Birth Shock! is an AHRC-funded (AH/K003364/1) engagement-focused project exploring and enhancing the impact and reach of The Birth Project (AH/V000926/1). This study is particularly concerned with the role the arts can play in the perinatal period, especially in supporting the wellbeing of new mothers. In particular, we wished to assess the role of a suite of films exploring this subject with trainee and health-professional audiences. The Birth Project focused on four central questions.●What role might arts engagement have to play in antenatal and postnatal care?●To what extent are hospital practices, that are [potentially] iatrogenic in nature, implicated in postnatal distress?●To what extent is ‘mutual recovery’ possible through engagement with the arts, and if so, to establish what form this may take?●What, in particular, does an arts-based approach offer in exploring birth experiences and the transition to motherhood?Within AH/V000926/1) by engaging with new non-academic and trainee professional audiences. This aim has been primarily conducted through online and in-person screening workshops of The Birth Project films (AH/K003364/1). An objective has included soliciting feedback on the effectiveness of these resources in facilitating enhanced understanding of these topics via a Likert questionnaire and free-text box. We have grouped the Likert responses under The Birth Project original questions to present an overview of the effectiveness of the resources in relation to these questions.These questions were examined in a suite of films. This current research is an AHRC-funded, engagement-focused project exploring and enhancing the impact and reach of The Birth Project , or strongly agreed (43.6%) that hospital practices were implicated in postnatal distress. Within the range of mean scores by professional group, midwives (4.4), peer support workers (4.3), student midwives (4.2), and medical students [4.6%, or ●To what extent is ‘mutual recovery’ possible through engagement with the arts, and if so, to establish what form this may take?Workshop participants, across a range of professional backgrounds, reflected on their increased awareness of the ways in which hospital-based practices and clinical procedures could be implicated in postnatal distress. Several comments by students pointed to a specific increased awareness of the longevity of postnatal distress. Participants reported that the films shed new light on the ways in which parents might be continuing to process their birth experiences long after their intervention as a professional had concluded.●What, in particular, does an arts-based approach offer in exploring birth experiences and the transition to motherhood?There wasn't a specific question on this topic in our follow-up workshops questionnaire. However, the original participants did articulate a sense of relief in feeling they were not alone with their feelings. Some of the free text comments in the post workshop questionnaire did address the need to support new mothers and the value of a group experience is implicit in some of the remarks about support. One respondent felt that the group experience would be less intimidating that one-to-one therapy and that in “a group you can find connections and belonging, versus feeling alone in your experiences of grief”.Birth Shock! participants reflected on the ways in which arts-based approaches might be useful for exploring discourses around birth trauma, and their own professional roles, as part of continuing professional education and development, as well as being an integrated part of postnatal maternity service provision - see also Hogan 2021 [In gan 2021 .The majority of participants reported somewhat agreeing (43.2%) or strongly agreeing (39.8%) with the statement ‘I feel emotionally moved by the films.’ Although it is a small minority, a total of 7.3% of participants strongly or somewhat disagreed with feeling emotionally moved by the films. Across the different professional groups, the mean scores indicated a cluster of similar responses, with midwives (4.1) and student midwives (4.1) scoring similar mean responses to the films, and health visitors (3.4) scoring lower. Obstetricians reported the lowest mean score (2.7), while medical students (4.6) reported the highest incidence of feeling emotionally moved. This data suggests that these films might be particularly emotionally powerful as training resources for students.3determination to give good compassionate and patient-centred care. A large number of respondents (88.2%) reported that they are aware that hospital practices are implicated in postnatal distress. A high proportion felt empowered to change their current or future practice as a result of the training session (72.4%), which is a highly noteworthy result. Finally, the vast majority of viewers (89.3%) thought that the project resources (films) would be a useful addition to healthcare training, with a minority of more experienced respondents, feeling they were already au-fait with the issues portrayed.A number of structured workshops aimed at health-care professionals and trainees (28) collated audience responses quantitatively and in free text. The majority of film viewers felt emotionally moved by the films (83%) and a majority felt that watching the films had increased their understanding of antenatal and postnatal practices as a result of the session (58.7%) and in some cases this was a profound and revelatory shift in perspective. Other, more experienced practitioners, (around 23%), were neutral, as felt they were well aware of the issues portrayed, though positively a number reported that the film and discussion session consolidated their The films were made available freely so we do not have absolute statistics for their uptake, because they can be downloaded and screened multiple times, we can note the following: that the documentary has had 4.1K views and the film on research methods has had 5.7K views (at November 2022) comparing favourably with academic paper readership estimates. We expect these figures to continue to rise, as the films get embedded into training courses.4https://www.youtube.com/user/DrSusanHogan/videos.These films have enabled trainees and groups of practitioners to discuss a range of issues pertaining to our central questions. They have raised awareness of the usefulness of arts-based support for new mothers. The films also raise crucial questions about the ethics of practice, questions of consent, dignity in care, and explore how hospital practices are potentially implicated in postnatal distress. Feedback confirms that the resources will be useful in healthcare training. Qualitative feedback indicates that the films are of more value to less experienced practitioners and trainees. General public audiences have also reported being moved by the films. We hope this short communication will further enhance uptake of these resources. The films have also been made available freely via the University of Derby website and YouTube. Film summaries have been added to YouTube so that content is explained, making their use easier: There is no conflict of interest.


Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period. SWB indicators have been increasingly used by researchers and policymakers as measures of life satisfaction to complement traditional objective development and economic metrics2. In the meantime, research centred around SWB has grown enormously in recent years1. Studies in this field have documented strong correlations between SWB and important human outcomes3, such as health and longevity4, social relationships1, and earning5.Subjective Well-Being (SWB) is commonly defined as the combination of reflective cognitive judgments and emotional feelings in ongoing life6. For example, Gallup surveys collect well-being indicators worldwide3. Similarly, household panels such as the PSID in the US7, SOEP in Germany8, and the BHPS in the UK9 incorporate SWB measures in their questionnaires. More recently, there have been efforts to map the impact of the COVID-19 pandemic on SWB. For instance, Patrick et al. (2020) conducted a national survey in the US to measure satisfaction with many aspects of daily life during the COVID-19 period10. Although survey methods are effective in quantifying SWB, they have scalability problems, as well as significant time delay and high implementation cost11. Such limitations are especially pronounced when researchers or policymakers try to make timely evaluations of well-being changes in response to unexpected events . There is a growing interest in developing more efficient methods that allow researchers to monitor instant well-being and trace back to history with high spatial-temporal granularity.Given the importance of SWB, researchers have been putting a lot of effort into measuring SWB, mainly employing self-report interviews and surveys12. For example, Twitter, one of the major social media platforms, has over 330 million monthly active users across the world13. While this scale of data was traditionally impossible to analyse, recent developments in NLP have made it possible to automatically extract sentiment information from unstructured social media posts using high-performance computing infrastructures.The rising adoption of social media platforms worldwide, together with the advances in Natural Language Processing (NLP) techniques, provides a new and valuable complement. Every day, active social media users create content through these platforms, generating useful traces of their attitudes, beliefs, and feelings at every moment14 and validated it to have strong correlations with SWB2. The existing studies have used different methods to extract overall scores of positive and negative emotions through either word-level or data-driven methods2. For example, Passi and Motisariya (2022) measure the public sentiment toward political leaders using Linguistic Inquiry and Word Count (LIWC)15. Schwartz et al. (2019) leverage the Hedonometer Index to investigate the evolution of expressed happiness before, during, and after visits to San Francisco’s urban park system16. Lyu et al.17 uncover the sentiment trend of the Chinese population during the peak period of COVID-19 using Bidirectional Encoder Representations from Transformers (BERT)17. Although there has been significant progress in sentiment analysis, the existing studies either focus on a specific topic/event or are limited to narrow temporal and spatial extents.Researchers in the field of NLP have recently developed sentiment analysis algorithms to quantify the affective states from texts18, an expressed sentiment database having SWB implications, that can provide a high-resolution and large-scale complement to traditional survey measures of SWB. The Sustainable Urbanization Lab of MIT trains a BERT-based multi-lingual sentiment classification model based on a labelled database and applies it to a global geotagged Twitter archive19 (containing 10 billion posts) from the Center of Geographic Analysis (CGA) of Harvard. We impute a sentiment score, defined as the probability of a post being classified as a post with a positive mood, for every post and aggregate them to multiple administrative levels  daily. Figure This project aims to develop a comprehensive Twitter sentiment geographical index (TSGI)20. The work documents the causal relationship between the outbreak of COVID-19 and a steep decline in expressed sentiment followed by a slower recovery in multiple countries. This study provides an example of how our dataset can be utilized to investigate global major events.This dataset has been partly used by a previous work which is published in Nature Human BehaviorOur primary contribution lies in the development of the most comprehensive open-source geotagged Twitter sentiment database to date. In contrast to previous studies that used the Twitter sentiment for a specific research question within a particular country, our database offers open access and several advantages: (1) Model performance: By employing BERT as the embedding method, we take sentence context into account to improve the accuracy of downstream classification tasks ; (2) Multilingual computation: We utilize a consistent method for multilingual sentiment computation on a global scale, enabling cross-country comparisons across over 164 countries; (3) Temporal coverage: Our dataset encompasses the most recent five-year period, allowing researchers to examine the sentiment impacts of the most up-to-date social challenges and policies. We expect that our TSGI database will be able to support in-time investigations of expressed sentiment alterations for researchers across disciplines and be a valuable complement to traditional surveys to provide SWB insights.19 (Archive): To generate our global sentiment index, we retrieve raw tweet data from Archive, a project at the Center for Geographic Analysis (CGA) at Harvard University. CGA maintains the Geotweet Archive v2.0, a global record of tweets spanning time, geography, and language. The primary purpose of the Archive is to make a comprehensive collection of geo-located tweets available to the academic research community. The Archive extends from 2010 to 2023. More information on this dataset is available (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3NCMB6). Tweet IDs can be requested via the request form (https://gis.harvard.edu/geotweet-request-form).Geotweet Archive v2.021.The tweets in the archive are harvested using the Twitter Streaming API which allows users to stream Tweets in real-time. Only tweets that carry one or both of the spatial attributes (Coordinate and Place) are included in the Archive. Approximately 1–2% of all tweets contain such geographic coordinates although this percentage needs verification and may vary over timeCoordinates: This attribute represents the geographic location of the Tweet as reported by the user or client application. The inner coordinates array is formatted as geoJson with longitude first, then latitude.Place- Twitter place attribute when present indicates that the tweet is associated with a place. It is the geographic place as defined by the user and is usually a town name. A bounding box is determined by Twitter based on this field. We assume the centroid of this Bounding Box as the coordinates when actual GPS coordinates are not present in the tweet. Further, we find the radius of the circle of this BB to estimate the spatial error associated with the coordinates.Twitter has two attributes/objects Coordinates and Place which are associated with the spatial location of the tweet. Each of these is described in detail below:Twitter Coordinates objects orCalculated using the centroid of Bounding Box based on Twitter’s place objectLatitude and Longitude- Every tweet has a latitude and longitude field which is derived from either:GPS: Flag for whether the coordinates of tweets are taken from GPS or Place name-based bounding box. If the tweet coordinates are from GPS, then this field is marked “Yes” otherwise “No”. When both are present, the GPS coordinate takes priority. This helps us determine if the tweet coordinates are the actual GPS coordinates from the tweet or derived coordinates based on the place name.Spatial Error- Every tweet is marked with a spatial error field which is very crucial in interpreting the location of the tweets. This is an estimate of meters horizontal error for the tweet coordinates. We have assumed a 10 m spatial error for GPS coordinates whereas the error for place name-based coordinates is calculated as the Radius of a circle with the area of the bounding box.The key fields for location signatures are described below:The count of tweets mentioned in the manuscript refers to tweets harvested directly from Twitter with one or both of the spatial attributes mentioned above. We do some pre-processing of tweets to add a few additional fields/attributes  to support our research. However, this pre-processing only added new fields to each tweet and did not change the total number of tweets harvested. To ensure the data quality, we reserve the data starting from 2019, which contains an overall 4.37 billion tweets until the end of December 2022. On average, 2.99 million geotagged tweets are collected every day.Geotweets from bots’ sender names are not generated by a human with a mobile device since they are randomly scattered across the globe. After a thorough analysis of our data in January 2018, we discovered several automated tweet-bots which generate tweets with randomly spoofed coordinates. These bots appear to be randomly distributed spatially. We provide a list of the most commonly occurring bots in Table et al.22, (1) we remove any URL from the tweet since they provide no information to determine the sentiment polarity; (2) given that the chosen labelled training dataset (Sentiment 140) has stripped out all emoticons to prevent potential overfitting23, we also remove emojis from the tweets to ensure the same format of our data; (3) we replace varying user mentions with a fixed format of string (@user) to notify the model about mentioning; (4) we remove any non-alphanumeric words from the original tweets.Before feeding the Twitter text data into the language model, to ensure the quality of the results, we take several pre-processing steps. Following Pradha In addition to the steps mentioned above, another step we take is to truncate the sentence. A sentence with more words contains more information. However, it requires a larger memory to store the information and a longer time for the language model to process it. According to our statistics on a randomly selected 20 million geotagged tweets generated in 2021 from the Archive, 90% of posts have a length of fewer than 32 words, and 99% of posts have a length of fewer than 52 words . Since assigning a dummy sentiment label to each tweet ignores the delicate difference between tweets, we leverage the SoftMax25 function to assign a score between zero and one to represent the tweet sentiment intensity  corpus33, Multi-Genre Natural Language Inference (MultiNLI)34 corpus, and Semantic Textual Similarity (STS) benchmark dataset35, allowing to encode text for more than 50 languages. The selected model takes the truncated pre-processed input and generates a vector of fixed size 768 (pre-defined by the model) representing the entire tweet.For this project, we leverage a modified BERT model, i.e., Sentence-BERT (S-BERT)36. For this sentiment classification task, we specifically design a four-layer dense neural network with the Sigmoid Linear Unit (SiLU)37 as the activation function  to each of them. A wide range of models including logistic regressions and neural networks can be fitted on the labelled training dataset and give a prediction on an unlabelled Twitter postion Fig. .Fig. 4De23. The dataset was constructed by imputing sentiment on every tweet based on the occurrence of positive or negative emojis with the text. We first apply the pre-processing steps described in the previous section, then compute the S-BERT embeddings for every observation in the Sentiment 140 dataset, train a classifier on 80% of the data (training set), and test the classifier on the remaining 20% of the data (testing set). In the end, our best model is trained using an SGD optimizer with 60 epochs achieving 83% accuracy on the testing set using the aforementioned neural network structure. As the baseline, a logistic regression model without any penalty achieves 81% accuracy on the testing set.The training and testing data we use is Sentiment 140, an English-language sentiment-labelled dataset containing 1.6 million Twitter postsLabelling a single tweet or a group of tweets with a continuous score between 0 and 1 is defined as sentiment intensity assignment, where 0 represents the most negative sentiment and 1 represents the most positive sentiment. Simply generating a dummy sentiment classification of each tweet is not granular enough to capture the intensity of sentiment. Therefore, we decide to assign a sentiment score at the tweet level to achieve a better proxy.et al., (2017)38 who applies SoftMax to a computer vision classification task, we add a SoftMax layer to convert the outputs of the last dense layer into a value between 0 and 1, representing the probability of the tweet to be categorized into positive sentiment class. We use this likelihood as the sentiment score on the tweet level.The last dense layer of the neural network outputs two unbounded values representing the weight of positive and negative categories. Then, following He In this subsection, we describe how we aggregate the sentiment of Tweets to construct sentiment indices. We define the boundary of the administrative area for each tweet and conduct spatial intersection to find the corresponding spatial belongings of each tweet. Finally, we aggregate the sentiment scores to sentiment indices based on their spatial belongings.We retrieve the global administrative boundary data from the Database of Global Administrative Boundaries (GADM), which contains a minimum mapping unit of ADMIN 2 (county/city) with the global scale and is updated frequently. For our TSGI, we use GADM version 3.6 for the spatial analysis which was the latest build when we downloaded the data. In total, GADM contains 256 distinct ADMIN 0 areas (country/region); 3638 distinct ADMIN 1 areas (state/province), and 46782 ADMIN 2 areas (county/city).39 on Harvard FAS Research Computing infrastructure. For each tweet, we extract the spatial information including ADMIN 0, ADMIN 1, and ADMIN 2 from GADM, allowing us to build sentiment indices at different levels.Using the acquired GADM vectorized data, we conduct spatial intersection for each tweet using Heavy.aiBesides the aforementioned spatial information, each tweet is labelled with the date when the tweet was generated. We then average the value of sentiment scores over location and date to build the temporal local index. To enable researchers to aggregate the sentiment scores on different scales , we attach a field indicating the number of tweets for every sentiment index record in our dataset.18. Multiple comma-separated values (CSV) files are under the folder with the information shown in Table The aggregated multi-level  daily sentiment indices are available at 10.7910/DVN/3IL00Qhttps://www.globalsentiment.mit.edu/dataset, based on the latest version of the sentiment analysis model.Moreover, a visualization of the historic sentiment indices is available on Since multilingual Sentence-BERT can convert sentences in different languages with similar meanings into similar vectors, it allows us to train the model on a rich-resource language and then apply it to other languages. In our case, the model is trained on the first 80% of Sentiment 140, a pure English-language dataset that achieves 83% accuracy on the rest of the 20%. We wish to evaluate the performance of our model in different languages.40. There are 1.6 million tweets id included in this dataset together with their sentiment label in negative, neutral, and positive classes. We rehydrate tweets text using Twitter API41; clean the text using the same pre-processing techniques; vectorize the text using the same Sentence-BERT model and apply the best-trained model on positive and negative tweets to get sentiment labels to every recovered tweet. Table We retrieve a multilingual sentiment dataset for 15 languages42, to estimate the general accuracy of our model on the actual data, we conduct additional analysis. Of all the data we have in 2021, 38.66% of geotagged tweets are in English, followed by Portuguese (13.45%) and Spanish (12.42%). In total, these 15 languages cover 66.90% of all tweets in 2021. Taking the share of language as the weight, the adjusted model accuracy for the languages is 72.2%.Since the language of the tweet is not evenly distributedTo validate language use and their location we analysed the latest 50 million tweets between January and February 2023. We checked the language distribution of tweets within our sample. As shown in Table Recognizing that smaller languages are more susceptible to biases in spatial representation, we conducted a spatial concentration analysis for several such languages, namely Tibetan, Armenian, and Sindhi. Although the number of tweets these languages is fewer than 5000 within our 50 million sample, we found that around 90% of these tweets are concentrated in countries where the corresponding languages are spoken  demonstrated the validity of Twitter sentiment by correlating it with the Gallup-Sharecare Well-Being Index survey, showing that adjusted LIWC or data-driven sentiment analysis models have robust correlations with regional well-being. The BERT model we employ has shown performance improvements over dictionary-based methods like LIWC and hedonometer . However, no research to date has specifically tested the correlations between BERT sentiment indices and well-calibrated subjective well-being surveys. As we lack access to global subjective well-being surveys, we instead demonstrate convergent validity by examining the correlation between our TSGI index and the multilingual Hedonometer Happiness trend across languages. Hedonometer Happiness is a widely accepted happiness measure based on the bag-of-words approach to reflect the general sentiment trend on Twitter by language without the geotagged information. As shown in Table The performance improvement depicted above mainly focuses on expressed sentiment. Our expressed sentiment database provides useful information for investigating the spatial and temporal variations in expressed sentiment across regions, with the understanding that expressed sentiment has a correlation with underlying subjective well-being variations. Jaidka According to our definition, a sentiment score represents the possibility of a tweet being classified as a post with a positive mood. Here we investigate the relationship between sentiment score and word usage to validate our method.47. It contains more than six thousand English words with human-labelled sentiment polarity tags . For each word showing up in two quarters, we only count the occurrence of those words that contain positive or negative sentiment.First, we randomly select 2.5 million posts across 2019 and 2022 respectively and filter out non-English tweets so that the readers here can better understand the content. Then, we divide the filtered dataset evenly into four quarters according to their sentiment scores. For this analysis, only the tweets with the top 25% sentiment score (top quarter) and the bottom 25% are reserved (bottom quarter). To better show the difference between the two subsets, we introduce Linguistic Inquiry and Word Count, a dictionary to aid word usage analysis48 . For this study, the location information is critical because a major advantage of our sentiment index is the regional variation and the related policy implications. However, a recent paper has found that geotagged posts are subjected to a bias of being happier compared to non-geotagged posts since people like to attach their tweets to a specific location to record the joyous and special events50. Nevertheless, our expressed sentiment indices based on the geotagged tweets do have significant correlations with Hedonometer sentiment generated using total tweets (see Table First, this dataset is constructed based on the geotagged posts only, which accounts for approximately 1–2% of the total traffic at Twitter51. Instead of sharing the exact coordinates, according to the study, most tweets are sharing the location which can be a country, city, or point of interest. To accommodate this change and prevent the potential bias introduced by policy change, we choose the county/city level as our finest aggregation level.Second, based on the retrieved coordinates of each tweet, we decide to aggregate the tweet sentiment scores at the county/city level to represent the local sentiment instead of going to a finer level. This is due to the precision consideration since the mid of 2019. Twitter changed its policy of location sharing in 2019 to put more emphasis on personal data protectionThird, as we lack socio-demographic information on Twitter users, our database should be regarded as an expressed sentiment database for Twitter users, as opposed to the overall population. We have tested an alternative aggregation approach to mitigate biases towards certain “Tweetaholics”, that involves first aggregating sentiment indices of tweets at the user level, followed by aggregating user sentiment at different administrative regions. We find a high correlation between the indices from different aggregation methods Table . This co


Oscillations of extracellular voltage, reflecting synchronous, rhythmic activity in large populations of neurons, are a ubiquitous feature in the mammalian brain, and are thought to subserve important, if not fully understood roles in normal and abnormal brain function. Oscillations at different frequency bands are hallmarks of specific brain and behavioral states. At the higher end of the spectrum, 150-200 Hz ripples occur in the hippocampus during slow-wave sleep, and ultrafast (400-600 Hz) oscillations arise in the somatosensory cortices of humans and several other mammalian species in response to peripheral nerve stimulation or punctate sensory stimuli. Here we report that brief optogenetic activation of thalamocortical axons, in brain slices from mouse somatosensory (barrel) cortex, elicited in the thalamorecipient layer local field potential (LFP) oscillations which we dubbed “ripplets”. Ripplets originated in the postsynaptic cortical network and consisted of a precisely repeating sequence of 2‑5 negative transients, closely resembling hippocampal ripples but, at ~400 Hz, over twice as fast. Fast-spiking (FS) inhibitory interneurons fired highly synchronous 400 Hz spike bursts entrained to the LFP oscillation, while regular-spiking (RS), excitatory neurons typically fired only 1-2 spikes per ripplet, in antiphase to FS spikes, and received synchronous sequences of alternating excitatory and inhibitory inputs. We suggest that ripplets are an intrinsically generated cortical response to a strong, synchronous thalamocortical volley, and could provide increased bandwidth for encoding and transmitting sensory information. Importantly, optogenetically induced ripplets are a uniquely accessible model system for studying synaptic mechanisms of fast and ultrafast cortical and hippocampal oscillations. A ubiquitous feature of neuronal networks, in the brains of multiple mammalian species throughout the evolutionary tree, is their propensity to engage in large-scale, synchronous and rhythmic patterns of electrical activity, reflected as oscillations in the local field potential (LFP) . LFP oscAt the high end of the frequency range, ultrafast, 250–600 Hz oscillations have been observed in both hippocampus and neocortex. In the hippocampus, such oscillations  are almost exclusively paroxysmal (reviewed in We recorded ex vivo extracellular and intracellular activity evoked by optogenetic activation of channelrhodopsin (ChR2)-expressing thalamocortical axons in thalamorecipient layers of mouse somatosensory (barrel) cortex. We report here that brief light pulses (as short as 1 ms) elicited a transient (<25 ms), highly reproducible LFP oscillation or ‘ripplet,’ which closely resembled hippocampal ripples but, at ~400 Hz, was more than twice as fast. Paired whole-cell recordings from fast-spiking (FS) inhibitory interneurons and regular-spiking (RS) excitatory cells revealed precise phase relationships between FS spikes, RS spikes and ripplets, and between FS spikes and sequences of alternating EPSCs and IPSCs in RS cells, suggesting that phasic RS→RS and RS→FS excitation underlies ripplet generation. Ripplets may be a stereotypical cortical response to strong, punctate stimuli that activate thalamocortical afferents with a high degree of synchrony and could be used by the cortex to encode specific features of a sensory event. Importantly, optogenetically evoked ex vivo ripplets are a uniquely accessible model for studying the synaptic basis of fast and ultrafast network oscillations, including hippocampal ripples.We recorded extracellular and intracellular responses in layer 4 (L4) of barrel cortex brain slices, evoked by widefield optogenetic activation of thalamocortical synapses. Slices were prepared from brains of 3–7-postnatal-week-old mice of both sexes expressing ChR2 in the somatosensory thalamus . We applied brief light pulses through the epi-illumination path of the microscope, likely activating most of the thalamocortical axons and terminals within the surrounding L4 barrel. To verify that ChR2 in L4 was expressed by thalamocortical and not intracortical axons, we imaged and quantified the extent of tdTomato reporter expression in the cortex . Within A brief light pulse  evoked a stereotypical field potential wavelet in L4 . In thisSynchronous spike volleys in thalamocortical terminals generate relatively large current sinks in layer 4, and these are discernible as negative transients in the LFP . To reveRipplet waveforms, as well as their isolated presynaptic components, were remarkably consistent between animals in their temporal structure, as illustrated in Since ripplets required intact glutamatergic neurotransmission, they most likely reflected population activity within L4; but in which cells? To answer this question, we examined light-induced whole-cell responses in inhibitory FS interneurons and excitatory, RS cells, two subclasses known to receive direct thalamocortical inputs . These tA brief  light pulse at 60–90% maximal intensity elicited a strong (10–30 mV) depolarization in L4 FS cells, which, in 80% of cells , gave rise to a stereotypical burst of 2–7  spikes. Representative bursts in an example FS cell, evoked by 2 and 5 ms light pulses, are illustrated in Very high frequency: Averaged over these 25 cells, the first three interspike intervals (ISIs) were 2.4 ± 0.05, 2.2 ± 0.08, and 2.7 ± 0.13 ms (mean ± SEM), respectively, for an average burst frequency ( = 1/average ISI) of 418 ± 9.5 Hz, not significantly different from the extracellular ripplet frequency (p=0.56).Very high temporal precision: Bursts in a given cell were precisely reproducible, as illustrated in High animal-to-animal reproducibility: Across slices from different animals, the distributions of peak spike times for each of the four spikes in the burst was very narrow: 3.1 ± 0.06, 5.5 ± 0.08, 7.7 ± 0.12, and 10.3 ± 0.14 ms after light onset. This is also illustrated in Since thalamocortical axons also form a lower tier of terminations in L5B , we alsoTo examine if burst patterns were dependent on stimulus parameters (light duration and intensity), we first compared bursts elicited by 2 and 5 ms light pulses. In the example cell in We then compared the number and precision of spikes in response to different light intensities. Bursts elicited by 20 and 90% intensity, in the same example cell from What were the temporal relationships between FS spike bursts, thalamocortical volleys, and ripplets? We plotted all FS spike times in our dataset as a histogram and then modeled each spike order as a Gaussian . OverlayGiven the precise temporal relationship between FS spikes and the population activity reflected as ripplets, one would expect different FS cells in the same barrel to fire in tight synchrony with each other. We therefore examined intra-barrel FS-FS temporal relationships by simultaneous paired recordings. Indeed, when stimulated by a light pulse, simultaneously recorded FS-FS pairs exhibited highly precise spike synchrony. In the two example pairs shown in We calculated the JBSI matrices for six FS-FS pairs from which at least 100 spikes were recorded per cell. The averaged horizontal and vertical cross-sections through the six matrices are plotted in What accounted for this highly precise FS-FS synchrony? Precise spike synchrony is most often attributed to electrical coupling via gap junctions , inhibitTo examine directly and in more detail the temporal relationship between FS spike bursts and ripplets, we recorded light-evoked bursts in nine FS cells , simultaneously with the LFP in the same barrel. Three examples from different animals are illustrated in Population activity reflected in the LFP is likely to be dominated by excitatory (RS) cells, which are the majority (~85%) cell type in L4 . We recoo, and assigning phase angles of –180o and 180o to the negative ripplet peaks preceding and following the trough, respectively. We then assigned a phase angle to each of the 28 FS spikes and 13 RS spikes observed in these experiments by interpolating its time of peak between the nearest ripplet trough and peak –82o, translating to a range of 0.65 ms), and RS spikes occurring at a median phase angle of 161o . Thus, on average, FS spikes preceded RS spikes by ~135o or ~0.9 ms. While this appears inconsistent with the order of firing during hippocampal ripples, in which FS cells lag behind pyramidal cells by about 100o  occurred 1.5 ms after RS spikes, exactly as in the hippocampus. Notably, a 1.5 ms lag is precisely the time it takes for RS cells to generate monosynaptic EPSPs in FS cells, and for postsynaptic FS cells to reach threshold and fire  by assigning each ripplet trough a phase angle of 0and peak . The phaout 100o , 100o ofand fire . Thus, tTo examine the temporal relationships between FS and RS cell responses directly, we performed simultaneous recordings from six FS-RS pairs , switching the RS recording mode between current and voltage clamp to reveal both spikes and postsynaptic currents, respectively. When in voltage clamp, we held the RS cells between –50 and –55 mV to distinguish between EPSCs (negative deflections) and IPSCs (positive deflections). We also tested recorded pairs for direct synaptic connections between them. Two example pairs are shown in 0 and RS IPSC onsets as 180o (o (10–90th percentile range: 5–106o). Therefore, given the 2.4 ms oscillation period, each FS spike occurred about 0.4 ms after the onset of an EPSC in the RS cell and 0.8 ms before the onset of an IPSC in the same RS cell. The most parsimonious interpretation for these temporal relationships is that RS and FS cells received a barrage of shared EPSPs at the ripplet frequency; these EPSPs elicited short-latency (0.4 ms) spikes in the FS cells, and FS spike volleys, in turn, elicited IPSPs in RS cells after a 0.8 ms monosynaptic delay. As described above, RS spikes occurred, on average, 0.9 ms after the previous FS spike volley, implying that RS cells reached firing threshold nearly simultaneously with the arrival of a wavefront of inhibition, leaving a very narrow ‘window of opportunity’ for RS cells to fire. The earliest cohort of RS cells to reach spike threshold fired while ‘laggards,’ cells slightly slower to reach threshold, were preempted from firing by the incoming IPSP volley. This explains why only a fraction of RS cells fired on any given cycle and also explains how synchronous RS firing was maintained from one cycle to the next.To quantify the temporal relationships between FS spikes and E-I alternations in RS cells, we assigned a phase angle to each of the 16 FS spikes observed in this dataset by defining RS EPSC onsets as 0 as 180o . The medA antagonist gabazine applied from an extracellular patch pipette placed in the same barrel. As observed in some studies of hippocampus ripples , ultrafast (~400 Hz) network oscillations, which we refer to as ‘ripplets,’ elicited in thalamorecipient cortical layers by brief (as short as 1 ms) optogenetic stimulation of thalamocortical axons and terminals. The oscillation was reflected in the extracellular voltage (the LFP) as 2–5 negative waves that immediately followed the initial current sink generated by the thalamocortical volley but were dependent on postsynaptic activity within L4 . In wholTaken together, these observations suggest the following synaptic mechanisms for ripplet generation . The higAccording to this model, both phasic inhibition and phasic excitation, reverberating at the same ultrafast frequency but in antiphase with each other , are necAlthough not nearly as widely reported or intensively studied as hippocampal ripples, neocortical ripplet-like oscillations have been observed previously in both humans and animals, either in response to electrical stimulation of peripheral nerves, or in response to strong, punctate sensory stimuli that are likely to activate afferents in a highly synchronous manner. In humans, noninvasive EEG and MEG scalp recordings revealed fast, transient oscillations  in response to electrical stimulation of peripheral nerves, riding over the N20 peak, which reflects the monosynaptic cortical response  and in four different species , supports the conclusion that the ripplets we observed in yet another species, the mouse, were not an artifact of our ex vivo preparation or optogenetic stimulation, but a manifestation of a ubiquitous intrinsic property of sensory cortices – their propensity for generating ultrafast oscillations in response to strong, synchronous activation of their afferents. In our experiments, the light pulse likely activated with a high degree of synchrony most, if not all thalamocortical terminals impinging on the recorded neurons. Strong synchrony is a well-documented feature of the sensory responses of thalamocortical neurons in vivo, and the degree of thalamic synchrony may be the most important determinant of the strength of the downstream cortical response . PreciseIf ripplets are an intrinsic feature of sensory neocortex, why have they not been more widely reported in vivo? A technical reason could be the sensitivity of these oscillations to inhalation anesthesia , but it There are many similarities but also a few notable differences between the ripplet events described here and ripples described in the CA1 and CA3 fields of the hippocampus  oscillation, even if additional, interneuron-autonomous mechanisms (see next section) contributed to FS-FS synchrony.Although widely studied, the mechanisms pacing hippocampal ripples are still under debate . West Virginia University has a PHS-approved Animal Welfare Assurance D16-00362 (A3597-01).We crossed KN282 mice (MMRRC strain 036680-UCD), in which neurons in the ventrobasal (VB) complex of the thalamus express Cre recombinase , with th2PO4 1.25, MgCl2.6H2O 10, CaCl2 0.25, KCl 2.5, NaHCO3 26, and D-glucose 11, pH 7.4. Thalamocortical brain slices . Slices were incubated for at least 30 min at 32°C and then at room temperature until use. For recording, individual slices were transferred to a submersion recording chamber and continuously superfused with 32°C oxygenated ACSF at a rate of 2–3 ml/min.Mice were decapitated under deep isoflurane anesthesia, the brains removed and submerged in ice-cold, sucrose-based artificial cerebrospinal fluid (ACSF) containing the following (in mM): sucrose 206, NaHn slices  of somat2 0.1, HEPES 10, EGTA 1.1, Mg-ATP 4, phosphocreatine-Tris 10, and 2 mg/ml biocytin, adjusted to pH 7.25 and 290 mOsm. Layer 4 and 5B FS and RS cells were identified visually by soma size and shape and targeted for single or dual whole-cell recordings using a MultiClamp 700B amplifier . Upon break-in, cells were routinely tested by a standardized family of incrementing 600-ms-long intracellular current steps in both negative and positive directions. Upon strong depolarization, FS cells invariably reached steady-state firing rates of 150–300 Hz and their spike trains showed no or little frequency adaptation, while RS cells typically fired an initial fast doublet, followed by a slower, steady-state firing at <50 Hz  under a 40x water immersion objective. For whole-cell recordings, glass micropipettes  were filled with an intracellular solution containing (in mM): K-gluconate 134, KCl 3.5, CaClt <50 Hz . In postt <50 Hz . In dualIntracellular recordings not passing our minimal quality criteria , were excluded from further analysis and reporting. Additional inclusion criteria for each analysis are stated in the ‘Results’ section. No datapoints  within the stated inclusion criteria were excluded from the analysis, figures, or reported results.2. In the course of the experiment, light intensity was adjusted to 10–90% of maximal intensity. Stimuli at each light level were typically repeated 8–12 times at 6–10 s intervals.To activate thalamocortical axons and terminals, pulses of white light from a high-power LED light source  were delivered through the epi-illumination light path and a GFP filter cube. Maximal illumination intensity of the Prizmatix light source was 1.0 mW, as measured in air at the focal plane of the 40x objective. Assuming the full light energy was spread evenly over the field of view of the 40x objective (550 μm diameter) and no light scattering in water, the estimated maximal illuminance at the tissue was 4.2 mW/mmβ = 2. Unlike many other synchrony measures, the JBSI is independent of firing rates and firing rate differentials, and is not sensitive to slow co-modulations in these rates. See https://github.com/aricagmon/JBSI-codes  before or after a spike in the longer train. Chance synchrony is the expected (i.e. average) synchrony remaining after shifting each spike in the shorter train by a random jitter ≤J, J being the ‘jitter window,’ and is calculated analytically rather than by Monte Carlo simulations. The JBSI is the difference between the observed and chance synchrony, that is, the magnitude of synchrony destroyed by the jitter, multiplied by a normalization factor β which limits the JBSI to values between 1 (highest possible synchrony) to -1 (lowest possible synchrony), with 0 indicating purely chance synchrony. Here we used J = 2·SW and Unless noted otherwise, exact p-values were computed using distribution-free, non-parametric permutation tests by performing 10,000 random permutations of the data and calculating the fraction of permutations resulting in equal or more extreme values of the relevant statistic under both tails . When no This study examines the potential neuronal basis for generating ultrafast oscillations (250-600Hz) in the cortex evoked by optogenetic stimulation of thalamocortical afferents in ex vivo brain slices. The authors proposed that these oscillations correlate with sensory stimulation and may be relevant for the perception of relevant sensory inputs and they combined ex-vivo whole-cell patch-clamp recordings, local field potential (LFP) recordings, and optogenetic activation of thalamic afferents to generate ripple-like extracellular waveforms in the cortex, referred to as "ripplets." The authors described the sequences of RS and FS neuron discharge and how they phase-locked to the ripplet, providing a model for the cellular mechanism generating the ripplet. The authors also cited the literature about ultrafast oscillations and carefully compared the novel ripplets to the well-known hippocampal ripples. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Laura Colgin as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: John R Huguenard (Reviewer #2); Laszlo Acsady (Reviewer #3).Thank you for submitting your article "Ultrafast (>400 Hz) network oscillations induced in thalamorecipient cortical layers by optogenetic activation of thalamocortical axons" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Based on the attached reviewers' comments, all the reviewers agree that this manuscript is of potential interest to the audience studying the mechanisms of high-frequency cortical oscillations and their role in sensory information transfer. All three reviewers also agree that the proposed method provides a successful ex vivo model to study the ultrafast oscillation-mediated circuitry and cellular mechanisms and that the data reasonably support most of the claims by the authors in this manuscript.However, all three reviewers also raised concerns in common about what extent the data could be interpreted based on the present data.Firstly, they raised the concern that the experiments claiming the spread of phasic activity from Layer 4 RS to Layer 5 FS cells can not be accepted as entirely conclusive as it is at the current state. In more detail, the horizontal cut severed the L4 RS to L5 FS connections and many TC inputs to the L5 FS apical dendrites and the axons of L4 FS cells to L5 FS cells which can be pivotal in the translaminar spread. The observation that L5 FS spike bursts decrease after cutting the L4/L5 boundary could be interpreted in other ways. The current interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities that are phase-locked with the ripplets observed in L4. As it is outside the main scope of the paper, it could be toned down or dropped. Alternatively, this part should be redone with appropriate control experiments with the angled slice-cutting methods and the experiments suggested by the reviewers.The second common concern is the interpretation of nearby FS synchrony induced by TC activation. The authors described the phase-locked responses of FS and RS cells with local ripple oscillations to optogenetic activation and highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4. This issue may be addressed by rephrasing and stating that Opto activation of TC creates highly synchronized inputs to the L4, which might further help the ultrafast synchrony.Throughout the manuscript, the authors understated the GABAergic mechanism by FS cells, whereas the highly  synchronous stimulation of many TC axons and terminals would activate FS cells which are GABAergic neurons.Thirdly, as the authors proposed a circuit model, it would be ideal that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular spiking neurons to support their circuit model. However, if it takes too much effort to test an in silico model, it can be considered as an option for the authors.eLife.There are more major concerns raised by the reviewers below, which should be addressed appropriately before we can consider publication in Should further data analysis, figures revision, and text rationale allow you to address these criticisms, we would be happy to review a revised manuscript.In the meantime, we hope you will find our referees' comments helpful. Please do not hesitate to contact us if there is anything you would like to discuss.Reviewer #1 (Recommendations for the authors):1) The authors should consider the logical flow when presenting the synchrony of the FS-FS spike burst. It would strongly support the main idea of the manuscript if the authors rephrase or/and rearrange the corresponding text and data.2) Please provide detailed information on the viral vector of JAWS used for the experiment of suppressing L4 RS cell firing in the methods section, and also present expression image and spike data in figures to support the idea for the subsequent experiment performed in L5.Reviewer #2 (Recommendations for the authors):1) While the authors are generally cautious in their interpretation of the optogenetic approach and how realistic it might be, we think that this issue deserves more direct discussion. In some ways, the high degree of nearby FS synchrony induced by TC optical activation is not at all surprising given what is already known about the potency of TC inputs onto FS cells (the senior author's own work and that of many other labs), and the highly  synchronous stimulation of many TC axons and terminals onto FS cells. The rare literature examples of high-frequency sensory evoked responses that the authors cite are arguably not simple sensory activity at all  The results regarding the propagation of high-frequency events to layer 5 are arguably outside the main scope of the paper, and could potentially be dropped. As is, many controls that have not been provided would need to be added. For example, demonstrating a complete lack of optogenetic effects in layer 4 with Jaws to rule out that there might actually be low levels of expression that could contribute to the results. In addition, ruling out the interaction between the blue and red light stimuli in the two pathways is required, as the spectral overlap for excitation spectrum is not as clean as one would need to unambiguously interpret these results.Reviewer #3 (Recommendations for the authors):1) In order to prove the critical role of FS-FS interactions in timing the oscillation the authors may consider GABA-A receptor blockers and/or optogenetic inhibition of PV cells.2) To gain further insights into the excitatory background of the oscillation I would propose performing RS-RS pair recordings. In this configuration, the kinetics and strength of their interactions could be measured to establish whether this is fast and strong enough to provide a drive for the oscillation. RS data similar to FS cells shown in Figure 2 (variable stimulation intensity vs jitter) could also help to elucidate the mechanisms. Using voltage-clamp experiments in RS cells could help to resolve the origin of the excitatory drive of the oscillation (TC or RS-RS interactions). Optimally experimental work would be coupled with in silico simulations to test which network activity may underly the oscillation.3) In order to make predictions for future research concerning the role of this ultrafast oscillation it would be advisable to test its postsynaptic effects on targets that do not receive direct thalamic inputs (i.e. upper layer 2/3 cells). This would not only allow more insights into the generation of the oscillation but could test whether the oscillation is indeed able to select a subset of L2/3 cells as suggested. For a translaminar spread to layer 3 measuring the strength and variability of the oscillation in layer 2/3 pyramidal cells, which are not directly contacted by thalamic inputs, would greatly enhance the predictive power of the paper for future research.Thank you for submitting your revised manuscript titled "Ultrafast (400 Hz) network oscillations induced in middle cortical layers by optogenetic activation of thalamocortical axons." We appreciate the effort you have made to improve your manuscript, and we have carefully reviewed it.We are pleased to see that the authors have addressed most of the comments made by the reviewers. The manuscript has been improved, and the unnecessary distracting parts have been removed, resulting in a smoother flow of data. However, we would like to draw your attention to a few critical comments that still need to be addressed.Firstly, the authors should include a discussion on how the strong optogenetic approaches used in this paper might or might not be relevant to the actual sensory processing in the brain circuit, given that the current data is obtained from brain slices and artificially synchronous wide-field optogenetic activation. We believe that this is an essential point to address, and we encourage you to include an appropriate discussion in the manuscript.Secondly, the authors should discuss in more detail the potential functional consequences and function of ultrafast brain oscillation in vivo, given that this manuscript focuses on the cellular nature of high-frequency oscillation in vitro. This is an excellent suggestion, and we believe that including a discussion on this topic will enhance the impact of your manuscript.We appreciate the valuable contribution that your manuscript has made, and we believe that addressing these comments on functional significance will further improve the quality and impact of your work. We have appended the full comments from the reviewers below for your reference.Reviewer #2 (Recommendations for the authors):We agree that this is a carefully controlled study that examines the mechanisms through which very high-frequency LFP activity can be evoked in sensory cortical slices. The recordings are elegant, with combined extracellular and multi-intracellular data, and high quality and the results clearly lead to the conclusion that this in vitro activity is driven by RS cell firing.However, regarding original major point #1, we do not share the opinion of the authors that these are well linked to naturalistic sensory stimuli. In all the papers cited to support this, the main means of activating cortical response is electrical shocks either delivered directly to peripheral sensory nerves , or to the snout (piglets). Baker found LFP wavelets with electrical shocks to peripheral nerves but was not surprised by this (their last paragraph), as it presumably results from a particular form of artificial activation. They did find multiple spikes (perhaps consistent with LFP wavelets) with tactile sensory responses, but notably did not observe LFP correlates to these more naturalistic stimuli. Jones found wavelets, particularly with simultaneous stimulation of multiple vibrissae, but also looked at more controlled stimuli of multiple whiskers. This last paper is the only one that comes close to reporting cortical high-frequency wavelets with what might be considered natural sensory.What the authors might consider "exceptionally salient stimuli" (line 72) is a bit unclear, but if by this they mean response to an electrical shock, we would argue this does indeed result from abnormal sensory recruitment, and presumably would not be part of sensory processing in 99.9% of contexts. The issue is that optogenetic stimulation artificially increases synchrony perhaps even more than electrical peripheral stimulation, as all the infected TC axons are presumably rapidly and synchronously activated by the light stimulus. Thus the abstract, and related text in the introduction and discussion needs to be modified to reflect the proper context. The following abstract statement "At the higher end of the scale, ultrafast (400-600 Hz) oscillations in the somatosensory cortex, in response to peripheral stimulation, were previously observed in human and a handful of animal studies;" should be revised to indicate that most of the literature on this point were obtained in fact with responses to electrical shocks, which are not a proxy for generic  peripheral sensory stimulation, but rather an artificial means of activating sensory pathways.Reviewer #3 (Recommendations for the authors):The authors adequately answered my queries concerning the mechanism of generating ultrafast network oscillation via the interacting elements of layer 4 neurons following the activation of their thalamic inputs. Now the comparison with earlier hippocampal data is fair, detailed, and concise. I have no more comments. Essential revisions:Based on the attached reviewers' comments, all the reviewers agree that this manuscript is of potential interest to the audience studying the mechanisms of high-frequency cortical oscillations and their role in sensory information transfer. All three reviewers also agree that the proposed method provides a successful ex vivo model to study the ultrafast oscillation-mediated circuitry and cellular mechanisms and that the data reasonably support most of the claims by the authors in this manuscript.However, all three reviewers also raised concerns in common about what extent the data could be interpreted based on the present data.Firstly, they raised the concern that the experiments claiming the spread of phasic activity from Layer 4 RS to Layer 5 FS cells can not be accepted as entirely conclusive as it is at the current state. In more detail, the horizontal cut severed the L4 RS to L5 FS connections and many TC inputs to the L5 FS apical dendrites and the axons of L4 FS cells to L5 FS cells which can be pivotal in the translaminar spread. The observation that L5 FS spike bursts decrease after cutting the L4/L5 boundary could be interpreted in other ways. The current interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities that are phase-locked with the ripplets observed in L4. As it is outside the main scope of the paper, it could be toned down or dropped. Alternatively, this part should be redone with appropriate control experiments with the angled slice-cutting methods and the experiments suggested by the reviewers.We agree with the editors and with all three reviewers that the experiments described in the original Figure 6 are not conclusive, as they do not test directly ripplet mechanisms in L4, and as they are open to alternative interpretations. We especially took notice of the comment by Reviewer #3 that the L4/L5 cut necessarily truncated the apical dendrites of L5 pyramidal cells, which could have removed some of their thalamocortical inputs and which could have also rendered these cells less responsive to local excitation. We note that this interpretation is still fully consistent with our model, which posits that firing of local excitatory neurons is necessary for pacing FS interneurons at ripplet frequency. Nevertheless, we felt that more work is required to arrive at an unequivocal interpretation of these experiments, and removed the slice cutting experiment from the current manuscript. We still report the observation that L5 FS interneurons fire light-induced bursts at 400 Hz, and provide examples in Figure 2 —figure supplement 2.The second common concern is the interpretation of nearby FS synchrony induced by TC activation. The authors described the phase-locked responses of FS and RS cells with local ripple oscillations to optogenetic activation and highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4. This issue may be addressed by rephrasing and stating that Opto activation of TC creates highly synchronized inputs to the L4, which might further help the ultrafast synchrony.Throughout the manuscript, the authors understated the GABAergic mechanism by FS cells, whereas the highly  synchronous stimulation of many TC axons and terminals would activate FS cells which are GABAergic neurons.This recommendation appear to be based on comments by Reviewer #1, who was concerned about the flow of logic in the manuscript, with the FS-FS synchrony seemingly disconnected from the other findings. To address this concern we switched the order of the original Figure 3 and Figure 4, so the FS-FS synchrony is now described immediately after the description of FS spike bursts in Figure 2. The logic of examining FS-FS synchrony is also better explained now.Thirdly, as the authors proposed a circuit model, it would be ideal that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular spiking neurons to support their circuit model. However, if it takes too much effort to test an in silico model, it can be considered as an option for the authors.We agree that a computational model of ripplets would be very informative – not only for deciding between competing mechanisms, but also for elucidating the parameter space of layer 4 circuitry which would allow ripplets to emerge. We are indeed planning to build a new, or adapt an existing model to simulate ripplets in silico, which we prefer to defer to a future study.There are more major concerns raised by the reviewers below, which should be addressed appropriately before we can consider publication in eLife.Should further data analysis, figures revision, and text rationale allow you to address these criticisms, we would be happy to review a revised manuscript.In the meantime, we hope you will find our referees' comments helpful. Please do not hesitate to contact us if there is anything you would like to discuss.Reviewer #1 (Recommendations for the authors):1) The authors should consider the logical flow when presenting the synchrony of the FS-FS spike burst. It would strongly support the main idea of the manuscript if the authors rephrase or/and rearrange the corresponding text and data.We rearranged the text/figures by switching the order of Figures 3 and 4, which we hope addresses the reviewer’s concern about logical flow.2) Please provide detailed information on the viral vector of JAWS used for the experiment of suppressing L4 RS cell firing in the methods section, and also present expression image and spike data in figures to support the idea for the subsequent experiment performed in L5.As Figure 6 has been removed, there is no longer a need to describe the JAWS experiments.Reviewer #2 (Recommendations for the authors):1) While the authors are generally cautious in their interpretation of the optogenetic approach and how realistic it might be, we think that this issue deserves more direct discussion. In some ways, the high degree of nearby FS synchrony induced by TC optical activation is not at all surprising given what is already known about the potency of TC inputs onto FS cells (the senior author's own work and that of many other labs), and the highly  synchronous stimulation of many TC axons and terminals onto FS cells. The rare literature examples of high-frequency sensory evoked responses that the authors cite are arguably not simple sensory activity at all , or a tap to the finger or palm surface in awake monkeys , all of which are naturally occurring stimuli and are also widely used stimuli in sensory neuroscience. We made some additional textual changes in the revision to emphasize this and also to explain why ripplet-like oscillations have not been more widely reported in previous studies.2) The results regarding the propagation of high-frequency events to layer 5 are arguably outside the main scope of the paper, and could potentially be dropped. As is, many controls that have not been provided would need to be added. For example, demonstrating a complete lack of optogenetic effects in layer 4 with Jaws to rule out that there might actually be low levels of expression that could contribute to the results. In addition, ruling out the interaction between the blue and red light stimuli in the two pathways is required, as the spectral overlap for excitation spectrum is not as clean as one would need to unambiguously interpret these results.We agree with the sentiment of all three reviewers that the experiments shown in Figure 6 on FS bursts in layer 5 were both outside the main line of experiments and also were not conclusive, so this figure and the related text have been removed. In response to this reviewer’s specific comments, we note that the results of the optogenetic suppression experiments with JAWS were not relied upon for any of our conclusions.Reviewer #3 (Recommendations for the authors):1) In order to prove the critical role of FS-FS interactions in timing the oscillation the authors may consider GABA-A receptor blockers and/or optogenetic inhibition of PV cells.against a critical role for FS-FS interactions in timing the oscillation . We do however argue that FS-RS connections are essential. By either model, blocking GABAergic inhibition would disrupt the oscillation, and therefore blocking GABA receptors would not help distinguish between these two competing models. Nevertheless, to demonstrate the critical need for inhibition, we added a supplementary figure  showing the effect of GABAA blockers on the oscillations.We find the premise of this comment puzzling, since our study argues 2) To gain further insights into the excitatory background of the oscillation I would propose performing RS-RS pair recordings. In this configuration, the kinetics and strength of their interactions could be measured to establish whether this is fast and strong enough to provide a drive for the oscillation. RS data similar to FS cells shown in Figure 2 (variable stimulation intensity vs jitter) could also help to elucidate the mechanisms. Using voltage-clamp experiments in RS cells could help to resolve the origin of the excitatory drive of the oscillation (TC or RS-RS interactions). Optimally experimental work would be coupled with in silico simulations to test which network activity may underly the oscillation.nd paragraph of the revised Discussion. We did follow the reviewer’s recommendation and added example RS-RS recordings , to demonstrate the synchronous occurrence of E’s and I’s in neighboring RS cells.The kinetics, strength and prevalence of RS-RS synaptic connections in layer 4 of barrel cortex are known from multiple previous studies, and these are now cited more broadly in the 23) In order to make predictions for future research concerning the role of this ultrafast oscillation it would be advisable to test its postsynaptic effects on targets that do not receive direct thalamic inputs (i.e. upper layer 2/3 cells). This would not only allow more insights into the generation of the oscillation but could test whether the oscillation is indeed able to select a subset of L2/3 cells as suggested. For a translaminar spread to layer 3 measuring the strength and variability of the oscillation in layer 2/3 pyramidal cells, which are not directly contacted by thalamic inputs, would greatly enhance the predictive power of the paper for future research.Recordings from L2/3 during ripplets would indeed be very interesting and would provide important clues as to the physiological role of ripplets; however such experiments would not contribute to explaining the synaptic circuitry underlying ripplets, which is the focus of the current study, so we prefer to defer these experiments to a future study.We are pleased to see that the authors have addressed most of the comments made by the reviewers. The manuscript has been improved, and the unnecessary distracting parts have been removed, resulting in a smoother flow of data. However, we would like to draw your attention to a few critical comments that still need to be addressed.Firstly, the authors should include a discussion on how the strong optogenetic approaches used in this paper might or might not be relevant to the actual sensory processing in the brain circuit, given that the current data is obtained from brain slices and artificially synchronous wide-field optogenetic activation. We believe that this is an essential point to address, and we encourage you to include an appropriate discussion in the manuscript.Secondly, the authors should discuss in more detail the potential functional consequences and function of ultrafast brain oscillation in vivo, given that this manuscript focuses on the cellular nature of high-frequency oscillation in vitro. This is an excellent suggestion, and we believe that including a discussion on this topic will enhance the impact of your manuscript.We appreciate the valuable contribution that your manuscript has made, and we believe that addressing these comments on functional significance will further improve the quality and impact of your work. We have appended the full comments from the reviewers below for your reference.In response to the latest round of review, we have made substantial textual revisions throughout the manuscript. The most substantial revisions are in response to Reviewer #2’s concern that we were confounding our optogenetic stimulation with naturalistic, physiological sensory stimuli. To alleviate this concern, we now specify, in the section of the Discussion titled “Previous observations of ripplet-like oscillations in the neocortex”, exactly what type of stimuli were used by these previous studies, making a clear distinction between peripheral nerve stimulation  and punctate tactile and auditory stimuli . We make a similar distinction in the relevant places in the Abstract and the Introduction.As we state throughout the manuscript, ripplets are generated by strong synchronous activation of thalamocortical afferents. How and under what conditions is such synchrony achieved? Clearly, our optogenetic stimulation elicited the required synchrony. The reviewer seems to suggest that such synchrony will rarely, if ever, be achieved in-vivo by naturalistic stimuli, even though ripplets were elicited in the Barth studies by whisker deflections and by auditory clicks, physiological sensory stimuli which, we submit, are regularly encountered in the daily lives of rodents. We therefore take a more prudent stance and suggest in the revised document (line 430) that “Precisely what degree of thalamocortical synchrony is required for ripplet generation, and what type and intensity of sensory stimuli elicit such synchrony in vivo, is yet to be determined.”I believe that these revisions adequately address the first point made in the editorial decision letter. The editors then add a second point, suggesting that we “discuss in more detail the potential functional consequences and function of ultrafast brain oscillation in vivo”. The potential function of ripplets was already discussed in detail in the previous submission, in the last section of the Discussion, under “Ideas and speculations: the functional significance of ripplets”. We now made additional textual revisions to this section, which I believe better clarify these ideas and suggest how spike sequences during ripplets can encode information about the sensory event that triggered them. I hope this adequately addresses this request.