Spontaneous behaviors drive multidimensional, brainwide activity

Stringer, Pachitariu, Steinmetz, Reddy, Carandini, Harris · Science · 2019 · neuroscience · 10.1126/science.aav7893 · record Stringer2019-spontaneous-behaviors-drive-multidimensional
tag layer · index · concepts · authors · עברית
Where this record's text came from. faceted 2026-08 by reading the locally held text (xml); every evidence string verified as an exact substring of that text before merging. Two recording modalities were used: 11-plane two-photon calcium imaging of >10,000 V1 neurons (the primary dataset, Figs 1, 2, 4) and 8 simultaneous Neuropixels probes for the brain-wide result (Fig. 3). The method facet records the imaging because it carries the main claim; the electrophysiology component is not lost, it is stated here. The paper also introduces an analysis method (Shared Variance Component Analysis), but the headline claim is an existence claim about what spontaneous activity encodes, so claim type is existence rather than method. 2026-08-12, 2026-08-15 and 2026-08-25 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
3 quotations on this page contain a reference number where the paper printed a superscript. This journal style prints citations as raised numerals — parameters12,13 — and the extraction flattened them into the line, so the sentence reads parameters 12 , 13 . The digits are citation markers and not part of what the author wrote. The quotes keep them for the same reason they keep every other extraction fault: a quote is reproduced exactly or it proves nothing.
How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Other fields define these differently the same word, defined another way in another discipline
principal component analysis 5 fields · cross-validation 3 fields · standard deviation 3 fields · arousal 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 26 concept tags on this paper, none is grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
spontaneous activity ongoing activity cortical variability random noise recapitulation Shared Variance Component Analysis reliable variance Shared Variance Components arousal facial motion reduced rank regression stimulus-only subspace behavior-only subspace shared subspace multiplicative gain up and down phases power law principal component Neuropixels visual cortex thalamus forebrain locomotion pupil diameter whisking orientation tuning Behavioural and state variables reach primary sensory cortex through neuromodulatory input carrying arousal state and top-down input from higher-order cortices carrying fine-grained motor variables, so sensory and motor information are integrated already at V1 The apparent similarity between spontaneous and stimulus-evoked population activity arises because a single common mean-rate dimension fluctuates in both conditions, not because cortex replays past sensory experience two-photon GCaMP6s motion energy Shared Variance Component Analysis (SVCA) Shared Variance Components (SVCs) visual stimulation dimensions of the video signal population activity running speed time lag pupil area Wilcoxon signed-rank test two-sided Wilcoxon sign-rank test Wilcoxon rank-sum test correlation Spearman’s rank correlation Pearson correlation principal component analysis regression cross-validation cross-validated error rate linear classification linear classifier standard deviation mean ± s.d. cross-validated calcium-imaging
Method, as described: in-vivo-two-photon-calcium-imaging

Legend

second readingalternativesan independent re-reading of a facet, recorded whether or not it agrees
relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1126/science.aav7893. The reading view exists for the papers whose licence permits it.

Every tag on this page

alternatives 2 · claim_relations 8 · claim_type 1 · concept_edges 6 · content_tags 79 · contribution 1 · controls 2 · limitations 7 · mechanisms 13 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 7 · transfers 3

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 6 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studythe structure of spontaneous and stimulus-evoked population activity in mouse visual cortex, and what the apparently noisy component of it encodes
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object_of_studythe paper's words
research_questionis cortical variability random noise, recapitulation of prior sensory experience, or encoding of ongoing behaviour
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research_questionthe paper's words
contributionshows spontaneous activity reliably encodes a high-dimensional behavioural signal present brain-wide, and that sensory input adds to it in orthogonal dimensions rather than displacing it
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contributionthe paper's words
claim_typeexistence
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claim_typethe paper's words
phenomenonspontaneous cortical population activity and trial-to-trial response variability
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phenomenonthe paper's words
methodcalcium-imaging
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]the tagger's words
mechanismsmechanisms
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mechanisms[1]the tagger's words
mechanismsreliable variance is defined by double cross-validation: neurons split into two spatially separate sets, timepoints split into train and test, and only variance that covaries across both splits counts
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mechanisms[2]1.0the tagger's words
mechanismsthe behavioural regressor is not chosen in advance but discovered, by taking a thousand principal components of facial motion rather than naming which movements should matter
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mechanisms[3]1.0the tagger's words
mechanismsthe hand-picked arousal variables are shown to be redundant once the video is used, which is how the paper argues the old low-dimensional accounts were glimpses of something larger
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mechanisms[4]1.0the tagger's words
mechanismsthe paper states what its own method cannot see, and distinguishes its phenomenon from the fast one by a timescale its sampling rate excludes
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mechanisms[5]1.0the tagger's words
mechanismsthe claim of high dimensionality is stress-tested against sample size: reliability of higher components rises with neuron count, so the reported dimensionality is a floor
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mechanisms[6]1.0the tagger's words
mechanismsdecoding is used as the check on the subspace geometry - if stimulus information really is confined to one subspace, a classifier reading the others should fail
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mechanisms[7]1.0the tagger's words
mechanismsthe imaging result is repeated in a different preparation and a different recording modality, eight probes across forebrain, before the brain-wide claim is made
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mechanisms[8]1.0the tagger's words
mechanismsa spatial null is run on the sorted activity continuum: position on it does not track position in the tissue, so the structure is not topographic
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mechanisms[9]1.0the tagger's words
mechanismstrial-to-trial variability is decomposed into a multiplicative part inside the stimulus subspace and an additive part outside it, which is what lets a long-running either/or dispute be answered with both
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mechanisms[10]1.0the tagger's words
mechanismsthe multiplicative gain is not left as a fitted nuisance parameter but predicted from the behavioural video, tying the statistical model back to an observable
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mechanisms[11]1.0the tagger's words
mechanismsthe affine model is tested and rejected, so the absence of additive variability within the stimulus subspace is a checked result rather than an assumption
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mechanisms[12]1.0the tagger's words
limitationsThe dimensionality estimate is bounded by the number of neurons recorded; larger populations would reveal still more dimensions.
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what this limits: the dimensionality figure, which is bounded by the recording and not by the brain: THE ESTIMATED RELIABILITY OF HIGHER SVCs INCREASED WITH THE NUMBER OF NEURONS ANALYZED ... RECORDINGS OF LARGER POPULATIONS WOULD IDENTIFY YET MORE DIMENSIONS
limitations[0]the tagger's words
limitationsThe imaging frame rate (400 ms per cortical scan) makes fast up/down phases undetectable in this dataset.
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what this limits: any statement about up/down dynamics from this dataset: UP/DOWN PHASES COULD NOT EVEN HAVE BEEN DETECTED IN OUR RECORDINGS, WHICH SCANNED THE CORTEX EVERY 400 ms
limitations[1]the tagger's words
limitationsThe unexplained dimensions of reliable variance are not identified; they may be motor activity invisible on the face or internal cognitive variables.
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what this limits: the behavioural account of the variance: THE REMAINING DIMENSIONS AND VARIANCE MIGHT IN PART REFLECT MOTOR ACTIVITY NOT VISIBLE ON THE FACE ... OR THEY MIGHT REFLECT INTERNAL COGNITIVE VARIABLES SUCH AS MOTIVATIONAL DRIVES
limitations[2]the tagger's words
limitationsthe recording rate makes the fast up/down phenomenon undetectable in the imaging data
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duplicate of: limitations[1] · what this limits: the same scope as limitations[1] - the scan rate excludes the phenomenon rather than measuring it as absent
limitations[3]1.0the tagger's words
limitationsmost of the reliable variance is still unexplained, and the paper offers two quite different candidates without deciding
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what this limits: the same scope as limitations[2] - most of the reliable variance is unexplained, with two very different candidates offered and neither tested
limitations[4]1.0the tagger's words
limitationsthe dimensionality reported is bounded by the number of neurons recorded, not by the brain
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what this limits: the same scope as limitations[0] - more neurons would give more dimensions
limitations[5]1.0the tagger's words
limitationsthe recapitulation account is not excluded, only shown to be unsupported by the similarity statistic on its own
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what this limits: the recapitulation literature: OUR RESULTS DO NOT EXCLUDE THAT GENUINE RECAPITULATION COULD OCCUR IN OTHER BEHAVIORAL CIRCUMSTANCES, but EVEN A SINGLE DIMENSION OF COMMON RATE FLUCTUATION IS SUFFICIENT FOR SOME PREVIOUSLY-APPLIED STATISTICAL METHODS TO REPORT it
limitations[6]1.0the tagger's words
transfersa train-test split applied simultaneously along two axes - cells and time - to define which variance counts as signal rather than relying on a noise model
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transfers[0]0.95the tagger's words
transfersmotion energy and its principal components, used to turn a video of a face into a thousand-dimensional behavioural regressor without deciding in advance which movements matter
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transfers[1]0.95the tagger's words
transfersreduced rank regression, used to ask how few behavioural dimensions suffice rather than how much total variance behaviour explains
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transfers[2]0.9the tagger's words
controlscomparing recordings made in complete darkness against recordings made with a gray screen
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eliminates: that the spontaneous population activity is driven by visual input from the gray screen rather than being genuinely spontaneous · outcome: held
controls[0]0.9the tagger's words
controlscross-validated stimulus decoding from the stimulus subspace compared against the spontaneous-only and behaviour-only subspaces
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eliminates: that the spontaneous and behavioural dimensions carry the stimulus information, rather than being orthogonal to it · outcome: held
controls[1]0.85the tagger's words
sensescortical variability - as random noise, as recapitulation of prior experience, or as encoding of behaviour; three incompatible readings of one measured quantity, named together by the authors
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senses[0]1.0the tagger's words
sensesvariability - independent across cells (harmless to coding) versus correlated (harmful or not, depending on its geometry); the distinction that makes the orthogonality result matter
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senses[1]1.0the tagger's words
sensescorrelated variability - mimicking stimulus differences, and so corrupting the code, versus lying orthogonal to them, and so carrying something else
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senses[2]1.0the tagger's words
sensesslow multi-second alternation between two sub-populations versus fast up/down phases modulating all neurons together; the same raster read at two timescales
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senses[3]1.0the tagger's words
sensessimilarity between spontaneous and evoked activity - as evidence of replayed experience versus as a mean-rate artefact; the paper argues the statistic cannot distinguish them
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senses[4]1.0the tagger's words
sensesarousal as three hand-picked variables versus behaviour as a thousand videographic dimensions; the first turns out to be a projection of the second
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senses[5]1.0the tagger's words
sensesreliable variance - defined operationally as what survives a split across both cells and time, not as what exceeds a noise threshold
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senses[6]1.0the tagger's words
alternativesmethod: two-photon calcium imaging of over 10,000 neurons with simultaneous facial videography, cross-validated by a purpose-built shared-variance method, and replicated with eight simultaneous Neuropixels probes
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a second reading DIFFERING from the recorded value. Recorded: calcium-imaging — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.methodthe tagger's words
alternativesphenomenon: spontaneous cortical activity and trial-to-trial variability of sensory responses
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a second reading DIFFERING from the recorded value. Recorded: spontaneous cortical population activity and trial-to-trial response variability — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.phenomenonthe tagger's words
claim_relationsextends → Previous reports of low-dimensional locomotion and arousal correlates in visual cortex (refs 8, 10-16, 33)
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claim_relations[0]the tagger's words
claim_relationscontradicts → Studies reporting similarity between spontaneous activity and sensory responses as evidence for recapitulation of sensory experience (refs 2-7)
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claim_relations[1]the tagger's words
claim_relationsextends → Conflicting prior findings on whether cortical variability is additive or multiplicative (refs 39-42)
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claim_relations[2]the tagger's words
claim_relationscritiques → studies inferring recapitulation of sensory experience from statistical similarity between spontaneous and evoked activity
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claim_relations[3]1.0the tagger's words
claim_relationsextends → reports of low-dimensional locomotion and arousal correlates in visual cortex, which the paper recasts as glimpses of a much larger set
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claim_relations[4]1.0the tagger's words
claim_relationssupports → the resolution of the additive-versus-multiplicative dispute about cortical variability, by assigning each to a different subspace
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claim_relations[5]1.0the tagger's words
claim_relationscontradicts → the view that trial-to-trial variability in sensory cortex is primarily unstructured noise
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claim_relations[6]0.95the tagger's words
claim_relationsbuilds-on → the anatomical fact that primary sensory cortex receives both neuromodulatory state input and higher-order cortical input
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claim_relations[7]0.95the tagger's words
concept_edgesspontaneous activity —distinct-from→ recapitulation
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strength of the relation: 0.75 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the similarity 'need not imply recapitulation'; distinct-from is loose.
concept_edges[1]the tagger's words
concept_edgesreliable variance —operationalised-as→ Shared Variance Component Analysis
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strength of the relation: 1
concept_edges[3]the tagger's words
concept_edgesEVIDENCE CORRECTED (RE-GROUNDED) · stimulus-only subspace —distinct-from→ behavior-only subspace
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EVIDENCE CORRECTED: RE-GROUNDED. The old span named neither subspace. This clause names both and states their independence, which is the distinctness the edge records. — strength of the relation: 0.95
concept_edges[5]the tagger's words
concept_edgesfacial motion —causes→ multiplicative gain
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strength of the relation: 0.85 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the gain 'could be predicted from' facial motion energy; causes is loose.
concept_edges[7]the tagger's words
concept_edgesShared Variance Components —operationalised-as→ reliable variance
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strength of the relation: 0.9 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'the first SVC'; the endpoint is a variant, and operationalised-as is loose.
concept_edges[10]the tagger's words
concept_edgesthalamus —part-of→ forebrain
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strength of the relation: 0.85 Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span lists recorded regions; part-of 'forebrain' is not stated.
also in: thalamus · forebrain
concept_edges[11]the tagger's words
content_tagsWilcoxon signed-rank test
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content_tags.statistics[0]the tagger's words
content_tagsWilcoxon rank-sum test
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content_tags.statistics[1]the tagger's words
content_tagscorrelation
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also in: correlation
content_tags.statistics[2]the tagger's words
content_tagscorrelation
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also in: correlation
content_tags.statistics[3]the tagger's words
content_tagsprincipal component analysis
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content_tags.statistics[4]the tagger's words
content_tagsregression
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also in: regression
content_tags.statistics[5]the tagger's words
content_tagscross-validation
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content_tags.statistics[6]the tagger's words
content_tagslinear classification
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content_tags.statistics[7]the tagger's words
content_tagsstandard deviation
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content_tags.statistics[8]the tagger's words
content_tagsShared Variance Component Analysis
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content_tags.statistics[9]1.0the tagger's words
content_tagsreduced rank regression
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content_tags.statistics[10]1.0the tagger's words
content_tagspower law
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also in: power law
content_tags.statistics[11]1.0the tagger's words
content_tagstwo-sided Wilcoxon sign-rank test
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content_tags.statistics[12]0.9the tagger's words
content_tagsWilcoxon rank-sum test
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content_tags.statistics[13]1.0the tagger's words
content_tagscross-validated
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content_tags.statistics[14]1.0the tagger's words
content_tagsOngoing population activity in mouse visual cortex reliably encoded a latent signal of at least 100 linear dimensions.
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content_tags.findings[0]the tagger's words
content_tagsThe single largest dimension of ongoing activity tracks arousal and drives roughly half the neurons up while driving the other half down, so it is not a global rate increase.
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content_tags.findings[1]the tagger's words
content_tagsA high-dimensional description of facial movement predicts about twice as much neural variance as the three standard arousal measures (running, pupil diameter and whisking) combined.
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content_tags.findings[2]the tagger's words
content_tagsThe same multidimensional behaviour-related activity structure is not confined to visual cortex but is present in neurons sampled across the forebrain.
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content_tags.findings[3]the tagger's words
content_tagsThe stimulus and behaviour subspaces overlap primarily because both can change the population mean rate; the finer patterns of increase and decrease were essentially orthogonal.
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content_tags.findings[4]the tagger's words
content_tagsStimulus identity can be decoded accurately from the stimulus-only subspace but not at all from the behaviour-only or spontaneous-only subspaces, which perform no better than random dimensions.
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content_tags.findings[5]the tagger's words
content_tagsTrial-to-trial response variability decomposes into multiplicative gain modulation inside the stimulus subspace plus additive ongoing activity in dimensions orthogonal to it.
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content_tags.findings[6]the tagger's words
content_tagsThalamic neurons are the most strongly behaviour-modulated population recorded, with 35.7% of their total variance predictable from facial video.
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content_tags.findings[7]the tagger's words
content_tags97% of the variance in the first shared component was reliable, leaving 3% for independent noise
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content_tags.findings[8]1.0the tagger's words
content_tagsreliable spontaneous variance decayed across dimensions as a power law of exponent 1.14, steeper than for stimulus responses but still high-dimensional
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content_tags.findings[9]1.0the tagger's words
content_tagsthe three explicit arousal variables together explained about half the variance of the first neural principal component
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content_tags.findings[10]1.0the tagger's words
content_tagsabout 57% of neurons correlated positively with the first PC and the rest negatively, so two sub-populations alternate
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content_tags.findings[11]1.0the tagger's words
content_tagsmultidimensional facial behaviour predicted roughly twice the variance the three arousal variables did
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content_tags.findings[12]1.0the tagger's words
content_tagsprediction kept improving up to at least 16 behavioural dimensions, suggesting visual cortex encodes at least that many motor dimensions
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content_tags.findings[13]1.0the tagger's words
content_tagsthose dimensions captured 21% of total population variance against 11% for the three-variable model
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content_tags.findings[14]1.0the tagger's words
content_tagsadding running, pupil and whisking to the video improved prediction by less than 1%, even though running is not visible in the face video
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content_tags.findings[15]1.0the tagger's words
content_tagsevery recorded brain area contained neurons across the whole activity continuum, so the structure is distributed rather than localised
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content_tags.findings[16]1.0the tagger's words
content_tagsthalamic neurons were the best predicted by behaviour, at 35.7% of total variance
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content_tags.findings[17]1.0the tagger's words
content_tagsbehavioural prediction was best at zero lag and decayed over seconds, full width at half maximum 2.5 s
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content_tags.findings[18]1.0the tagger's words
content_tagsstimulus and behaviour subspaces overlapped in essentially one dimension, which is the population mean rate
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content_tags.findings[19]1.0the tagger's words
content_tagsa classifier decoded stimuli at 10% error from the stimulus subspace and at chance from the behaviour or spontaneous subspaces
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content_tags.findings[20]1.0the tagger's words
content_tagsthe behaviour subspace was active before the stimulus and continued unchanged through it, while the stimulus subspace was silent then active
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content_tags.findings[21]1.0the tagger's words
content_tagsa purely multiplicative trial-varying gain accounted for 89% of the variance in the stimulus subspace
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content_tags.findings[22]1.0the tagger's words
content_tagsthat gain was predictable from facial motion at r = 0.61 cross-validated
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content_tags.findings[23]1.0the tagger's words
content_tagsadding an additive term to the multiplicative model did not significantly improve it
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content_tags.findings[24]1.0the tagger's words
content_tagsstimulus and behaviour representations were mixed in the same neurons rather than carried by separate populations
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content_tags.findings[25]1.0the tagger's words
content_tagsrecordings in darkness and with a gray screen did not differ
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content_tags.findings[26]1.0the tagger's words
content_tagsposition on the sorted activity continuum bore little relation to position in the tissue
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content_tags.findings[27]1.0the tagger's words
content_tagsShared Variance Component Analysis (SVCA)
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content_tags.definitions[0]the tagger's words
content_tagsShared Variance Components (SVCs)
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content_tags.definitions[1]the tagger's words
content_tagsmotion energy
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also in: motion energy
content_tags.definitions[2]the tagger's words
content_tagsShared Variance Component Analysis
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content_tags.definitions[3]1.0the tagger's words
content_tagsup and down phases
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content_tags.definitions[4]1.0the tagger's words
content_tagsfacial motion
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also in: facial motion
content_tags.definitions[5]1.0the tagger's words
content_tagsvisual stimulation
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content_tags.variables[0]the tagger's words
content_tagsdimensions of the video signal
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content_tags.variables[1]the tagger's words
content_tagspopulation activity
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content_tags.variables[2]the tagger's words
content_tagsrunning speed
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also in: running speed
content_tags.variables[4]1.0the tagger's words
content_tagsfacial motion
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also in: facial motion
content_tags.variables[5]1.0the tagger's words
content_tagsreliable variance
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content_tags.variables[6]1.0the tagger's words
content_tagstime lag
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also in: time lag
content_tags.variables[7]1.0the tagger's words
content_tagsmultiplicative gain
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content_tags.variables[8]1.0the tagger's words
content_tagspupil area
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: pupil area
content_tags.variables[9]the tagger's words
content_tagswhisking
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
also in: whisking
content_tags.variables[10]the tagger's words
content_tags11,262 ± 2,282 — mean and s.d. of the number of excitatory neurons simultaneously imaged, over nine sessions in six mice
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content_tags.quantities[0]the tagger's words
content_tags97% — fraction of the variance along the first shared variance component that was reliable rather than independent noise
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content_tags.quantities[1]the tagger's words
content_tags1.14 — exponent of the power law by which reliable spontaneous variance decays across dimensions
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content_tags.quantities[2]the tagger's words
content_tags21%± 1% SE — share of total population variance captured by the 16 predictive facial-behaviour dimensions (31% of the reliable variance)
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content_tags.quantities[3]the tagger's words
content_tags10.1 ± 4.0 % — cross-validated error rate for decoding which of 32 natural images was shown, from the stimulus subspace
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content_tags.quantities[4]the tagger's words
content_tags89% ± 0.1% SE — variance in the stimulus subspace explained by a purely multiplicative trial-gain model
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content_tags.quantities[5]the tagger's words
content_tagsr = 0.61 ± 0.02 SE — cross-validated correlation between the per-trial multiplicative gain and its prediction from facial motion energy
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content_tags.quantities[6]the tagger's words
content_tagsless than 1% — extra explained variance gained by adding running, pupil and whisking to the facial video predictors
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content_tags.quantities[7]the tagger's words
content_tags11,262 ± 2,282 neurons — excitatory neurons imaged per session, over nine sessions in six mice
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content_tags.quantities[8]1.0the tagger's words
content_tags97% — reliable variance fraction of the first shared variance component
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
content_tags.quantities[9]1.0the tagger's words
content_tagsexponent 1.14 — power law decay of reliable spontaneous variance across dimensions
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content_tags.quantities[10]1.0the tagger's words
content_tags16 dimensions — behavioural dimensions at which neural prediction saturated
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content_tags.quantities[11]1.0the tagger's words
content_tags21% ± 1% SE against 11% ± 1% SE — total variance explained by videographic behaviour versus the three arousal variables
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content_tags.quantities[12]1.0the tagger's words
content_tags35.7% — thalamic variance predicted from behaviour, the highest of any region
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content_tags.quantities[13]1.0the tagger's words
content_tags2.5±0.4 s — full width at half maximum of the behavioural prediction across time lags
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content_tags.quantities[14]1.0the tagger's words
content_tags10.1 ± 4.0 % — stimulus decoding error from the stimulus subspace, against 53% and 57% from the others
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content_tags.quantities[15]1.0the tagger's words
content_tags89% ± 0.1% SE — variance in the stimulus subspace captured by a multiplicative gain model
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content_tags.quantities[16]1.0the tagger's words
content_tagsr = 0.61 ± 0.02 SE — cross-validated prediction of the trial gain from facial motion
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content_tags.quantities[17]1.0the tagger's words
content_tags2,296, 2,668 and 1,462 units — units held stable for about an hour in the three electrophysiology mice
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content_tags.quantities[18]1.0the tagger's words
content_tags57% ± 6.7% SE — share of neurons positively correlated with the first principal component
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content_tags.quantities[19]1.0the tagger's words

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