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

Khan, Poort, Chadwick, Blot, Sahani, Mrsic-Flogel, Hofer · 2018 · neuroscience · doi:10.1038/s41593-018-0143-z · full text at the source · TEI xml · tagged edition · index · concepts · authors
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.
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, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
selectivity8 learning17 PYR4 PV22 SOM12 VIP14 interneuron24 cell class noise correlation4 interaction weight subnetwork2 plasticity11 gate heterogeneous2 immunostaining transgenic2 calcium3 neuropil3 optogenetic16 population15 stimulus13 grating2 recurrent6 external input model22 dynamics coupling3 inhibition17 cortex9 residual3 calcium-imaging10
Method, as described: two-photon-calcium-imaging

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content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonlearning-induced stimulus selectivity in visual cortical interneurons— “Learning increases the selectivity of cortical representations for behaviorally relevant stimuli, but how this occurs through changes in activity of different cell types remains unclear.”
claim-typemechanism— “A linear dynamical system model revealed that PYR to PV interactions reorganized such that stimulus selective PYR-PV subnetworks emerged during learning.”
method — evidence— “We imaged simultaneously responses of pyramidal cells (PYR), and parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate two visual patterns.”
object of study — evidence— “Learning increased the selectivity for both stimuli in subsets of PYR, PV and SOM cells but not VIP cells. Strikingly, PV neurons became as selective as PYR cells and less correlated with stimulus preference of the local network.”
research question — evidence— “Moreover, each interneuron class has been suggested to act as a functionally (and thus computationally) homogeneous unit during sensory or behavioral events( Kato et al., 2015 ; Kvitsiani et al., 2013 ; Pi et al., 2013 ; Hangya et al., 2014 ; Pinto and Dan, 2015 ; Karnani et al., 2016 ), but it is not clear whether learning leads to…
contribution — evidence— “A linear dynamical system model revealed that PYR to PV interactions reorganized such that stimulus selective PYR-PV subnetworks emerged during learning. In contrast, SOM cells became decorrelated from the local network and may gate selectivity changes.”
transferfrom systems identification and state-space modelling: the linear dynamical system, used to turn simultaneous recordings into a weight matrix between identified cells— “To address these questions, we fit the data to a linear dynamical system (LDS) model which predicted the activity of each cell based on two main components: (1) a weighted contribution of inputs from all other simultaneously recorded cells on the previous time step (the recurrent input), and (2) a contribution from a trial-invariant…transfers[0]
transferfrom signal detection theory: d-prime style normalisation - the selectivity index is a response difference divided by the pooled standard deviation— “We quantified the selectivity of each cell as the selectivity index (SI) which was the difference between the mean response (0-1 s) to the vertical and angled grating divided by the pooled standard deviation.”transfers[1]
transferfrom immunohistochemistry: post-hoc staining registered to the in vivo imaging plane, so that functional records acquire a molecular label— “To measure the overlap of labelling by transgenic mouse lines and immunostaining, we performed immunostaining on PV, SOM, and VIP transgenic mice (2 mice each) expressing tdTomato in the respective interneuron class, and determined the percentage of overlap between immunostaining and transgenic marker expression, which in each case was above…transfers[2]
transferfrom slice electrophysiology: loose-patch recording with simultaneous imaging, imported purely to calibrate the indicator against spikes— “To test that the increased preference for task-relevant stimuli was not a result of nonlinearities in the relationship between a neuron’s firing rate and calcium concentration, we performed loose-patch recordings of spiking activity while simultaneously imaging GCaMP6 calcium fluorescence in from the three interneuron classes in visual cortex…transfers[3]
mechanismSOM 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— “We therefore suggest that SOM cells gate learning-related plasticity, by preventing PYR cells that are highly correlated with the SOM population from increasing their stimulus selectivity.”mechanisms[0]
mechanismConcurrent SOM-mediated apical dendritic inhibition prevents the association of visual and top-down inputs in pyramidal cells— “If PYR cells respond to a visual, bottom-up input that also correlated with activity in a subpopulation of SOM cells, they may receive concurrent SOM mediated inhibition of the apical dendrite which may prevent the association of visual and top down inputs.”mechanisms[1]
mechanismPlasticity of excitatory connections onto PV cells makes them integrate local pyramidal input selectively, forming stimulus-specific PYR-PV subnetworks— “Changes in PYR to PV interactions might lead to the emergence of stimulus selective PYR-PV subnetworks, implying that PV cells may integrate local excitatory inputs more specifically as a function of learned stimulus relevance.”mechanisms[2]
mechanismPYR 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— “Manipulating the interaction weights in the model allowed us to determine the extent to which the recurrent local network accounted for the stimulus selectivity of interneurons. We deleted all interaction weights and simulated the responses of the population (see Online Methods ). Pre learning, the selectivity of PV and SOM cells did not…mechanisms[3]
mechanismand the increased PV selectivity then feeds back - deleting PV to PYR weights reduces pyramidal selectivity more after learning than before— “Importantly, the increased post learning selectivity of PV cells further boosts the selectivity of PYR cells. Specific deletion of PV to PYR weights reduced PYR selectivity, and the effect was greater after learning ( Fig. 4f , P < 10 -3 ). In contrast, the effect of deleting SOM and PYR cell weights on PYR cells did not change with…mechanisms[4]
mechanismSOM 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— “We therefore suggest that SOM cells gate learning-related plasticity, by preventing PYR cells that are highly correlated with the SOM population from increasing their stimulus selectivity. Pairing of top-down and bottom-up inputs arriving in apical and basal dendrites respectively is important for forming new associations( Larkum, 2013 ), and…mechanisms[5]
mechanismlearning decreased noise correlations both within and between cell classes, and the activity of SOM cells became strongly decorrelated, so they seemed to become uncoupled from the local network— “We found that learning decreased noise correlations both within and between cell classes (sign test, all Ps < 10 -4 , with the exception of SOM-SOM cell pairs, P = 0.35), indicating that firing in the network became more independent ( Fig. 3c ). Notably, the activity of SOM cells became strongly decorrelated from that of PYR, PV and VIP…mechanisms[6]
builds-on Poort et al., 2015— “Experimental procedures for the behavioral task and two-photon calcium imaging have been described in detail in a previous study( Poort et al., 2015 ).”claim_relations[0]
extends 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)— “The increased selectivity of PV cells for the grating stimuli was unexpected given that these interneurons are thought to broadly integrate the activity of surrounding PYR cells( Kerlin et al., 2010 ; Bock et al., 2011 ; Hofer et al., 2011 ; Scholl et al., 2015 ), thus providing local inhibition proportional to the average local activity…claim_relations[1]
contradicts the treatment of a molecularly defined interneuron class as a functionally homogeneous unit— “Importantly, despite differences in average response profiles, the responses of individual neurons within each cell class were highly heterogeneous, including stimulus-evoked increases and decreases in activity ( Fig. 1e , Supplementary Fig. 2a, b ). Within-class response diversity was also apparent in responses triggered by the onset of…claim_relations[2]
contradicts the view that PV cells necessarily pool broadly and therefore stay untuned— “The increased selectivity of PV cells for the grating stimuli was unexpected given that these interneurons are thought to broadly integrate the activity of surrounding PYR cells( Kerlin et al., 2010 ; Bock et al., 2011 ; Hofer et al., 2011 ; Scholl et al., 2015 ), thus providing local inhibition proportional to the average local activity…claim_relations[3]
supports the account that noise correlations arise from concurrent functional interactions rather than from shared external noise— “First, the origins of noise correlations in a neural network have been debated. Here we suggest that noise correlations can to a large extent be accounted for by concurrent functional interactions with simple linear dynamics.”claim_relations[4]
critiques optogenetic manipulation of whole cell classes as a way to study circuit mechanisms— “The heterogeneous nature of experience-dependent response changes in different inhibitory neurons suggests that optogenetic manipulations of entire cell classes may not be the most appropriate approach for investigating the circuit mechanisms of cortical processing and plasticity, and may…claim_relations[5]
distinguishes-from the alternative that PV cells simply acquire selective local input without decoupling, which predicts no drop in co-fluctuationsclaim_relations[6]
predicts 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]
senseTHE 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]
senseAND 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]
senseTHE 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]
senseAND 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]
senseTWO 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]
senseand 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]
senseA 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]
senseand again in the constrained fit - the effect is said to be stronger, with P = XX
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senses[7]
senseTHE 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]
senseTWO 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]
senseAN 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]
sensewith 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]
senseTHE 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]
senseand 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]
senseA 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]
senseAND 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]
controlthe behavioural covariates - eye position, pupil size, eye movements, running and licking - were measured and tested against the learning effect on response selectivity — held — rules out 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
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controls[0]
controlthe same behavioural covariates tested a second time, against the learning effect on NOISE CORRELATIONS rather than on selectivity — held — rules out that the change in noise correlations with learning is an artefact of eye position, arousal or movement
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controls[1]
differs inthe alternative that PV cells simply acquire selective local input without decoupling, which predicts no drop in co-fluctuations — THE AXIS IS A PREDICTION THAT SEPARATES TWO MECHANISMS, AND ONLY ONE MAKES IT. 'The first, but not the second alternative would predict that co-fluctuations between PV neurons and the local network should decrease.' 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.
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Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[6]
effect directionlearning —causes→ selectivity — increases
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concept_edges[0]
holds whenlearning —causes→ selectivity — in PYR, PV and SOM cells; the authors state VIP cells are the exception
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concept_edges[0]
notelearning —causes→ selectivity — increased in subsets of PYR, PV and SOM cells BUT NOT VIP - the effect is per-class and per-subset, not general
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concept_edges[0]
auditPYR —part-of→ subnetwork — 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.
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concept_edges[3]
notePYR —part-of→ subnetwork — PYR to PV interactions reorganized so that stimulus selective PYR-PV subnetworks EMERGED during learning
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concept_edges[3]
noteSOM —contrasted-with→ VIP — the SOM mean does not move but a SUBPOPULATION does - the class average hides the effect
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concept_edges[19]
auditPYR —causes→ PV — 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.
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concept_edges[24]
auditexternal input —causes→ PV — 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.
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concept_edges[25]
noteexternal input —causes→ PV — TWO 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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concept_edges[25]
weightexternal input —causes→ PV — 0.9
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concept_edges[25]
effect directionlearning —causes→ noise correlation — reduces
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concept_edges[26]
holds whenlearning —causes→ noise correlation — in every cell-class pairing EXCEPT SOM-SOM, which the authors report as not significant
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concept_edges[26]
notelearning —causes→ noise correlation — decreased within AND between classes, with the single exception of SOM-SOM pairs
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concept_edges[26]
noteinteraction weight —causes→ noise correlation — 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
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concept_edges[31]
auditPV —causes→ selectivity — 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.
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concept_edges[36]
effect directionPV —causes→ selectivity — increases
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Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[36]
notePV —causes→ selectivity — and it feeds back - deleting PV to PYR weights costs PYR selectivity more after learning, where deleting SOM and PYR weights does not change
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concept_edges[36]
auditSOM —causes→ plasticity — 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.
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concept_edges[39]
noteSOM —causes→ plasticity — 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
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concept_edges[39]
weightSOM —causes→ plasticity — 0.9
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concept_edges[39]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
outcome (stated by the tagger, no quotation)control — heldcontrols[1]
for which uselimitation — 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
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limitations[0]
for which uselimitation — 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
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limitations[1]
for which uselimitation — 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
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limitations[2]
duplicate oflimitation — limitations[0]
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limitations[3]
for which uselimitation — the same scope as limitations[0] - the inferred connectivity has not been measured
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limitations[3]
for which uselimitation — 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%
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Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
limitations[4]
for which uselimitation — the model-fit statistics as printed: the staged copy carries unfilled placeholders where three reported values should be
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limitations[5]
replicationuntested
limitationThe 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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limitations[0]
limitationThe 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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limitations[1]
limitationthe 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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limitations[2]
limitationand the connectivity the model infers has not been measured; the direct test is named as future work
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limitations[3]
limitationthe 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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limitations[4]
limitationand the staged copy is an author manuscript in which three reported statistics are still unfilled placeholders
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limitations[5]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingLearning increased stimulus selectivity in subsets of pyramidal, PV and SOM cells, but left VIP cells unchanged.
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content_tags.findings[0]
findingPV 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]
findingFitting 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]
findingAfter 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]
findingLearning reduced noise correlations both within and across cell classes, so network firing became more independent.
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content_tags.findings[4]
findingHow 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]
findinglearning 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]
findingPYR 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]
findingTHE 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]
findingAND 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]
findingTHE 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]
findingAND 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]
findingTWO 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]
findingand measured the other way round the same labels agree above 90%, so the disagreement is directional
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content_tags.findings[13]
findingA 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]
findingand again in the constrained fit, where the effect is said to be stronger with P = XX
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content_tags.findings[15]
findingTHE MODEL IS BLIND TO CELL TYPE and cell-class specific differences in interaction weights appear anyway
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content_tags.findings[16]
findingTWO 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]
findingPV 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]
findingAN 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]
findingwith 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]
findinglearning decreased noise correlations within and between classes - SOM cells decorrelating most, PV cells least
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content_tags.findings[21]
findingthe 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]
findingpost 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]
findingPV 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]
findingand 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]
findingTHE 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]
findingwith the proposed mechanism a gate - high correlation with SOM cells prevents plasticity, low correlation permits it
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content_tags.findings[27]
findingthe 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]
findingAND 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]
findingthe 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]
quantity0.31 ± 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]
quantity0.25 ± 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]
quantity0.19 ± 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]
quantity1249, 132, 58 and 175 — Numbers of PYR, PV, SOM and VIP cells imaged, across 8 mice
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content_tags.quantities[3]
quantityslope = 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]
quantity0.013 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]
quantity1.00, 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]
quantity92.6% ... 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]
quantityabove 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]
quantityR 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]
quantityP = XX — a second unfilled placeholder, this time behind the claim that the effect was stronger
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content_tags.quantities[10]
quantity0.31 ± 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]
quantity0.7 — 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]
variablecell class (independent)
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content_tags.variables[2]
variableselectivity index (dependent)
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content_tags.variables[3]
variablerunning speed (covariate)
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content_tags.variables[5]
variableeye position, pupil size, eye movements, running and licking (control)
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content_tags.variables[6]
variableselectivity
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content_tags.variables[7]
variablenoise correlation
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content_tags.variables[8]
variableinteraction weight
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content_tags.variables[9]
variablecoupling
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content_tags.variables[10]
definitionselectivity index (SI)
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content_tags.definitions[0]
definitionnoise correlations
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content_tags.definitions[1]
definitionbehavioral d-prime
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content_tags.definitions[2]
definitionPSTH similarity score
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content_tags.definitions[3]
definitionlinear dynamical system (LDS) model
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content_tags.definitions[4]
definitionselectivity
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content_tags.definitions[5]
definitionnoise correlation
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content_tags.definitions[6]
definitioninteraction weight
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content_tags.definitions[7]
definitiongate
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content_tags.definitions[8]
key-termsselectivity, 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
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptselectivity — not defined in this paper's own words; the quantity learning changes, and the one the whole paper is indexed to
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concepts[0]
conceptlearning — not defined in this paper's own words; the manipulation
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concepts[1]
conceptPV — not defined in this paper's own words; the class that unexpectedly becomes selective
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concepts[3]
conceptnoise correlation — not defined in this paper's own words; the estimate of mutual influence
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concepts[8]
conceptrecurrent — not defined in this paper's own words; the input from the local network
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concepts[22]
conceptmodel — not defined in this paper's own words; the linear dynamical system
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concepts[24]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
sign test as written: sign test with Bonferroni correction
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content_tags.statistics[0]
Wilcoxon rank-sum test P
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content_tags.statistics[1]
Wilcoxon signed-rank test
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content_tags.statistics[2]
correlation R as written: Pearson correlation coefficient
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content_tags.statistics[3]
t-test as written: Student's t-distribution
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content_tags.statistics[4]
bootstrap as written: bootstrap procedure where we randomly selected cells with replacement 10,000 times
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content_tags.statistics[5]
confidence interval CI as written: bootstrapped 90% confidence intervals
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content_tags.statistics[6]
classifier / decoding accuracy as written: random forest decoder
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content_tags.statistics[7]
regression as written: robust regression
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content_tags.statistics[8]
coefficient of determination R 2 as written: we computed R 2
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content_tags.statistics[9]
sign test
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[10]
Wilcoxon rank-sum test
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[11]
bootstrap test
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[12]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typemechanism — a second reading reaching the same value; recorded: mechanism
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alternatives.claim_type
methoda 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 — a second reading DIFFERING from the recorded value; recorded: calcium-imaging
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alternatives.method
phenomenonlearning-induced sharpening of cortical representations, decomposed into which cell class changes and which interactions carry the change — a second reading DIFFERING from the recorded value; recorded: learning-induced stimulus selectivity in visual cortical interneurons
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alternatives.phenomenon

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