forts: + [00:00:23][^3^][3] Contexte de l'éducation * Importance de l'éducation au collège et au lycée * Difficultés rencontrées par les enseignants face à des élèves ayant de faibles connaissances * Tension entre l'éducation des enseignants et les opportunités sur le marché du travail + [00:14:03][^4^][4] Étude sur les compétences mathématiques * Comparaison entre problèmes mathématiques standards et appliqués * Difficultés des élèves à appliquer des compétences abstraites dans des situations réelles * Faible pourcentage d'élèves capables de résoudre des problèmes pratiques malgré de bonnes compétences abstraites + [00:17:00][^5^][5] Performance des enfants sur les marchés * Les enfants sur les marchés réalisent des calculs complexes rapidement et avec précision * Différence entre les compétences mathématiques utilisées à l'école et celles utilisées dans la vie quotidienne * Hypothèse sur l'impact de l'incitation et de l'intérêt sur la performance des enfants + [00:25:02][^6^][6] Expérience sur les marchés * Enfants effectuant des transactions avec des légumes et rendant la monnaie correctement * Comparaison de la performance des enfants sur les marchés avec celle à l'école * Influence de la familiarité avec les problèmes sur la capacité à les résoudre + [00:31:54][^7^][7] Stratégies de calcul des enfants * Utilisation de stratégies rudimentaires mais efficaces sur les marchés * Difficultés rencontrées par les enfants lorsqu'ils essaient d'appliquer des algorithmes scolaires * Importance de la pression et des conditions réelles pour la performance des calculs + [00:40:22][^8^][8] Amélioration des compétences par la pratique * Les enfants des marchés montrent une meilleure capacité à résoudre des problèmes concrets * Tentatives infructueuses de transférer des stratégies efficaces de la vie réelle à des problèmes abstraits * Difficulté à changer les approches des enfants face à des problèmes abstraits

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Résumé de la vidéo [00:00:00][^1^][1] - [00:47:31][^2^][2]:

Cette vidéo présente une conférence d'Esther Duflo sur les approches expérimentales en éducation, mettant l'accent sur le développement de la cohésion sociale et la réussite scolaire. Elle discute de divers programmes internationaux visant à améliorer la coopération, la confiance et la tolérance parmi les élèves, et souligne l'importance de ces compétences pour le bien-être économique et social à long terme.

Points forts: + [00:00:23][^3^][3] Introduction de la conférence * Présentation par Esther Duflo * Importance de la recherche sur la cohésion sociale * Lien entre cohésion sociale et réussite scolaire + [00:09:03][^4^][4] Programme en Turquie * Développement de la cohésion sociale dans les écoles primaires * Intégration des réfugiés syriens * Utilisation de la prise de perspective + [00:17:34][^5^][5] Mesure de la violence * Utilisation de carnets de bord pour observer la violence * Interrogation des enseignants et élèves sur les comportements problématiques * Jeux comportementaux pour mesurer la coopération et l'altruisme + [00:25:32][^6^][6] Suivi à long terme * Impact sur l'emploi, les revenus et la criminalité * Baisse de l'impulsivité et augmentation de la confiance * Amélioration des parcours scolaires et professionnels + [00:38:56][^7^][7] Programme en France * Projet civique en groupes * Expérience directe de la démocratie * Amélioration des performances scolaires et des attitudes civiques + [00:47:01][^8^][8] Conclusion * Effets positifs à court et long terme * Bénéfices supérieurs aux coûts * Importance de la cohésion sociale pour la société

Résumé de la vidéo [00:00:00][^1^][1] - [00:44:48][^2^][2]:

Cette vidéo présente une conférence d'Esther Duflo sur les approches expérimentales en éducation, où elle discute des groupes de niveau, de l'enseignement personnalisé et de l'hétérogénéité des niveaux scolaires des élèves. Elle explore les effets des classes de niveau sur les progrès des élèves et examine des alternatives comme le tutorat et les plateformes d'apprentissage.

Points forts: + [00:00:22][^3^][3] Introduction de la conférence * Présentation par Mac Gurgan, directeur du programme innovation données expérimentation en éducation * Discussion sur les groupes de niveau et l'enseignement personnalisé * Importance de la recherche dans ces domaines + [00:01:22][^4^][4] Hétérogénéité des niveaux scolaires * Constat de la grande hétérogénéité des niveaux scolaires des élèves * Organisation du système scolaire pour réduire cette hétérogénéité * Examen de l'histoire de l'éducation en France et des choix faits dans les années 70 + [00:10:50][^5^][5] Effets des classes de niveau * Analyse des progrès des élèves dans les classes de niveau au premier degré * Étude des données de laadep et constatation de l'absence de différence significative * Discussion sur le redoublement et ses effets sur les élèves + [00:25:15][^6^][6] Évaluation des regroupements par niveau * Bilan de la littérature sur les effets du regroupement des élèves par niveau * Indices d'effets négatifs pour les plus faibles et positifs pour les plus forts * Exemple d'une étude sur l'enseignement des maths au primaire + [00:32:13][^7^][7] Considérations sur les effets de pair * Influence de la qualité académique des pairs sur les progressions des élèves * Implications des regroupements par niveau sur les inégalités entre élèves * Importance de prendre en compte le contexte et les caractéristiques des élèves + [00:41:11][^8^][8] Alternatives aux classes de niveau * Présentation du tutorat et des plateformes d'apprentissage comme solutions * Discussion sur l'efficacité du tutorat et les projets en cours * Évaluation des plateformes d'apprentissage et leur potentiel pour gérer l'hétérogénéité

Résumé de la vidéo [00:00:00][^1^][1] - [01:07:34][^2^][2]:

Cette vidéo présente une session de formation sur les droits de l'enfant à l'école, animée par François Ogier de l'atelier Canopé du Cantal. La session aborde l'importance de la formation continue des enseignants, le partenariat entre Réseau Canopé et UNICEF France, et les ressources pédagogiques disponibles pour intégrer les droits de l'enfant dans l'éducation.

Points forts: + [00:00:13][^3^][3] Introduction et contexte * Présentation de l'animateur et du sujet * Importance de la formation des enseignants * Partenariat entre Réseau Canopé et UNICEF + [00:03:40][^4^][4] Perspectives des enfants sur l'école * Enfants partageant leur vision de l'école * L'école comme lieu d'apprentissage et de citoyenneté * Importance de l'accueil et du respect + [00:05:13][^5^][5] Discussion sur la formation et les ressources * Présentation des intervenants et de leurs rôles * Ressources pour l'accompagnement des droits de l'enfant * Kits pédagogiques adaptés aux différents niveaux scolaires + [00:25:26][^6^][6] Objectifs et mise en œuvre des droits de l'enfant * Intégration des droits de l'enfant dans tous les temps d'apprentissage * Rôle des acteurs éducatifs et territoriaux * Accès à la plateforme UNICEF Academy pour les ressources + [00:32:44][^7^][7] Approche pédagogique et activités * Sensibilisation à la participation et à l'expression des élèves * Utilisation de ressources pour aborder des sujets historiques et actuels * Importance de l'interdisciplinarité dans l'enseignement des droits

Résumé de la vidéo [00:33:00][^1^][1] - [01:06:32][^2^][2]:

Cette vidéo présente une discussion sur les droits de l'enfant à l'école, en mettant l'accent sur la formation et l'accompagnement de la communauté éducative. Elle souligne l'importance de l'engagement des élèves dans des actions concrètes et la promotion des valeurs républicaines.

Points forts: + [00:33:00][^3^][3] Participation des élèves * Importance du vote des délégués de classe * Expression des souhaits et des idées par les élèves * Interaction avec la direction et la mairie pour réaliser des projets + [00:47:00][^4^][4] Respect des surnoms et des identités * Sensibilisation sur l'usage inapproprié des surnoms * Droit des enfants à refuser des surnoms non désirés * Importance de l'acceptation et du respect des identités individuelles + [00:58:52][^5^][5] Activités pédagogiques liées aux programmes * Intégration des droits de l'enfant dans l'enseignement * Développement des compétences de participation chez les élèves * Utilisation de ressources historiques pour enseigner les droits de l'enfant + [01:05:24][^6^][6] Lien avec l'histoire et l'actualité * Évocation de l'âge industriel et du travail des enfants * Réflexion sur les discriminations passées et présentes * Analyse critique de la situation des enfants à travers l'histoire

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That's scary. I mean if some states are willing to put a women's reproductive health in jeopardy, what will some of these states regulate/deregulate as it relates to access to food. for the poor.

This is because the cost of healthy whole foods is expensive (especially when we consider the total costs of a week's worth of groceries) and the time it takes to make healthy meals is limited depending on the structural dynamic of the family, working hours, and energy levels.

開くカッコが抜けています。

ここは翻訳し忘れ？

メッセージを翻訳し忘れているようです。

これは翻訳し忘れ？

余分なバッククォートが入っているようです。

原文にはこれはないのでトルでよさそうです。

原文にはこれはないのでトルでよさそうです。

I am not certain that I agree with this. It seems to suggest that a digitized item becomes an entirely different entity once it is placed in the digital environment rendering it useless to traditional methods of analysis. While that is possible I suppose, It makes more sense to me that digitized and digital born items are available to different methods of analysis perhaps based on the conceptual foundations of traditional methods such as sociology, history, etc, but tweaked for their new iteration and environment.

The two strategies Hayles has pointed out, assimilation and distinction, are the 2 most distinct and dissimilar options. However, the digital environment is both a distinct domain, or rather, an extended domain of Humanities, and also provides a tool set that can be utilized by traditional and digital humanities. Given the significant overlap in domains between the two as well as the ever evolving differences brought about by continually advancing technology, traditional and digital humanities will almost certainly have to meet in the middle in some fashion.

I haven't had a chance to read Davidson's article yet so I may be misunderstanding or extrapolating in ways that are addressed by the article. However, "decentering authority and credentialing practices" seems a slippery slope. I will need to read Davidson't article to understand his vision of decentering. Technology can, of course, be a key ingredient in the advancement of digital humanities, but it may be too Pollyana to envision the advances made by good actors without also considering the harm bad actors could cause. I am specifically thinking of Fake News, AI postings, and generally malicious humans that seem to argue the need for tighter or more trusted authority and centrailized credentialing.

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Transverse fractures through the hair shafts (trichoschisis).

richorrhexis nodosa is a common hair problem in which thickened or weak points (nodes) along the hair shaft cause your hair to break off easily

Trichothiodystrophy (TTD) is a rare autosomal recessive multisystem disorder characterized by sulfur-deficient brittle hair, mental and physical retardation, ichthyosis, and, in many patients, cutaneous photosensitivity but no cancer incidence.

This is in contrast to telogen effluvium or hair shedding that arises during the telogen or resting stage of the hair cycle.

Anagen effluvium refers to hair shedding that arises during the anagen or growth stage of the hair cycle.

:(( you haven't even seen the worst of it

rhaegar seems so hot....sorry but like the way he's still haunting this mans thoughts LIKE

she ate that ngl

i love him taking the blame even if its the bare minium

not for long i fear

insanee ik cat also slept for that long

ooo

sighhh

i can excuse queer but gay is def intentional especially if renly and loras are banging

so hightower

he's kinda real

yuh

jaime is terroizing the elderly (ned)

lol

i hope one day it is by like dany or something

lmao but i do feel bad for him like he's been pretty nice to the starks yet they do this to him

ITS SO COOL

Cryoglobulinemia is a rare medical condition characterized by the presence of abnormal proteins called cryoglobulins in the blood, which precipitate or clump together at low temperatures. These cryoglobulins, composed of immunoglobulins and sometimes complement components, deposit in small- to medium-sized blood vessels throughout the body, causing endothelial injury and end-organ damage. Cryoglobulins can cause a range of symptoms, including joint pain, skin rashes, and kidney problems, due to their tendency to obstruct blood vessels and trigger inflammatory reactions.

Blue toe syndrome” (BTS) refers to the acute onset of purple painful digits in the absence of evident trauma, cold-associated injury or disorders that induce generalized cyanosis.

Janeway lesions are rare, non-tender, small erythematous or haemorrhagic macular, papular or nodular lesions on the palms or soles only a few millimeters in diameter that are associated with infective endocarditis and often indistinguishable from Osler's nodes.

Leukocytoclastic vasculitis is a cutaneous, small-vessel vasculitis of the dermal capillaries and venules.

Acrodynia is a manifestation of chronic mercury poisoning or idiosyncrasy to mercury. This symptom complex includes dermatological and systemic manifestations of exposure to various forms of mercury

Erythromelalgia is a rare clinical syndrome characterized by a triad of redness, warmth, and burning pain, most notably affecting the extremities. It usually affects the lower extremities (most commonly feet) or may involve upper extremities (hands) in few cases. The episodes are typically precipitated by exercise and relieved by cooling the affected parts.

Measures of [[bovine fertility]] are: - new pregnancy sooner after calving/birth (genetic selection primarily aims for this) - require fewer ART insemination attempts

Importante!

Agent架构

ok do it now.

[[❓️:]] How does intensive selection for milk yield negatively affect reproductive efficiency of cows?

[[❓️:]] How does intensive selection for milk yield negatively affect reproductive efficiency of cows?

DOI: 10.1016/j.celrep.2021.108876

Resource: RRID:Addgene_8601

Curator: @Naa003

SciCrunch record: RRID:Addgene_8601

What is this?

DOI: 10.1016/j.celrep.2021.108876

Resource: RRID:Addgene_8601

Curator: @Naa003

SciCrunch record: RRID:Addgene_8601

What is this?

DOI: 10.1016/j.xpro.2024.103129

Resource: (ATCC Cat# CRM-HTB-26, RRID:CVCL_0062)

Curator: @evieth

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1101/2024.06.06.597835

Resource: BDSC_94703

Curator: @evieth

SciCrunch record: RRID:BDSC_94703

What is this?

DOI: 10.1101/2024.06.06.597835

Resource: RRID:BDSC_94699

Curator: @evieth

SciCrunch record: RRID:BDSC_94699

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9791,RRID:BDSC_9791)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9791

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9786

Curator: @anisehay

SciCrunch record: RRID:BDSC_9786

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9787,RRID:BDSC_9787)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9787

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 23642,RRID:BDSC_23642)

Curator: @anisehay

SciCrunch record: RRID:BDSC_23642

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9785,RRID:BDSC_9785)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9785

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9780

Curator: @anisehay

SciCrunch record: RRID:BDSC_9780

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9781

Curator: @anisehay

SciCrunch record: RRID:BDSC_9781

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9780

Curator: @anisehay

SciCrunch record: RRID:BDSC_9780

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9778,RRID:BDSC_9778)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9778

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9778,RRID:BDSC_9778)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9778

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9779

Curator: @anisehay

SciCrunch record: RRID:BDSC_9779

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_23250

Curator: @anisehay

SciCrunch record: RRID:BDSC_23250

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9776,RRID:BDSC_9776)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9776

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_51847

Curator: @anisehay

SciCrunch record: RRID:BDSC_51847

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 30518,RRID:BDSC_30518)

Curator: @anisehay

SciCrunch record: RRID:BDSC_30518

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 67877,RRID:BDSC_67877)

Curator: @anisehay

SciCrunch record: RRID:BDSC_67877

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 42704,RRID:BDSC_42704)

Curator: @anisehay

SciCrunch record: RRID:BDSC_42704

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9773,RRID:BDSC_9773)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9773

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9769

Curator: @anisehay

SciCrunch record: RRID:BDSC_9769

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9768

Curator: @anisehay

SciCrunch record: RRID:BDSC_9768

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9770

Curator: @anisehay

SciCrunch record: RRID:BDSC_9770

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_9766

Curator: @anisehay

SciCrunch record: RRID:BDSC_9766

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 9764,RRID:BDSC_9764)

Curator: @anisehay

SciCrunch record: RRID:BDSC_9764

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9759

Curator: @anisehay

SciCrunch record: RRID:BDSC_9759

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9761

Curator: @anisehay

SciCrunch record: RRID:BDSC_9761

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_34670

Curator: @anisehay

SciCrunch record: RRID:BDSC_34670

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_27299

Curator: @anisehay

SciCrunch record: RRID:BDSC_27299

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9757

Curator: @anisehay

SciCrunch record: RRID:BDSC_9757

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_9758

Curator: @anisehay

SciCrunch record: RRID:BDSC_9758

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_56036

Curator: @anisehay

SciCrunch record: RRID:BDSC_56036

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_32426

Curator: @anisehay

SciCrunch record: RRID:BDSC_32426

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 43963,RRID:BDSC_43963)

Curator: @anisehay

SciCrunch record: RRID:BDSC_43963

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_35649

Curator: @anisehay

SciCrunch record: RRID:BDSC_35649

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_31493

Curator: @anisehay

SciCrunch record: RRID:BDSC_31493

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_50725

Curator: @anisehay

SciCrunch record: RRID:BDSC_50725

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_29431

Curator: @anisehay

SciCrunch record: RRID:BDSC_29431

What is this?

DOI: 10.7554/eLife.58107

Resource: (BDSC Cat# 37496,RRID:BDSC_37496)

Curator: @anisehay

SciCrunch record: RRID:BDSC_37496

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_38215

Curator: @anisehay

SciCrunch record: RRID:BDSC_38215

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_28724

Curator: @anisehay

SciCrunch record: RRID:BDSC_28724

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_38339

Curator: @anisehay

SciCrunch record: RRID:BDSC_38339

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_16876

Curator: @anisehay

SciCrunch record: RRID:BDSC_16876

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_33722

Curator: @anisehay

SciCrunch record: RRID:BDSC_33722

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_32355

Curator: @anisehay

SciCrunch record: RRID:BDSC_32355

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_34592

Curator: @anisehay

SciCrunch record: RRID:BDSC_34592

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_26314

Curator: @anisehay

SciCrunch record: RRID:BDSC_26314

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_57436

Curator: @anisehay

SciCrunch record: RRID:BDSC_57436

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_31517

Curator: @anisehay

SciCrunch record: RRID:BDSC_31517

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_63582

Curator: @anisehay

SciCrunch record: RRID:BDSC_63582

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_50587

Curator: @anisehay

SciCrunch record: RRID:BDSC_50587

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_56497

Curator: @anisehay

SciCrunch record: RRID:BDSC_56497

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_33742

Curator: @anisehay

SciCrunch record: RRID:BDSC_33742

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_41688

Curator: @anisehay

SciCrunch record: RRID:BDSC_41688

What is this?

DOI: 10.7554/eLife.58107

Resource: BDSC_29406

Curator: @anisehay

SciCrunch record: RRID:BDSC_29406

What is this?

DOI: 10.7554/eLife.58107

Resource: RRID:BDSC_8753

Curator: @anisehay

SciCrunch record: RRID:BDSC_8753

What is this?

DOI: 10.3389/fnmol.2024.1409401

Resource: (IMSR Cat# JAX_034832,RRID:IMSR_JAX:034832)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:034832

What is this?

DOI: 10.3389/fnmol.2024.1409401

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.3389/fnmol.2024.1409401

Resource: Addgene_24002

Curator: @evieth

SciCrunch record: RRID:Addgene_24002

What is this?

DOI: 10.1101/2024.06.06.597807

Resource: (IMSR Cat# JAX_021039,RRID:IMSR_JAX:021039)

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:021039

What is this?

DOI: 10.1101/2024.06.05.597539

Resource: IMSR_JAX:037340

Curator: @evieth

SciCrunch record: RRID:IMSR_JAX:037340

What is this?

DOI: 10.1038/s41467-024-49590-6

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @evieth

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1002/cam4.7384

Resource: IBM SPSS Statistics (RRID:SCR_016479)

Curator: @evieth

SciCrunch record: RRID:SCR_016479

What is this?