Stable representation of a naturalistic movie emerges from episodic activity with gain variability

Stable representation of a naturalistic movie emerges from episodic activity with gain variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Stable representation of a naturalistic movie emerges from episodic activity with gain variability Ji Xia, Tyler Marks, Michael Goard, Ralf Wessel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-126977/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Aug, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Visual cortical responses are known to be highly variable across trials within an experimental session. However, the long-term stability of visual cortical responses is poorly understood. Chronic imaging experiments in V1 showed that neural responses to repeated natural movie clips were unstable across weeks. Single neuronal responses consisted of sparse episodic activity which were stable in time but unstable in spike rates across weeks. Further, we found that the individual episode, instead of neuron, served as the basic unit of the week-to-week fluctuation. To investigate how population activity encodes the stimulus, we extracted a stable one-dimensional representation of the time in the natural movie, using an unsupervised method. Moreover, most week-to-week fluctuation was perpendicular to the stimulus encoding direction, thus leaving the stimulus representation largely unaffected. We propose that precise episodic activity with coordinated gain changes are keys to maintain a stable stimulus representation in V1. Cellular & Molecular Neuroscience visual cortical responses neuronal responses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 27 Aug, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-126977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":7601745,"identity":"578b649c-b506-4f6f-a189-e99af6f7a654","order_by":0,"name":"Ji Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZgYGg4QKMKuBgYGNaC1ngAw2RmK1gABjGylaDI4zPyh4OO9w4vz5jQ0MH8oOE9Yi2cxmYJC47XDihmOMDYwzzhGhhZ+ZAaTlduIGoMOYeduI0MLGzP7BIHHO7cT5bUAtf4nRws/MA7Sl4XZiA9BhzIzEaJFs5ikwSDj233jDscSGgz3n0glrMTh/fJvhj5o02fnNhw8++FFmTVgLELAZwFgHiFIPBMwPiFU5CkbBKBgFIxQAAP/XO8noG6FWAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1349-9114","institution":"Washington University in St. Louis","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Xia","suffix":""},{"id":7601746,"identity":"55242a8c-e711-4f74-8c7c-8a86f1a2696b","order_by":1,"name":"Tyler Marks","email":"","orcid":"","institution":"University of California, Santa Barbara","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tyler","middleName":"","lastName":"Marks","suffix":""},{"id":7601747,"identity":"fae70155-580b-4d18-87a3-e4d801bd3bf4","order_by":2,"name":"Michael Goard","email":"","orcid":"https://orcid.org/0000-0002-5366-8501","institution":"University of California, Santa Barbara","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Goard","suffix":""},{"id":7601748,"identity":"3484affc-9cfc-4c16-817e-531020c45ba6","order_by":3,"name":"Ralf Wessel","email":"","orcid":"","institution":"Washington University in St. Louis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Wessel","suffix":""}],"badges":[],"createdAt":"2020-12-11 21:46:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-126977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-126977/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-021-25437-2","type":"published","date":"2021-08-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":4786353,"identity":"2ad615d9-960c-4726-ac0d-1734fde49cf2","added_by":"auto","created_at":"2021-01-07 18:56:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":421006,"visible":true,"origin":"","legend":"Single neuron responses to natural movies are unstable across weeks.a. Experimental setup. We performed chronic calcium imaging of excitatory neurons in the primary visual cortex of awake, head-fixed mice during visual stimulation with repeated natural movies. Visual cortex (contralateral to visual stimulus delivery) is retinotopically mapped in Emx1-Cre::TITL-GCaMP6s mice. V1 fields are chosen from the region selective for the center of the presentation screen. Widefield scale bar = 1 mm; 2-photon scale bar = 100 μm. Average activity of four example well-tracked neurons across weeks are shown in the bottom panel. b. 𝛥𝛥𝛥𝛥/𝛥𝛥 responses of one example neuron during the same natural movie clip for 30 trials per experimental session for 6 weeks (movie starts at 5 sec and lasts for 30 sec duration). We recorded 1 experimental session per week. c. Similarity (correlation coefficient between trial-averaged 𝛥𝛥𝛥𝛥/𝛥𝛥) averaged over neurons during week 1 and that during other weeks are plotted for all the recorded imaging fields. Different imaging fields are denoted by different colors. The black curve with error bar denotes mean and standard deviation of similarity over imaging fields. Only a subset of imaging fields have recordings on week 6 (6 fields) and week 7 (5 fields). Specifically, the similarities of the fifth week were significantly lower than the similarities of the second week (Mann-Whitney U test, p \u003c 0.01).","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/82a1b91bf3364c52d5a69296.jpg"},{"id":4786571,"identity":"6a0126ff-a82d-4b32-9fc4-0ead95e112ea","added_by":"auto","created_at":"2021-01-07 18:59:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":794068,"visible":true,"origin":"","legend":"Single neuron responses consist of episodic activity with distinct episode-specific rate variations across weeks.\n\na. Top: inferred spikes of the same neuron shown in Fig. 1b. Bottom: peristimulus time histogram (PSTH) (black) and smoothed PSTH (blue) of the same neuron. Shaded areas (yellow) denote spiking episodes for this neuron. b. Top: spiking episodes for all the neurons in the example imaging field. Neurons are ordered by latency of their spiking episodes with the highest spiking rates. Bottom: number of neurons with overlapped spiking episodes.c. Top: Distributions of durations of spiking episodes from all imaging fields. Different colors denote different imaging fields. Bottom: distribution of durations of spiking episodes defined from PSTH of trials across weeks (yellow) plotted against distribution of durations from PSTH of trials within weeks (orange). d. Top: averaged spike rates over trials of all the spiking episodes in one example imaging field are plotted for different weeks and for even and odd trials in week 1. spiking episodes are ordered by their averaged spike rates during week 1. Bottom left: correlation coefficients (CC) between averaged spike rates of week pairs (dots) and even/odd trials within the week (lines) are shown for the example imaging field across weeks. Bottom right: CC within week averaged across weeks is plotted against CC across weeks averaged across all the week pairs (for all imaging fields, Mann-Whitney U test, p \u003c 0.005). Different colors denote different imaging fields. Colormap maximum value is set to 4 Hz. e. Mean spike rate during each spiking episode in the example neuron varies across weeks. f. Histogram of mean CC between mean spike rates during spiking episodes within the same neuron. Different colors denote different imaging fields. The black solid line is a gaussian curve fitted to the distribution of mean CC from all the imaging fields (mean 0.13, s.t.d. 0.30). The black dash dotted line is a gaussian curve fitted to the distribution of mean CC between simulated independent and identically distributed Poisson spike trains with the firing rates of a randomly selected spiking episode for a given neuron (mean 0.46, s.t.d. 0.31). The black dashed line indicates the chance level, which is a gaussian curve fitted to the distribution of mean CC between spiking episodes with independently shuffled weeks (mean 0.0036, s.t.d. 0.23). Only neurons with more than one spiking episode were included in this analysis.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/3bb2e178271fc6f9d424ab18.jpg"},{"id":4786573,"identity":"e194a1ec-38d8-481e-81dc-ae1a96e5552f","added_by":"auto","created_at":"2021-01-07 18:59:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":818887,"visible":true,"origin":"","legend":"Latent factors resembling episodic activity with gain changes capture the across-week fluctuations a. Schematic of Tensor Component Analysis (TCA). Neural activity (𝛥𝛥𝛥𝛥/𝛥𝛥) is organized into a third-order tensor with dimensions N x T x K. TCA approximates the data as a sum of outer products of three vectors from R components: neuron factors describe the weights of each neuron to that component, temporal factors describe the temporal dynamics of each component, and trial factors describe the modulation of the component across trials.b. Normalized 𝛥𝛥𝛥𝛥/𝛥𝛥 responses and reconstructed 𝛥𝛥𝛥𝛥/𝛥𝛥from 40 TCA components of two example neurons from the example imaging field. Reliability was defined as averaged correlation-coefficient between pairs of single-trial responses 3.c. Neuron, temporal, and trial factors of nonnegative TCA with 40 components for the example imaging field. Colormap maximum values are set to 2 for neuron factors and trial factors. We ordered components according to the K-means clustering on their trial factors. Within each thus determined cluster, we further ordered the components by the time to peak in their temporal factors. We ordered neurons in the neuron factors by their dominant components.d. Correlation coefficient (CC) between trial factors shown in c. e. CC between trial factors averaged across trial pairs within week plotted against CC between trial factors averaged across trial pairs across weeks. Different color denotes different imaging fields. The week-to-week variability of trial factors was significantly larger than the corresponding trial-to-trial variability within each week (for all imaging fields, Mann-Whitney U test, p \u003c 0.0001).","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/adc26b388793bf92397f3efa.jpg"},{"id":4786357,"identity":"2a89e9f5-d161-4ec9-8917-de1a8ab3b7a0","added_by":"auto","created_at":"2021-01-07 18:56:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327687,"visible":true,"origin":"","legend":"Stable manifolds exist in unstable population activity.a. 3-dimensional neural trajectories extracted from reconstructed (denoised) 𝛥𝛥𝛥𝛥/𝛥𝛥 populational activity across weeks from the example imaging field using Isomap. Each dot represents instantaneous population activity. Color of the dot indicates the corresponding time in the trial.b. Neural trajectories along the first 3 Isomap dimensions (the same as shown in a) organized in trial by time matrices.c. Correlation coefficients (CC) between transformed reconstructed 𝛥𝛥𝛥𝛥/𝛥𝛥 (neural trajectories) across trials along each Isomap dimension are plotted for all 10 imaging fields. 7Different color denotes different imaging fields.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/820784e233d09d0046ff1519.jpg"},{"id":4786354,"identity":"584e97ec-2fcf-4c90-9f8b-a41753118bcf","added_by":"auto","created_at":"2021-01-07 18:56:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":632321,"visible":true,"origin":"","legend":"The manifold mediates a stable representation of the time within the movie clip. a. Illustration of the unsupervised method with data from the example imaging field (n = 140): first, we projected reconstructed DF/F responses into the first two Isomap dimensions, each dot denotes instantaneous population activity; second, we randomly pick 80% of the instantaneous population activity as training set and rest of them as test set; third, we fitted a spline to the neural manifold of the training set and assigned coordinates with randomly picked origin to the fitted spline; finally, we shifted and flipped the coordinates on the fitted spline to match with the actual time and assigned decoded time to each point in the test set by its nearest coordinate on the spline.b. Decoded time from the neural manifold plotted against actual time in the movie for the example imaging field (n = 140).c. Violin plots showed decoding error (absolute circular difference between decoded time and actual time) for all the imaging fields. Imaging fields were ordered by the number of recorded neurons.","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/c3040a329a648d9f86f4d04a.jpg"},{"id":4786574,"identity":"0219f24f-514d-4528-a722-b5bda5409c88","added_by":"auto","created_at":"2021-01-07 18:59:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":801311,"visible":true,"origin":"","legend":"Both week-to-week fluctuation and trial-to-trial variation within the week is restricted to non-coding directions.a. TCA components of one imaging field (n = 166). We ordered components by the time to peak in their temporal factors. We ordered neurons in the neuron factors by their dominant components. Colormap maximum values are set to 2 for all the factors.b. Left: 2-dimensional neural manifold extracted from reconstructed (denoised) 𝛥𝛥𝛥𝛥/𝛥𝛥 population activity (n = 166) across weeks using Isomap. Each dot represents instantaneous population activity in the test set. Color of the dot (Same colormap as Fig. 4a) indicates the corresponding time in the trial. Black line is the fitted spline to the training set. Right: Zoom-in view on the neural manifold. Instantaneous population activity corresponding to 28 s in the trial was highlighted with black shade. Blue arrow denotes the direction perpendicular to the spline, and red arrow denotes the direction parallel to the spline. c. Histogram of the variance of population activity parallel or perpendicular to the spline for the imaging field shown in a\u0026b.d. Median variance of population activity parallel to the spline plotted against median variance of population activity perpendicular to the spline for all the imaging fields. Except for one imaging field (gray one), variance of population activity parallel to the spline was significantly smaller than the variance perpendicular to the spline (for all imaging fields, Mann-Whitney U test, p \u003c 0.0001). The one outlier (gray line) is from an imaging field with the least number of recorded neurons (n = 49), whose neural manifold didn’t have a clear ring shape (Supplemental Fig. 5a).e. The same zoom-in view on the neural manifold as shown in b (right). Each triangle represents instantaneous population activity within a week. Color of the triangle denotes different weeks. Each cross represents the trial-averaged instantaneous population activity within a week. Color of the cross also denotes different weeks. f. Left: histogram of the variance of trial-averaged population activity within a week parallel or perpendicular to the spline for the imaging field. Right: histogram of the variance of single-trial population activity within a week parallel or perpendicular to the spline for the imaging field.g. Median variance of trial-averaged population activity or single-trial population within a week parallel to the spline plotted against median variance of population activity perpendicular to the spline for all the imaging fields. Y axis is clippled at 9 for visualization. Except for one imaging field (gray one), variance of population activity parallel to the spline was significantly smaller than the variance perpendicular to the spline for both across weeks and within a week cases (for all imaging fields, Mann-Whitney U test, p \u003c 0.0001). The one outlier (gray line) is from an imaging field with the least number of recorded neurons (n = 49).","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/b76674c55b186d9162d7cfce.jpg"},{"id":4786572,"identity":"e183eaa3-77db-4dba-b389-50b13144c796","added_by":"auto","created_at":"2021-01-07 18:59:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":986193,"visible":true,"origin":"","legend":"The precisely timed episodic activity constrains neural variability to non-coding directions.a. Reconstructed 𝛥𝛥𝛥𝛥/𝛥𝛥 responses and shuffled reconstructed 𝛥𝛥𝛥𝛥/𝛥𝛥 responses of two example neurons from the imaging field (n = 166). The single neuron response was circularly shifted by a random amount independently for each trial for shuffling.b. Left: 2-dimensional neural manifold extracted from shuffled reconstructed 𝛥𝛥𝛥𝛥/𝛥𝛥 population activity (example single neuronal shuffled responses shown in a). The same colormap was used as in Fig. 6b, left panel. Right: neural trajectories along the first 2 Isomap dimensions (the same as shown in the left panel) organized in trial by time matrices. Here we set the number of nearest neighbors of ISOMAP to be 100 (see Methods).c. TCA components of the imaging field (n = 166) with shuffled factors. For each component,we independently shuffled neuron order in the neuron factor, circularly shifted the time factor and the trial factor by a random amount. Components with shuffled factors were ordered again in the same fashion as Fig.6a. d. 2-dimensional neural manifold extracted from reconstructed (denoised) 𝛥𝛥𝛥𝛥/𝛥𝛥 population activity (n = 166) from components with shuffled factors using Isomap. Each dot represents instantaneous population activity in the test set. Black line is the fitted spline to the training set. Instantaneous population activity corresponding to 16 s in the trial was highlighted with black shade.e. Histogram of the variance of neural variability parallel or perpendicular to the spline for reconstructed (denoised) 𝛥𝛥𝛥𝛥/𝛥𝛥 populational activity (n = 166) from components with shuffled factors (i.e., shuffled data with preserved trial structure). f. Median variance of neural variability parallel to the spline plotted against median variance of neural variability perpendicular to the spline for all the imaging fields for reconstructed activity from components with shuffled factors. Variance of population activity parallel to the spline was significantly smaller than the variance perpendicular to the spline (Mann- Whitney U test, p \u003c 0.0001) for all imaging fields.g. Radius (distance to the center of the point cloud) distribution of points on the neural manifold from original TCA components plotted against radius distribution from TCA components with shuffled factors for all the imaging fields. Except for 3 imaging fields (n = 49, n = 63, n = 88), the radius of points on the neural manifold from original TCA components was significantly larger than the radius from TCA components with shuffled factors (Mann-Whitney U test, p \u003c 0.0001).","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1/29bd9069bc231acd442b4f20.jpg"},{"id":15779539,"identity":"bf427841-9fba-49ca-a27b-bba9c6409d0f","added_by":"auto","created_at":"2021-11-22 15:37:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5973219,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptXIA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1_covered.pdf"},{"id":13571832,"identity":"ce5a1dbd-5e44-4b8e-85a7-0dc569ade720","added_by":"auto","created_at":"2021-09-17 03:49:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5968282,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptXIA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1_covered.pdf"},{"id":4786781,"identity":"04ce09f2-cde9-4a47-ad02-1b6e12368f8f","added_by":"auto","created_at":"2021-01-07 19:02:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11568985,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptXIA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-126977/v1_stamped.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Stable representation of a naturalistic movie emerges from episodic activity with gain variability","fulltext":[{"header":"Full Text","content":"\u003cp\u003eThis preprint is available for \u003ca href='/article/rs-126977/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"visual cortical responses, neuronal responses","lastPublishedDoi":"10.21203/rs.3.rs-126977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-126977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Visual cortical responses are known to be highly variable across trials within an experimental session. However, the long-term stability of visual cortical responses is poorly understood. Chronic imaging experiments in V1 showed that neural responses to repeated natural movie clips were unstable across weeks. Single neuronal responses consisted of sparse episodic activity which were stable in time but unstable in spike rates across weeks. Further, we found that the individual episode, instead of neuron, served as the basic unit of the week-to-week fluctuation. To investigate how population activity encodes the stimulus, we extracted a stable one-dimensional representation of the time in the natural movie, using an unsupervised method. Moreover, most week-to-week fluctuation was perpendicular to the stimulus encoding direction, thus leaving the stimulus representation largely unaffected. We propose that precise episodic activity with coordinated gain changes are keys to maintain a stable stimulus representation in V1.","manuscriptTitle":"Stable representation of a naturalistic movie emerges from episodic activity with gain variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-07 18:56:39","doi":"10.21203/rs.3.rs-126977/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"134fbef8-93ed-4857-861f-6454db2b4791","owner":[],"postedDate":"January 7th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":1775867,"name":"Cellular \u0026 Molecular Neuroscience"}],"tags":[],"updatedAt":"2021-11-22T15:34:28+00:00","versionOfRecord":{"articleIdentity":"rs-126977","link":"https://doi.org/10.1038/s41467-021-25437-2","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2021-08-27 04:00:00","publishedOnDateReadable":"August 27th, 2021"},"versionCreatedAt":"2021-01-07 18:56:39","video":"","vorDoi":"10.1038/s41467-021-25437-2","vorDoiUrl":"https://doi.org/10.1038/s41467-021-25437-2","workflowStages":[]},"version":"v1","identity":"rs-126977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-126977","identity":"rs-126977","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}