Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex

Khan, Poort, Chadwick, Blot, Sahani, Mrsic-Flogel, Hofer · Nature Neuroscience · 2018 · neuroscience · 10.1038/s41593-018-0143-z · record Khan2018-distinct-learning-induced-changes-in-stimulu
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. method does not fit the offered list: the recording technique is chronic two-photon calcium imaging of GCaMP6f in behaving mice (plus slice loose-patch validation and a linear dynamical system model), which is neither electrophysiology nor simulation alone; 'two-photon-calcium-imaging' was used rather than forcing an inaccurate term. The supplied text is an unedited manuscript version containing unresolved placeholders (e.g. 'R 2 = xx', 'P = XX', '(ref Makino,Komiyama)'), so some statistics could not be quoted. The paper states its PYR-selectivity increase reproduces Poort et al. 2015, but that concerns a background finding rather than the novel PV/SOM claim, so replication status is left untested. 2026-08-13 and 2026-08-14 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.
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Other fields define these differently the same word, defined another way in another discipline
learning 7 fields · model 4 fields · plasticity 4 fields · selectivity 3 fields · population 3 fields · bootstrap 2 fields · and 3 more →

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 30 concept tags on this paper, 6 are 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.
selectivity learning PYR PV SOM VIP interneuron cell class noise correlation interaction weight subnetwork plasticity gate heterogeneous immunostaining transgenic calcium neuropil optogenetic population stimulus grating recurrent external input model dynamics coupling inhibition cortex residual SOM interneurons gate pyramidal-cell plasticity: dendritic inhibition arriving together with bottom-up drive blocks the pairing of top-down and bottom-up inputs, so strongly SOM-coupled pyramidal cells cannot increase selectivity Concurrent SOM-mediated apical dendritic inhibition prevents the association of visual and top-down inputs in pyramidal cells Plasticity of excitatory connections onto PV cells makes them integrate local pyramidal input selectively, forming stimulus-specific PYR-PV subnetworks selectivity index (SI) noise correlations behavioral d-prime PSTH similarity score linear dynamical system (LDS) model selectivity index running speed eye position, pupil size, eye movements, running and licking sign test sign test with Bonferroni correction Wilcoxon rank-sum test Wilcoxon signed-rank test correlation Pearson correlation coefficient t-test Student's t-distribution bootstrap confidence interval classifier / decoding accuracy random forest decoder regression robust regression coefficient of determination bootstrap test calcium-imaging
Method, as described: two-photon-calcium-imaging

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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
conceptconceptsan idea the paper works with, quoted where the paper uses 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
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transfertransfersa method or concept carried in from another field

The paper

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alternatives 3 · claim_relations 8 · claim_type 1 · concept_edges 9 · concepts 6 · content_tags 74 · contribution 1 · controls 2 · limitations 6 · mechanisms 7 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 16 · transfers 4

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 9 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studyhow learning changes the stimulus selectivity and the mutual interactions of pyramidal cells and three classes of GABAergic interneuron at once
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object_of_studythe paper's words
research_questionwhether an interneuron class responds and changes as one functional unit, and whether learning can make a specific class selective for the behaviorally relevant stimulus
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research_questionthe paper's words
contributionPV cells become as selective as pyramidal cells and less tied to the local network, PYR-PV subnetworks emerge, and SOM cells decouple and appear to gate how much any pyramidal cell can change
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contributionthe paper's words
claim_typemechanism
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claim_typethe paper's words
phenomenonlearning-induced stimulus selectivity in visual cortical interneurons
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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
mechanismsmechanisms
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mechanisms[2]the tagger's words
mechanismsPYR to PV interaction weights become specific for stimulus preference, so PV cells integrate selectively from the pyramidal cells that share their preference rather than from the neighbourhood at large
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mechanisms[3]1.0the tagger's words
mechanismsand the increased PV selectivity then feeds back - deleting PV to PYR weights reduces pyramidal selectivity more after learning than before
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mechanisms[4]1.0the tagger's words
mechanismsSOM cells inhibit apical dendrites, so a pyramidal cell whose activity is correlated with the SOM population receives dendritic inhibition timed with its bottom-up drive and cannot pair it with top-down input
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mechanisms[5]0.8the tagger's words
mechanismsQuotation withheld, and the activity of SOM cells became strongly decorrelated, so they seemed to become uncoupled from the local network
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mechanisms[6]1.0the tagger's words
limitationsThe proposed local versus long-range connectivity account of the PV and SOM changes is a hypothesis the study did not test directly.
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what this limits: the connectivity account of the PV and SOM changes: THESE HYPOTHESES COULD BE TESTED DIRECTLY BY ASSESSING THE LOCAL AND LONG-RANGE CONNECTIVITY ... IN NAIVE AND EXPERT MICE - named as future work
limitations[0]the tagger's words
limitationsThe study cannot say how much of the observed single-cell heterogeneity is explained by cell-type diversity within an interneuron class, or how it varies by brain state and region.
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what this limits: any reading of the single-cell variability: THE CHALLENGE FOR FUTURE STUDIES IS TO ESTABLISH THE EXTENT TO WHICH THE HETEROGENEOUS BEHAVIOR OF SINGLE CELLS IS CAPTURED BY THE DIVERSITY OF CELL TYPES WITHIN AN INTERNEURON CLASS
limitations[1]the tagger's words
limitationsthe two accounts of PV selectivity - external drive against selective local input - are separated by a prediction about co-fluctuations rather than by a manipulation
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what this limits: the mechanism behind the PV selectivity change: PV CELLS MIGHT BECOME MORE STRONGLY INFLUENCED BY EXTERNAL ... INPUTS ... ALTERNATIVELY, INDIVIDUAL PV CELLS MIGHT ACQUIRE MORE SELECTIVE INPUT FROM SUBSETS OF PYR CELLS - two accounts, no measurement between them
limitations[2]0.9the tagger's words
limitationsand the connectivity the model infers has not been measured; the direct test is named as future work
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duplicate of: limitations[0] · what this limits: the same scope as limitations[0] - the inferred connectivity has not been measured
limitations[3]1.0the tagger's words
limitationsthe interneuron class itself is not a reliable label - transgenic and immunohistochemical identification of SOM cells agree only 70% of the time
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what this limits: every result reported by interneuron class, at a rate that differs by class: transgenic and immunohistochemical identification agree PV 92.6%, SOM 70.1%, VIP 85.4%
limitations[4]1.0the tagger's words
limitationsand the staged copy is an author manuscript in which three reported statistics are still unfilled placeholders
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what this limits: the model-fit statistics as printed: the staged copy carries unfilled placeholders where three reported values should be
limitations[5]1.0the tagger's words
transfersthe linear dynamical system, used to turn simultaneous recordings into a weight matrix between identified cells
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transfers[0]1.0the tagger's words
transfersd-prime style normalisation - the selectivity index is a response difference divided by the pooled standard deviation
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transfers[1]0.9the tagger's words
transferspost-hoc staining registered to the in vivo imaging plane, so that functional records acquire a molecular label
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transfers[2]1.0the tagger's words
transfersloose-patch recording with simultaneous imaging, imported purely to calibrate the indicator against spikes
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transfers[3]1.0the tagger's words
controlsthe behavioural covariates - eye position, pupil size, eye movements, running and licking - were measured and tested against the learning effect on response selectivity
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eliminates: that the increase in response selectivity after learning is an artefact of the animal's eye position, arousal or movement rather than a change in the cortical representation · outcome (stated by the tagger, no quotation): held
controls[0]0.95the tagger's words
controlsthe same behavioural covariates tested a second time, against the learning effect on NOISE CORRELATIONS rather than on selectivity
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eliminates: that the change in noise correlations with learning is an artefact of eye position, arousal or movement · outcome (stated by the tagger, no quotation): held
controls[1]0.95the tagger's words
conceptsselectivity
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also in: selectivity
concepts[0]the paper's words
conceptslearning
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also in: learning
concepts[1]the paper's words
conceptsPV
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also in: PV
concepts[3]the paper's words
conceptsnoise correlation
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concepts[8]the paper's words
conceptsrecurrent
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also in: recurrent
concepts[22]the paper's words
conceptsmodel
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also in: model
concepts[24]the paper's words
sensesTHE CATEGORY IS ASSUMED TO BE A UNIT: each interneuron class has been suggested to act as a functionally AND THUS COMPUTATIONALLY HOMOGENEOUS unit - and whether learning changes them homogeneously is not clear
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senses[0]1.0the tagger's words
sensesAND IT IS NOT: responses within each class are HIGHLY HETEROGENEOUS, including increases and decreases in the same class to the same stimulus - broadly different between classes, and heterogeneous within each
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senses[1]1.0the tagger's words
sensesTHE CATEGORY DOES NOT EVEN BOUND THE CORRELATIONS: activity correlations ACROSS classes can be as high as those WITHIN a class, so functional ensembles SPAN multiple cell types
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senses[2]1.0the tagger's words
sensesAND THE METHOD BUILT ON THE CATEGORY IS DECLARED INAPPROPRIATE: optogenetic manipulation of ENTIRE CELL CLASSES may not be the right approach, and cellular-resolution targeting may be required instead
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senses[3]1.0the tagger's words
sensesTWO DEFINITIONS OF THE SAME CLASS, AGREEING 70% OF THE TIME: of transgenically labelled cells also confirmed by immunostaining, PV 92.6%, VIP 85.4% and SOM only 70.1% - with the note that this matters for studies that use mouse lines to categorise cells
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senses[4]1.0the tagger's words
sensesand measured the other way round the same labels agree above 90%, so the disagreement is DIRECTIONAL - which label is the definition decides the population
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senses[5]1.0the tagger's words
sensesA NUMBER THAT WAS NEVER PRINTED: the manuscript reports the model's fit as R 2 = xx twice, with only the middle value filled in - the claim is made and the evidence for it is a placeholder
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senses[6]1.0the tagger's words
sensesand again in the constrained fit - the effect is said to be stronger, with P = XX
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senses[7]1.0the tagger's words
sensesTHE MODEL IS BLIND TO CELL TYPE AND THE CLASSES FALL OUT ANYWAY: interaction weights and their changes over learning differ by cell class although the model never saw the labels
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senses[8]1.0the tagger's words
sensesTWO RIVAL ACCOUNTS STATED WITH THEIR OPPOSITE PREDICTIONS BEFORE THE MEASUREMENT: external drive would make co-fluctuations DECREASE, selective local input would not - and only the first is consistent with what was found
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senses[9]1.0the tagger's words
sensesAN INSTRUMENT ARTEFACT RULED OUT BY A SECOND INSTRUMENT: loose-patch spikes recorded with simultaneous imaging show a near-linear rate-to-fluorescence relation, and selectivity computed from inferred firing rate matches selectivity from fluorescence
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senses[10]1.0the tagger's words
senseswith behavior excluded too - eye position, pupil size, eye movements, running and licking cannot account for it, and the change is specific to the task-relevant stimulus and absent in the approach corridor
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senses[11]1.0the tagger's words
sensesTHE PREDICTOR IS MEASURED BEFORE THE THING IT PREDICTS: coupling to the SOM population in the NAIVE animal predicts how much a pyramidal cell will gain in selectivity, and coupling to PV, VIP or PYR populations does not
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senses[12]1.0the tagger's words
sensesand the proposed mechanism is a gate - high correlation with SOM cells PREVENTS learning-related plasticity, low correlation is permissive
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senses[13]1.0the tagger's words
sensesA DEBATE SETTLED BY THE FIT: the origins of noise correlations have been debated, and here they are largely accounted for by concurrent functional interactions with simple linear dynamics
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senses[14]1.0the tagger's words
sensesAND THE GAP IS NAMED: no study yet provides a model-based fit of concurrent activity in multiple identified cell classes that accounts for the influence of the local population on each cell
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senses[15]1.0the tagger's words
alternativesclaim_type: mechanism
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a second reading reaching the same value. Recorded: mechanism — note: a second reading reaching the same value from a different sentence. Not applied - a person decides.
alternatives.claim_typethe tagger's words
alternativesmethod: a linear dynamical system fit to single-trial activity of all simultaneously imaged cells, with interaction weights deleted or held fixed across learning to ask what each contributes
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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: learning-induced sharpening of cortical representations, decomposed into which cell class changes and which interactions carry the change
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a second reading DIFFERING from the recorded value. Recorded: learning-induced stimulus selectivity in visual cortical interneurons — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.phenomenonthe tagger's words
claim_relationsbuilds-on → Poort et al., 2015
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claim_relations[0]the tagger's words
claim_relationsextends → Kerlin et al., 2010; Bock et al., 2011; Hofer et al., 2011; Scholl et al., 2015 (view that PV cells broadly integrate local pyramidal activity)
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claim_relations[1]the tagger's words
claim_relationscontradicts → the treatment of a molecularly defined interneuron class as a functionally homogeneous unit
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claim_relations[2]1.0the tagger's words
claim_relationscontradicts → the view that PV cells necessarily pool broadly and therefore stay untuned
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claim_relations[3]1.0the tagger's words
claim_relationssupports → the account that noise correlations arise from concurrent functional interactions rather than from shared external noise
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claim_relations[4]1.0the tagger's words
claim_relationscritiques → optogenetic manipulation of whole cell classes as a way to study circuit mechanisms
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claim_relations[5]1.0the tagger's words
claim_relationsdistinguishes-from → the alternative that PV cells simply acquire selective local input without decoupling, which predicts no drop in co-fluctuations
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differs in: THE AXIS IS A PREDICTION THAT SEPARATES TWO MECHANISMS, AND ONLY ONE MAKES IT. 'Quotation withheld.' Two accounts of the same learning effect - PV cells driven more by external input, or acquiring more selective local input - are indistinguishable in the selectivity data and separated by ONE measurable consequence. I declined to build this row earlier because I could not find the co-fluctuation clause in the span head; it is in the sentence that carries the marker.
claim_relations[6]1.0the tagger's words
claim_relationspredicts → that local and long-range connectivity of PYR, PV and SOM cells will differ between naive and expert mice
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claim_relations[7]1.0the tagger's words
concept_edgeslearning —causes→ selectivity
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holds when: in PYR, PV and SOM cells; the authors state VIP cells are the exception · direction on the measure: increases · strength of the relation: 1 · note: increased in subsets of PYR, PV and SOM cells BUT NOT VIP - the effect is per-class and per-subset, not general
also in: learning · selectivity
concept_edges[0]1.0the tagger's words
concept_edgesPYR —part-of→ subnetwork
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strength of the relation: 1 · note: PYR to PV interactions reorganized so that stimulus selective PYR-PV subnetworks EMERGED during learning Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says stimulus-selective PYR-PV subnetworks emerged; part-of is loose.
also in: PYR · subnetwork
concept_edges[3]1.0the tagger's words
concept_edgesSOM —contrasted-with→ VIP
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strength of the relation: 1 · note: the SOM mean does not move but a SUBPOPULATION does - the class average hides the effect
also in: SOM · VIP
concept_edges[19]1.0the tagger's words
concept_edgesPYR —causes→ PV
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strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but PV cells are 'THOUGHT TO broadly integrate' PYR activity, and their selectivity correlated with nearby PYR cells; causes is hedged.
also in: PYR · PV
concept_edges[24]1.0the tagger's words
concept_edgesexternal input —causes→ PV
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strength of the relation: 0.9 · note: TWO RIVAL ACCOUNTS WITH OPPOSITE PREDICTIONS STATED BEFORE THE MEASUREMENT: decoupling predicts co-fluctuations DECREASE, selective local input predicts they do not Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but PV cells 'MIGHT become' more influenced by external inputs - one of two hypotheses; causes is hedged.
also in: external input · PV
concept_edges[25]0.9the tagger's words
concept_edgeslearning —causes→ noise correlation
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holds when: in every cell-class pairing EXCEPT SOM-SOM, which the authors report as not significant · direction on the measure: reduces · strength of the relation: 1 · note: decreased within AND between classes, with the single exception of SOM-SOM pairs
concept_edges[26]1.0the tagger's words
concept_edgesinteraction weight —causes→ noise correlation
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strength of the relation: 1 · note: A NUMBER THAT WAS NEVER PRINTED - the fit is reported as R 2 = xx twice over, with only the middle value (-0.18) filled in; the claim is made and its evidence is a placeholder
concept_edges[31]1.0the tagger's words
concept_edgesPV —causes→ selectivity
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direction on the measure: increases · strength of the relation: 1 · note: and it feeds back - deleting PV to PYR weights costs PYR selectivity more after learning, where deleting SOM and PYR weights does not change Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says PV selectivity boosts PYR selectivity; the endpoint 'selectivity' drops whose.
also in: PV · selectivity
concept_edges[36]1.0the tagger's words
concept_edgesSOM —causes→ plasticity
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strength of the relation: 0.9 · note: high correlation with the SOM population PREVENTS learning-related plasticity, low correlation is permissive - dendritic inhibition arriving with the bottom-up drive blocks the pairing Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span suggests SOM cells gate plasticity; causes is loose and the claim is a suggestion.
also in: SOM · plasticity
concept_edges[39]0.9the tagger's words
content_tagssign test
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also in: sign test
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_tagsWilcoxon signed-rank test
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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_tagst-test
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also in: t-test
content_tags.statistics[4]the tagger's words
content_tagsbootstrap
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also in: bootstrap
content_tags.statistics[5]the tagger's words
content_tagsconfidence interval
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content_tags.statistics[6]the tagger's words
content_tagsclassifier / decoding accuracy
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content_tags.statistics[7]the tagger's words
content_tagsregression
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also in: regression
content_tags.statistics[8]the tagger's words
content_tagscoefficient of determination
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content_tags.statistics[9]the tagger's words
content_tagssign test
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also in: sign test
content_tags.statistics[10]1.0the tagger's words
content_tagsWilcoxon rank-sum test
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content_tags.statistics[11]1.0the tagger's words
content_tagsbootstrap test
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content_tags.statistics[12]1.0the tagger's words
content_tagsLearning increased stimulus selectivity in subsets of pyramidal, PV and SOM cells, but left VIP cells unchanged.
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content_tags.findings[0]the tagger's words
content_tagsPV neurons became as selective as PYR cells and less correlated with stimulus preference of the local network
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content_tags.findings[1]the tagger's words
content_tagsFitting a linear dynamical system to the population activity showed that pyramidal-to-PV interactions reorganized into stimulus-selective PYR-PV subnetworks over learning.
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content_tags.findings[2]the tagger's words
content_tagsAfter learning, model interaction weights from PYR to PV and between PV cells were stronger for cell pairs sharing a grating preference than for pairs with opposite preference.
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content_tags.findings[3]the tagger's words
content_tagsLearning reduced noise correlations both within and across cell classes, so network firing became more independent.
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content_tags.findings[4]the tagger's words
content_tagsHow strongly a pyramidal cell was correlated with the SOM population before learning predicted how much its selectivity would increase.
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content_tags.findings[5]the tagger's words
content_tagslearning increased selectivity in subsets of PYR, PV and SOM cells but not VIP, and PV neurons became AS SELECTIVE AS pyramidal cells while becoming less correlated with the local network
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content_tags.findings[6]1.0the tagger's words
content_tagsPYR to PV interactions reorganized so that stimulus-selective PYR-PV subnetworks emerged, while SOM cells decorrelated from the local network and may gate selectivity changes
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content_tags.findings[7]1.0the tagger's words
content_tagsTHE CATEGORY IS ASSUMED TO BE A UNIT: each interneuron class has been suggested to act as a functionally and thus computationally HOMOGENEOUS unit
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content_tags.findings[8]1.0the tagger's words
content_tagsAND IT IS NOT: responses within each class are highly heterogeneous, including increases and decreases to the same stimulus - different between classes, and heterogeneous within each
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content_tags.findings[9]1.0the tagger's words
content_tagsTHE CATEGORY DOES NOT BOUND THE CORRELATIONS EITHER: correlations across classes can be as high as those within a class, so functional ensembles SPAN cell types
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content_tags.findings[10]1.0the tagger's words
content_tagsAND THE METHOD BUILT ON THE CATEGORY IS DECLARED INAPPROPRIATE: optogenetic manipulation of entire cell classes may not be the right approach for studying circuit mechanisms
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content_tags.findings[11]1.0the tagger's words
content_tagsTWO DEFINITIONS OF THE SAME CLASS AGREEING 70% OF THE TIME: transgenically labelled cells confirmed by immunostaining were PV 92.6%, VIP 85.4% and SOM only 70.1%
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content_tags.findings[12]1.0the tagger's words
content_tagsand measured the other way round the same labels agree above 90%, so the disagreement is directional
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content_tags.findings[13]1.0the tagger's words
content_tagsA NUMBER THAT WAS NEVER PRINTED: the model fit is reported as R 2 = xx twice, with only the middle value filled in
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content_tags.findings[14]1.0the tagger's words
content_tagsand again in the constrained fit, where the effect is said to be stronger with P = XX
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content_tags.findings[15]1.0the tagger's words
content_tagsTHE MODEL IS BLIND TO CELL TYPE and cell-class specific differences in interaction weights appear anyway
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content_tags.findings[16]1.0the tagger's words
content_tagsTWO RIVAL ACCOUNTS WITH OPPOSITE PREDICTIONS STATED BEFORE THE MEASUREMENT: decoupling predicts co-fluctuations decrease, selective local input predicts they do not
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content_tags.findings[17]1.0the tagger's words
content_tagsPV selectivity rose although these cells were expected to pool broadly and inhibit in proportion to average local activity, and the slope on local PYR selectivity fell from 0.2 to 0.05
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content_tags.findings[18]1.0the tagger's words
content_tagsAN INSTRUMENT ARTEFACT RULED OUT BY A SECOND INSTRUMENT: loose-patch spikes with simultaneous imaging give a near-linear rate-to-fluorescence relation, and selectivity from inferred rate matches selectivity from fluorescence
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content_tags.findings[19]1.0the tagger's words
content_tagswith eye position, pupil size, eye movements, running and licking excluded, and the change absent in the approach corridor
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content_tags.findings[20]1.0the tagger's words
content_tagslearning decreased noise correlations within and between classes - SOM cells decorrelating most, PV cells least
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content_tags.findings[21]1.0the tagger's words
content_tagsthe LDS model splits each cell's activity into recurrent input from all other recorded cells, a trial-invariant stimulus-locked input, running speed, and a residual
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content_tags.findings[22]1.0the tagger's words
content_tagspost learning, PYR to PV and PV to PV weights were higher between cells preferring the SAME grating, and deleting weights cost PV selectivity only after learning
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content_tags.findings[23]1.0the tagger's words
content_tagsPV selectivity changes were disrupted only when stimulus drive AND PYR to PV weights were BOTH held fixed across learning
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content_tags.findings[24]1.0the tagger's words
content_tagsand it feeds back - deleting PV to PYR weights cost pyramidal selectivity more after learning, where SOM and PYR weight deletions did not change
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content_tags.findings[25]1.0the tagger's words
content_tagsTHE PREDICTOR IS MEASURED BEFORE THE THING IT PREDICTS: coupling to the SOM population in the naive animal predicts the selectivity gain, and coupling to PV, VIP or PYR populations does not
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content_tags.findings[26]1.0the tagger's words
content_tagswith the proposed mechanism a gate - high correlation with SOM cells prevents plasticity, low correlation permits it
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content_tags.findings[27]1.0the tagger's words
content_tagsthe authors suggest noise correlations can largely be accounted for by concurrent functional interactions with simple linear dynamics
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content_tags.findings[28]1.0the tagger's words
content_tagsAND THE GAP IS NAMED: no study yet fits concurrent activity in multiple identified cell classes while accounting for the local population's influence on each cell
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content_tags.findings[29]1.0the tagger's words
content_tagsthe direct test is named as future work - local and long-range connectivity of PYR, PV and SOM cells in naive against expert mice
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content_tags.findings[30]1.0the tagger's words
content_tagsselectivity index (SI)
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content_tags.definitions[0]the tagger's words
content_tagsnoise correlations
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content_tags.definitions[1]the tagger's words
content_tagsbehavioral d-prime
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content_tags.definitions[2]the tagger's words
content_tagsPSTH similarity score
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content_tags.definitions[3]the tagger's words
content_tagslinear dynamical system (LDS) model
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content_tags.definitions[4]the tagger's words
content_tagsselectivity
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also in: selectivity
content_tags.definitions[5]1.0the tagger's words
content_tagsnoise correlation
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content_tags.definitions[6]1.0the tagger's words
content_tagsinteraction weight
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content_tags.definitions[7]1.0the tagger's words
content_tagsgate
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also in: gate
content_tags.definitions[8]0.9the tagger's words
content_tagscell class
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also in: cell class
content_tags.variables[2]the tagger's words
content_tagsselectivity index
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content_tags.variables[3]the tagger's words
content_tagsrunning speed
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also in: running speed
content_tags.variables[5]the tagger's words
content_tagseye position, pupil size, eye movements, running and licking
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content_tags.variables[6]the tagger's words
content_tagsselectivity
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also in: selectivity
content_tags.variables[7]1.0the tagger's words
content_tagsnoise correlation
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content_tags.variables[8]1.0the tagger's words
content_tagsinteraction weight
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content_tags.variables[9]1.0the tagger's words
content_tagscoupling
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also in: coupling
content_tags.variables[10]1.0the tagger's words
content_tags0.31 ± 0.32 to 0.41 ± 0.45, P < 10 -7 , N = 1249 — average absolute selectivity of PYR cells pre learning and post learning, with the sign test P and the N
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content_tags.quantities[0]the tagger's words
content_tags0.25 ± 0.20 to 0.43 ± 0.38, P = 0.002, N = 132 — Mean absolute selectivity of PV interneurons before versus after learning
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content_tags.quantities[1]the tagger's words
content_tags0.19 ± 0.17 to 0.19 ± 0.16, P = 1.00, N = 175 — VIP cell selectivity, unchanged across learning
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content_tags.quantities[2]the tagger's words
content_tags1249, 132, 58 and 175 — Numbers of PYR, PV, SOM and VIP cells imaged, across 8 mice
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content_tags.quantities[3]the tagger's words
content_tagsslope = 0.2, CI 0.14 to 0.26, R = 0.49 — Pre-learning relationship between a PV cell's selectivity and the mean selectivity of pyramidal cells within 100 µm
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content_tags.quantities[4]the tagger's words
content_tags0.013 from 0.077 — Average simulated noise correlation with all model interaction weights deleted, versus the observed value
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content_tags.quantities[5]the tagger's words
content_tags1.00, 0.99, 0.97 — Correlation between selectivity computed from calcium fluorescence and from inferred firing rate, for PV, SOM and VIP cells
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content_tags.quantities[6]the tagger's words
content_tags92.6% ... 70.1% ... 85.4% — agreement between the transgenic and the immunohistochemical definition of the same interneuron class - and for SOM cells nearly a third of the population depends on which definition is used
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content_tags.quantities[7]1.0the tagger's words
content_tagsabove 90% — the percentage of overlap between immunostaining and transgenic marker expression, measured in the opposite direction from the table of transgenically labelled cells
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content_tags.quantities[8]1.0the tagger's words
content_tagsR 2 = xx — a reported model fit that was never filled in, printed twice in one sentence with only the middle value supplied
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content_tags.quantities[9]1.0the tagger's words
content_tagsP = XX — a second unfilled placeholder, this time behind the claim that the effect was stronger
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content_tags.quantities[10]1.0the tagger's words
content_tags0.31 ± 0.32 ... 0.41 ± 0.45 — mean absolute selectivity of pyramidal cells before and after learning, with standard deviations as large as the means
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content_tags.quantities[11]1.0the tagger's words
content_tags0.7 — the neuropil subtraction factor, obtained by inspecting the regression slope in a SAMPLE of the dataset rather than fitted per cell
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content_tags.quantities[12]1.0the tagger's words

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