A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex — TEI-tagged view

de Vries, Lecoq, Buice, Groblewski, Ocker, Oliver, Feng, Cain · 2020 · neuroscience · doi:10.1038/s41593-019-0550-9 · 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 suggested list: the recording modality is in vivo two-photon calcium imaging, not electrophysiology, so a precise lowercase-hyphenated value was used; the paper is also partly a resource/pipeline paper ("Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset."). Claim relations is restricted to the three places where prior work is named in words — nearly all other citations in this article are bare superscript numbers (e.g. "While previous literature has found higher direction selectivity in layer 4 within V1 18"), which the rules say must not be turned into relations. Replication status left untested: the paper reports a new survey and does not state that its classification has been independently reproduced. 2026-08-13 and 2026-08-15 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.
32 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.
Other fields define these differently the same word, defined another way in another discipline
bias 38 fields · reliability 5 fields · coefficient of variation 4 fields · model 4 fields · quality control 3 fields · class 3 fields · and 2 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
none class8 model22 bias19 quality control10 survey70 receptive field5 natural2 gratings Vip14 Sst7 Cre line V16 RL2 visual cortex9 running2 area2 sparseness2 calcium-imaging10
Method, as described: two-photon-calcium-imaging

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonfunctional organization of mouse visual cortex— “These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.”
claim-typeexistence— “While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas.”
method — evidence— “Calcium imaging was performed using a two-photon-imaging instrument (either a Scientifica Vivoscope or a Nikon A1R MP+; the Nikon system was adapted to provide space to accommodate the running disk).”
object of study — evidence— “To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available…
research question — evidence— “A key challenge results from the fact that this understanding is based on many small studies, recording responses from different stages in the circuit, using different stimuli and analyses 5 . The inherent experimental selection biases and lack of standardization of this approach introduce additional obstacles to creating a cohesive…
contribution — evidence— “To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available…
transferfrom industrial process engineering, via standard operating procedures: the pipeline itself - STANDARD OPERATING PROCEDURES WITH INTEGRATED ENGINEERING TOOLS, applied to make 243 mice comparable to each other— “Here we scaled calcium imaging, combining standard operating procedures with integrated engineering tools to address some of the challenges of this difficult technique, to create an unprecedented survey of 59,610 neurons in mouse visual cortex, across 243 mice, using a standard and well-studied but diverse set of visual stimuli.”transfers[0]
mechanismHand-tuning stimuli to each recorded neuron builds selection bias into the classical literature, which is why the standard model looks better in small studies than in an unbiased survey.— “The inherent experimental selection biases and lack of standardization of this approach introduce additional obstacles to creating a cohesive understanding of cortical function.”mechanisms[0]
mechanismThe unresponsive 'none' neurons may in fact be visually driven but tuned to highly sparse, specific natural features constructed by hierarchical processing, which the presented stimulus set never hits.— “It is possible these neurons are visually driven, but are responsive to highly sparse and specific natural features that may arise through hierarchical processing 37 .”mechanisms[1]
mechanismBecause responses are so sparse, different neurons are active at different moments, which is what keeps their pairwise noise correlations weak.— “Such high sparseness suggests that neurons are active at different times and thus their activities are weakly correlated.”mechanisms[2]
mechanismthe bias this survey is built against, described as a procedure: NEURONS WERE IDENTIFIED BY RESPONDING TO A TEST STIMULUS - THE STIMULUS WAS THEN HAND-TUNED TO ELICIT THE STRONGEST RELIABLE RESPONSE FROM THAT NEURON, AND THE EXPERIMENT PROCEEDED USING MANIPULATIONS AROUND THIS CONDITION— “Historically, visual physiology has been dominated by single-neuron electrophysiological recordings in which neurons were identified by responding to a test stimulus. The stimulus was then hand-tuned to elicit the strongest reliable response from that neuron, and the experiment proceeded using manipulations around this condition. Such studies…mechanisms[3]
mechanismand what that procedure cannot see: THESE STUDIES MAY HAVE FAILED TO CAPTURE THE VARIABILITY OF RESPONSES, THE BREADTH OF FEATURES THAT WILL ELICIT A NEURAL RESPONSE, AND THE BREADTH OF FEATURES THAT DO NOT ELICIT A RESPONSE - THIS RESULTS IN SYSTEMATIC BIAS IN THE MEASUREMENT OF NEURONS AND A CONFIRMATION BIAS REGARDING MODEL ASSUMPTIONS— “Historically, visual physiology has been dominated by single-neuron electrophysiological recordings in which neurons were identified by responding to a test stimulus. The stimulus was then hand-tuned to elicit the strongest reliable response from that neuron, and the experiment proceeded using manipulations around this condition. Such studies…mechanisms[4]
mechanismthe one design decision that removes it: THIS PIPELINE REDUCED CRITICAL EXPERIMENTAL BIASES BY SEPARATING QUALITY CONTROL OF DATA COLLECTION FROM RESPONSE CHARACTERIZATION— “This pipeline reduced critical experimental biases by separating quality control of data collection from response characterization. Such a survey is crucial for assessing the successes and shortcomings of contemporary models of visual cortex.”mechanisms[5]
mechanismand the reason it matters for a whole literature: THIS UNDERSTANDING IS BASED ON MANY SMALL STUDIES, RECORDING RESPONSES FROM DIFFERENT STAGES IN THE CIRCUIT, USING DIFFERENT STIMULI AND ANALYSES - THE INHERENT EXPERIMENTAL SELECTION BIASES AND LACK OF STANDARDIZATION OF THIS APPROACH INTRODUCE ADDITIONAL OBSTACLES TO CREATING A COHESIVE UNDERSTANDING OF CORTICAL FUNCTION— “A key challenge results from the fact that this understanding is based on many small studies, recording responses from different stages in the circuit, using different stimuli and analyses 5 . The inherent experimental selection biases and lack of standardization of this approach introduce additional obstacles to creating a cohesive…mechanisms[6]
builds-on Jewell et al. L0-penalized event detection method— “We used the L0-penalized method of Jewell, et al for event detection”claim_relations[0]
builds-on Vinje and Gallant definition of lifetime sparseness— “Lifetime sparseness was computed using the definition in Vinje and Gallant 20 .”claim_relations[1]
builds-on Schoppe et al. computation of CCmax— “We follow the computation from Schoppe, et al . 76 :”claim_relations[2]
critiques the standard model of visual cortex built from hand-tuned single-neuron recordings— “Historically, visual physiology has been dominated by single-neuron electrophysiological recordings in which neurons were identified by responding to a test stimulus. The stimulus was then hand-tuned to elicit the strongest reliable response from that neuron, and the experiment proceeded using manipulations around this condition. Such studies…claim_relations[3]
contradicts the smaller summation area reported for Vip neurons— “Interestingly, receptive field mapping using locally sparse noise revealed that Vip neurons in V1 have remarkably large receptive field areas, larger than both Sst and excitatory neurons ( Figure 3f ), in contrast to the smaller summation area for Vip neurons, previously measured using windowed drifting gratings 23 , 33 . This suggests that…claim_relations[4]
extends previous work showing functional differences among cortical areas in layer 2/3, extended across layers and 12 transgenic lines— “Further, it is known that stimulus statistics affect visual responses, such that responses to natural scenes cannot be well predicted by responses to noise or grating stimuli 10 – 15 . To examine the extent of this discrepancy and its variation across areas and layers, we designed a stimulus set that included both artificial (gratings and…claim_relations[5]
qualifies the assignment of AL, PM and AM to a dorsal stream— “At the same time, the overrepresentation of classes responsive to natural movies and motion stimuli in areas AL, PM, and AM relative to the other classes (which are more responsive to spatial stimuli) is consistent with the assignment of these areas to the putative “dorsal” or “motion” stream”claim_relations[6]
senseTHE LARGEST CLASS OF NEURONS RESPONDS TO NOTHING: REMARKABLY, THE LARGEST CLASS OF NEURONS WAS THE “none” CLASS, CONSTITUTING THOSE NEURONS THAT DID NOT RESPOND RELIABLY TO ANY OF THE STIMULI (34% OF NEURONS) - THESE NEURONS ARE THE LEAST LIKELY TO BE DESCRIBED BY “classical receptive fields,” AS EVIDENCED BY THEIR POOR MODEL PERFORMANCE FOR ALL STIMULI— “Remarkably, the largest class of neurons was the “none” class, constituting those neurons that did not respond reliably to any of the stimuli (34% of neurons). These neurons are the least likely to be described by “classical receptive fields,” as evidenced by their poor model performance for all stimuli”senses[0]
senseand in one area it is almost everything: AREA RL HAS THE LARGEST PROPORTION OF NEURONS IN THE “none” CLASS, OVER 85%, CONSISTENT WITH THE VERY LOW PERCENT OF RESPONSIVE NEURONS— “Area RL has the largest proportion of neurons in the “none” class, over 85%, consistent with the very low percent of responsive neurons ( Figure 2 ). It is possible that neurons in this area are specialized for visual features not probed here, or that they show a greater degree of multi-modality than in the other visual areas, integrating…senses[1]
senseTHE PAPER ASKS THE QUESTION ITSELF AND LEAVES IT OPEN: WHAT, THEN, DO THESE NEURONS DO? - and offers both readings, that they ARE RESPONSIVE TO HIGHLY SPARSE AND SPECIFIC NATURAL FEATURES or COULD BE INVOLVED IN NON-VISUAL COMPUTATION— “What, then, do these neurons do? It is possible these neurons are visually driven, but are responsive to highly sparse and specific natural features that may arise through hierarchical processing 37 . Indeed, the field has a growing body of evidence that the rodent visual system exhibits sophisticated computations. For instance, neurons as…senses[2]
senseTHE CLASS THE STANDARD MODEL EXPLAINS IS A TENTH OF THE CORTEX: NEURONS IN THE “DG-SG-NS-NM” CLASS WERE WELL PREDICTED, WITH VALUES COMPARABLE TO THOSE FOUND IN PRIMATES - HOWEVER, THIS CLASS CONSTITUTES ONLY 10% OF THE MOUSE VISUAL CORTEX. And EVEN WITH THE INCLUSION OF RUNNING, OUR MODELS PREDICT RESPONSES IN A MINORITY OF NEURONS— “The true test of a model is its ability to predict arbitrary novel responses, in addition to responses from stimuli used for characterization. Even with the inclusion of running, our models predict responses in a minority of neurons ( Figure 7 ). Neurons in the “DG-SG-NS-NM” class were well predicted, with values comparable to those found in…senses[3]
senseTHE SELECTION BIAS OF A WHOLE LITERATURE, DESCRIBED AS A PROCEDURE: NEURONS WERE IDENTIFIED BY RESPONDING TO A TEST STIMULUS - THE STIMULUS WAS THEN HAND-TUNED TO ELICIT THE STRONGEST RELIABLE RESPONSE FROM THAT NEURON... THIS RESULTS IN SYSTEMATIC BIAS IN THE MEASUREMENT OF NEURONS AND A CONFIRMATION BIAS REGARDING MODEL ASSUMPTIONS— “Historically, visual physiology has been dominated by single-neuron electrophysiological recordings in which neurons were identified by responding to a test stimulus. The stimulus was then hand-tuned to elicit the strongest reliable response from that neuron, and the experiment proceeded using manipulations around this condition. Such studies…senses[4]
senseand the fix is one sentence of study design: THIS PIPELINE REDUCED CRITICAL EXPERIMENTAL BIASES BY SEPARATING QUALITY CONTROL OF DATA COLLECTION FROM RESPONSE CHARACTERIZATION - the decision to include a cell is made before anyone knows whether it responds— “This pipeline reduced critical experimental biases by separating quality control of data collection from response characterization. Such a survey is crucial for assessing the successes and shortcomings of contemporary models of visual cortex.”senses[5]
senseTHE PROBLEM STATED AS A PROBLEM OF SYNTHESIS: THIS UNDERSTANDING IS BASED ON MANY SMALL STUDIES, RECORDING RESPONSES FROM DIFFERENT STAGES IN THE CIRCUIT, USING DIFFERENT STIMULI AND ANALYSES - THE INHERENT EXPERIMENTAL SELECTION BIASES AND LACK OF STANDARDIZATION OF THIS APPROACH INTRODUCE ADDITIONAL OBSTACLES TO CREATING A COHESIVE UNDERSTANDING OF CORTICAL FUNCTION— “A key challenge results from the fact that this understanding is based on many small studies, recording responses from different stages in the circuit, using different stimuli and analyses 5 . The inherent experimental selection biases and lack of standardization of this approach introduce additional obstacles to creating a cohesive…senses[6]
senseand the field asked for exactly this: INFLUENTIAL REVIEWS HAVE QUESTIONED THE VALIDITY OF THIS STANDARD MODEL, AND HAVE ARGUED THAT “What would be most helpful is to accumulate a database of single unit or multi-unit data that would allow modelers to test their best theory under ecological conditions.”— “On the basis of these issues, influential reviews have questioned the validity of this standard model 5 – 7 , and have argued that “What would be most helpful is to accumulate a database of single unit or multi-unit data that would allow modelers to test their best theory under ecological conditions.”senses[7]
senseA MISSING HALF OF THE ANSWER, ATTRIBUTED TO THE WINDOW AND NOT THE BRAIN: THE LACK OF AN INVERSE RELATIONSHIP, WHEREIN SPATIAL INFORMATION IS OVERREPRESENTED RELATIVE TO MOTION IN A PUTATIVE “ventral” STREAM, LIKELY REFLECTS THE FACT THAT WE WERE UNABLE TO IMAGE THE PUTATIVE VENTRAL AREAS LI, POR, OR P WITHIN OUR CRANIAL WINDOW— “The lack of an inverse relationship, wherein spatial information is overrepresented relative to motion in a putative “ventral” stream, likely reflects the fact that we were unable to image the putative ventral areas LI, POR, or P within our cranial window.”senses[8]
senseTWO INTERNEURON CLASSES, 94% AGAINST 9%, TO THE SAME STIMULUS: 94% OF Sst NEURONS IN V1 ARE RESPONSIVE TO DRIFTING GRATINGS, AND RESPOND QUITE RELIABLY ACROSS TRIALS, FAR MORE THAN THE OTHER CRE LINES - Vip NEURONS, ON THE OTHER HAND, ARE LARGELY UNRESPONSIVE, EVEN SUPPRESSED, BY DRIFTING GRATINGS, WITH ONLY 9% OF Vip NEURONS IN V1 LABELLED RESPONSIVE— “Sst neurons are strongly driven by all visual stimuli used here, with the plurality belonging to the “DG-SG-NS-NM” class ( Figure 5f ). Their responses to drifting gratings are particularly robust in that 94% of Sst neurons in V1 are responsive to drifting gratings, and respond quite reliably across trials, far more than the other Cre lines (…senses[9]
senseand the same cells are highly responsive to a different stimulus class: Vip NEURONS ARE VERY RESPONSIVE TO BOTH NATURAL SCENES AND NATURAL MOVIES, but SHOW LITTLE SELECTIVITY TO THESE STIMULI, AS THEIR MEDIAN LIFETIME SPARSENESS IS LOWER THAN BOTH THE Sst AND THE EXCITATORY NEURONS. `Vip cells are unresponsive` is true of one stimulus set and false of another— “Vip neurons, however, are very responsive to both natural scenes and natural movies, the majority falling in the “NS-NM” class ( Figure 6f , Extended Data 6 , 7 , Supplementary Figure 8 ), but show little selectivity to these stimuli, as their median lifetime sparseness is lower than both the Sst and the excitatory neurons”senses[10]
senseA MEASUREMENT THAT REVERSES AN EARLIER ONE, WITH THE STIMULUS NAMED: RECEPTIVE FIELD MAPPING USING LOCALLY SPARSE NOISE REVEALED THAT Vip NEURONS IN V1 HAVE REMARKABLY LARGE RECEPTIVE FIELD AREAS, LARGER THAN BOTH Sst AND EXCITATORY NEURONS, IN CONTRAST TO THE SMALLER SUMMATION AREA FOR Vip NEURONS, PREVIOUSLY MEASURED USING WINDOWED DRIFTING GRATINGS— “Interestingly, receptive field mapping using locally sparse noise revealed that Vip neurons in V1 have remarkably large receptive field areas, larger than both Sst and excitatory neurons ( Figure 3f ), in contrast to the smaller summation area for Vip neurons, previously measured using windowed drifting gratings 23 , 33 . This suggests that…senses[11]
senseTHE STIMULUS CLASS DECIDES THE ANSWER, AND THE PAPER SAYS SO BEFORE MEASURING: IT IS KNOWN THAT STIMULUS STATISTICS AFFECT VISUAL RESPONSES, SUCH THAT RESPONSES TO NATURAL SCENES CANNOT BE WELL PREDICTED BY RESPONSES TO NOISE OR GRATING STIMULI - WHILE ARTIFICIAL STIMULI CAN BE EASILY PARAMETERIZED AND INTERPRETED, NATURAL STIMULI ARE CLOSER TO WHAT IS ETHOLOGICALLY RELEVANT— “Further, it is known that stimulus statistics affect visual responses, such that responses to natural scenes cannot be well predicted by responses to noise or grating stimuli 10 – 15 . To examine the extent of this discrepancy and its variation across areas and layers, we designed a stimulus set that included both artificial (gratings and…senses[12]
sensethe headline positive, with its complement: 77% OF NEURONS IN THE MOUSE VISUAL CORTEX RESPOND TO AT LEAST ONE OF THESE VISUAL STIMULI - and THE RESPONSES TO ALL STIMULI ARE HIGHLY SPARSE AND VARIABLE— “We find that 77% of neurons in the mouse visual cortex respond to at least one of these visual stimuli, many showing classical tuning properties, such as orientation and direction selective responses to gratings. These tuning properties exhibit differences across cortical areas and Cre lines. While subtle differences do exist between the…senses[13]
senseTHE DIFFERENCES ARE BETWEEN THE INHIBITORY LINES, NOT THE EXCITATORY ONES: WHILE SUBTLE DIFFERENCES DO EXIST BETWEEN THE EXCITATORY CRE LINES, THESE POPULATIONS ARE LARGELY SIMILAR; THE MORE MARKED DIFFERENCES ARE AMONG THE INHIBITORY INTERNEURONS— “We find that 77% of neurons in the mouse visual cortex respond to at least one of these visual stimuli, many showing classical tuning properties, such as orientation and direction selective responses to gratings. These tuning properties exhibit differences across cortical areas and Cre lines. While subtle differences do exist between the…senses[14]
senseand yet the excitatory lines are not interchangeable either: RORB, SCNN1A-TG3, AND NR5A1, WHICH LABEL DISTINCT LAYER 4 POPULATIONS IN V1, EXHIBIT DISTINCT SPATIAL AND TEMPORAL TUNING PROPERTIES, DIFFERENT DEGREES OF RUNNING CORRELATION, AND SUBTLE DIFFERENCES IN THEIR CLASS DISTRIBUTION - THESE DIFFERENCES SUGGEST THAT THERE ARE SEPARATE CHANNELS OF FEEDFORWARD INFORMATION— “On a coarse scale, excitatory populations behave similarly; however, closer examination reveals distinct functional properties across Cre lines. For instance, Rorb, Scnn1a-Tg3, and Nr5a1, which label distinct layer 4 populations in V1, exhibit distinct spatial and temporal tuning properties ( Figure 3 , Supplementary Figure 1 , 2 ), different…senses[15]
senseTHE CLASSES DO NOT RESPECT THE AREAS: THE DIFFERENT CLASSES ARE LARGELY INTERMINGLED, AND FOUND IN ALL OF THE CORTICAL AREAS RECORDED HERE, SUGGESTING A LARGELY PARALLEL ORGANIZATION— “The different classes are largely intermingled, and found in all of the cortical areas recorded here, suggesting a largely parallel organization”senses[16]
senseand what organisation there is comes from proportions rather than presence: THE OVERREPRESENTATION OF CLASSES RESPONSIVE TO NATURAL MOVIES AND MOTION STIMULI IN AREAS AL, PM, AND AM RELATIVE TO THE OTHER CLASSES IS CONSISTENT WITH THE ASSIGNMENT OF THESE AREAS TO THE PUTATIVE “dorsal” OR “motion” STREAM— “At the same time, the overrepresentation of classes responsive to natural movies and motion stimuli in areas AL, PM, and AM relative to the other classes (which are more responsive to spatial stimuli) is consistent with the assignment of these areas to the putative “dorsal” or “motion” stream”senses[17]
senseTHE DATASET IS THE CONTRIBUTION AND IT IS ALREADY BEING REUSED: THESE DATA HAVE BEEN USED BY OTHER RESEARCHERS TO DEVELOP IMAGE PROCESSING METHODS, TO EXAMINE STIMULUS ENCODING AND DECODING, AND TO TEST MODELS OF CORTICAL COMPUTATIONS— “We believe that the openly available Allen Brain Observatory provides an important foundational resource for…senses[18]
controlquality control of the recording separated from characterisation of the response — held — rules out the dataset selecting for cells that respond - STRICT QUALITY CONTROL MEASURES NOT DEPENDENT UPON STIMULUS-DRIVEN ACTIVITYcontrols[0]
controlthe functional classes validated against a model containing the standard machinery plus running speed — partial — rules out the classes being an artefact of the reliability clustering - the models contain MOST OF THE BASIC FEATURES FOUND IN VISUAL NEUROPHYSIOLOGICAL MODELING as well as THE RUNNING SPEED OF THE MOUSE
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.
controls[1]
controlboth artificial and natural stimuli presented to the same neurons — held — rules out a conclusion that holds only for parameterised stimuli - RESPONSES TO NATURAL SCENES CANNOT BE WELL PREDICTED BY RESPONSES TO NOISE OR GRATING STIMULI
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controls[2]
controlrunning checked as an explanation for the unresponsive class — held — rules out locomotion accounting for the 'none' class - WE FOUND LITTLE EVIDENCE THAT THESE NEURONS WERE CORRELATED WITH THE MOUSE'S RUNNING
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controls[3]
controla STANDARDISED survey - fixed operating procedures and stimuli applied identically across 59,610 neurons in 243 mice - in place of experiment-specific stimulus choices — held — rules out the experimenter's choice of stimulus as the determinant of what is found. The paper states the fault it is answering: earlier studies 'may have failed to capture…', producing 'a CONFIRMATION BIAS regarding…'
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controls[4]
differs inthe standard model of visual cortex built from hand-tuned single-neuron recordings — how the neurons enter the sample - NEURONS WERE IDENTIFIED BY RESPONDING TO A TEST STIMULUS and THE STIMULUS WAS THEN HAND-TUNED TO ELICIT THE STRONGEST RELIABLE RESPONSE
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[3]
differs inthe smaller summation area reported for Vip neurons — the stimulus used to map it - LOCALLY SPARSE NOISE here, against WINDOWED DRIFTING GRATINGS previously
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claim_relations[4]
differs inthe assignment of AL, PM and AM to a dorsal stream — that the complement was not testable - WE WERE UNABLE TO IMAGE THE PUTATIVE VENTRAL AREAS LI, POR, OR P WITHIN OUR CRANIAL WINDOW
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.
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]
notenone —is-a→ class — THE LARGEST CLASS OF NEURONS WAS THE 'none' CLASS, CONSTITUTING THOSE NEURONS THAT DID NOT RESPOND RELIABLY TO ANY OF THE STIMULI (34% OF NEURONS)
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concept_edges[0]
auditnone —part-of→ RL — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but RL has the largest share of non-responsive neurons; part-of is loose.
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concept_edges[2]
depends onnone —part-of→ RL — which area is measured - the same class is 34% of the survey as a whole
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concept_edges[2]
notenone —part-of→ RL — AREA RL HAS THE LARGEST PROPORTION OF NEURONS IN THE 'none' CLASS, OVER 85%
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concept_edges[2]
weightnone —part-of→ RL — 0.85
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concept_edges[2]
auditquality control —causes→ bias — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the pipeline reduced biases; the edge carries no direction.
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concept_edges[4]
notequality control —causes→ bias — THIS PIPELINE REDUCED CRITICAL EXPERIMENTAL BIASES BY SEPARATING QUALITY CONTROL OF DATA COLLECTION FROM RESPONSE CHARACTERIZATION
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concept_edges[4]
auditnatural —contrasted-with→ gratings — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the source endpoint 'natural' is a bare adjective; the span writes 'natural (scenes and movies) stimuli'.
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concept_edges[11]
notenatural —contrasted-with→ gratings — RESPONSES TO NATURAL SCENES CANNOT BE WELL PREDICTED BY RESPONSES TO NOISE OR GRATING STIMULI
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concept_edges[11]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
outcome (stated by the tagger, no quotation)control — partialcontrols[1]
outcome (stated by the tagger, no quotation)control — heldcontrols[2]
outcome (stated by the tagger, no quotation)control — heldcontrols[3]
outcome (stated by the tagger, no quotation)control — heldcontrols[4]
for which uselimitation — the same scope as limitations[4] - the putative ventral higher visual areas could not be imaged, so the expected inverse organisation is missing for a reason about the window, not the brain
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limitations[0]
for which uselimitation — the class of neurons responding only to natural stimuli: THESE NEURONS COULD BE 'CLASSICAL' NEURONS AS WELL BUT ARE TUNED FOR SPATIAL OR TEMPORAL FREQUENCIES THAT WERE NOT INCLUDED IN OUR STIMULUS SET
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limitations[1]
for which uselimitation — the explanatory reach of the wavelet models: THERE IS A GREAT DEAL OF VISUALLY RESPONSIVE ACTIVITY THAT IS NOT BEING CAPTURED BY THESE MODELS
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limitations[2]
for which uselimitation — the epileptiform screening, and therefore the quality control on the inhibitory lines: INHIBITORY LINES WERE EXCLUDED FROM THIS ANALYSIS AS THE NEURONAL LABELLING WAS TOO SPARSE TO RELIABLY ASSESS THESE EVENTS
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limitations[3]
for which uselimitation — reading the absence of a ventral counterpart to the dorsal specialisation as a finding about the cortex
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limitations[4]
for which uselimitation — reading 'does not respond to visual stimuli' as 'is not visual'
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limitations[5]
for which uselimitation — reading poor prediction for artificial stimuli as a property of the neurons rather than of the parameter range
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[6]
for which uselimitation — any claim that the standard models describe this cortex: EVEN WITH THE INCLUSION OF RUNNING, OUR MODELS PREDICT RESPONSES IN A MINORITY OF NEURONS
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[7]
replicationuntested
limitationThe putative ventral higher visual areas could not be imaged, so the expected inverse (spatial-over-motion) organisation could not be tested.
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[0]
limitationNeurons classed as responding only to natural stimuli may be conventional cells tuned to spatial or temporal frequencies the stimulus set did not sample.
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[1]
limitationThe wavelet models leave a large amount of visually responsive activity unexplained.
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[2]
limitationThe epileptiform-event screening used to exclude mice could not be applied to the inhibitory Cre lines.
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[3]
limitationone half of the expected organisation is missing for a reason that is about the window, not the brain - WE WERE UNABLE TO IMAGE THE PUTATIVE VENTRAL AREAS LI, POR, OR P WITHIN OUR CRANIAL WINDOW
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limitations[4]
limitationthe largest class may be an artefact of the stimulus set rather than a property of the cells - IT IS POSSIBLE THESE NEURONS ARE VISUALLY DRIVEN, BUT ARE RESPONSIVE TO HIGHLY SPARSE AND SPECIFIC NATURAL FEATURES, or THESE NEURONS COULD BE INVOLVED IN NON-VISUAL COMPUTATION
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limitations[5]
limitationand the stimulus parameters may have been set too low - AS OUR STIMULUS PARAMETERS WERE CHOSEN TO MATCH PREVIOUS MEASUREMENTS OF MOUSE ACUITY, THIS COULD SUGGEST THAT THE ACUITY OF MOUSE HAS BEEN UNDERESTIMATED
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[6]
limitationthe models fail for most of the cortex - EVEN WITH THE INCLUSION OF RUNNING, OUR MODELS PREDICT RESPONSES IN A MINORITY OF NEURONS
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[7]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingIN TOTAL, 77% OF NEURONS WERE RESPONSIVE TO AT LEAST ONE OF THE VISUAL STIMULI PRESENTED
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.findings[0]
findingBY FAR THE LARGEST SINGLE CLASS REVEALED BY THIS ANALYSIS IS THAT OF NEURONS THAT ARE LARGELY UNRESPONSIVE TO ALL STIMULI, TERMED `none,` WHICH CONTAINS 34±2% OF THE NEURONS
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content_tags.findings[1]
findingArea RL is the extreme case, with over 85% of its neurons falling into the unresponsive 'none' class.
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.findings[2]
findingHow reliably a neuron responds to one stimulus type predicts almost nothing about how reliably it responds to another.
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.findings[3]
findingA standard linear-plus-quadratic Gabor wavelet encoding model fits only about 2% of the population well, with median correlations near 0.2.
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content_tags.findings[4]
findingAdding the mouse's running speed to the encoding model gave essentially no extra predictive power except for Vip neurons in V1.
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content_tags.findings[5]
findingMost neurons do not respond on most individual trials, even to the stimulus condition that drives their largest average response.
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content_tags.findings[6]
findingSst interneurons are the most reliably driven population, with 94% of Sst neurons in V1 responsive to drifting gratings.
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content_tags.findings[7]
findingVip interneurons are the mirror image: only 9% of Vip neurons in V1 are responsive to drifting gratings, and many are suppressed by them.
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content_tags.findings[8]
findingCOMPARISONS ACROSS AREAS AND LAYERS REVEALED THAT DIRECTION SELECTIVITY IS HIGHEST IN LAYER 4 OF V1
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content_tags.findings[9]
findingExcitatory neurons had a median lifetime sparseness of 0.77 for natural scenes.
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content_tags.findings[10]
findingTHIS DIVERGENCE IN REPRESENTATION OF THE MOTION CLASSES FROM THE NATURAL CLASSES IN AREAS AL, PM, AND AM IS CONSISTENT WITH THE PUTATIVE DORSAL AND VENTRAL STREAM SEGREGATION IN THE VISUAL CORTEX
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content_tags.findings[11]
findingVip neurons in V1 have unusually large receptive fields mapped with sparse noise, larger than Sst or excitatory neurons and at odds with their small summation area measured with windowed gratings.
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content_tags.findings[12]
findingREMARKABLY, THE LARGEST CLASS OF NEURONS WAS THE `none` CLASS, CONSTITUTING THOSE NEURONS THAT DID NOT RESPOND RELIABLY TO ANY OF THE STIMULI (34% OF NEURONS)
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content_tags.findings[13]
findingAREA RL HAS THE LARGEST PROPORTION OF NEURONS IN THE `none` CLASS, OVER 85%
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.findings[14]
findingthe well-predicted class CONSTITUTES ONLY 10% OF THE MOUSE VISUAL CORTEX, and EVEN WITH THE INCLUSION OF RUNNING, OUR MODELS PREDICT RESPONSES IN A MINORITY OF NEURONS
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.findings[15]
finding77% OF NEURONS IN THE MOUSE VISUAL CORTEX RESPOND TO AT LEAST ONE OF THESE VISUAL STIMULI, and THE RESPONSES TO ALL STIMULI ARE HIGHLY SPARSE AND VARIABLE
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.findings[16]
finding94% OF Sst NEURONS IN V1 ARE RESPONSIVE TO DRIFTING GRATINGS, while ONLY 9% OF Vip NEURONS IN V1 are LABELLED RESPONSIVE
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.findings[17]
findingVip NEURONS IN V1 HAVE REMARKABLY LARGE RECEPTIVE FIELD AREAS, LARGER THAN BOTH Sst AND EXCITATORY NEURONS, IN CONTRAST TO THE SMALLER SUMMATION AREA PREVIOUSLY MEASURED USING WINDOWED DRIFTING GRATINGS
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content_tags.findings[18]
findingTHE DIFFERENT CLASSES ARE LARGELY INTERMINGLED, AND FOUND IN ALL OF THE CORTICAL AREAS RECORDED HERE, SUGGESTING A LARGELY PARALLEL ORGANIZATION
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content_tags.findings[19]
findingWHILE SUBTLE DIFFERENCES DO EXIST BETWEEN THE EXCITATORY CRE LINES, THESE POPULATIONS ARE LARGELY SIMILAR; THE MORE MARKED DIFFERENCES ARE AMONG THE INHIBITORY INTERNEURONS
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.findings[20]
quantity59,610 — neurons imaged in the survey, drawn from 432 experiments
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content_tags.quantities[0]
quantity77% — share of neurons responsive to at least one visual stimulus
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.quantities[1]
quantity34±2% — share of clustered neurons in the 'none' class, unresponsive to every stimulus
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.quantities[2]
quantity33% — share of neurons in area RL responding to any stimulus, the lowest of the six areas
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content_tags.quantities[3]
quantitymedian 0.36 — WHILE Sst NEURONS WERE COMPARABLE TO EXCITATORY NEURONS (MEDIAN 0.77), Vip NEURONS EXHIBITED LOW LIFETIME SPARSENESS (MEDIAN 0.36)
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content_tags.quantities[4]
quantity13% — share of neurons whose evoked responses were significantly modulated by running
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.quantities[5]
quantity1.9 fold — increase in the median evoked response when the mouse was running versus stationary
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.quantities[6]
quantitymedian 0.02 — noise correlation of excitatory populations, against 0.06 for Sst and 0.15 for Vip
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.quantities[7]
quantity517451 — number of free parameters in the 3D Gabor wavelet encoding model
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.quantities[8]
quantity33.6% — share of neurons successfully matched across all three imaging sessions
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.quantities[9]
quantity21% — share of responsive neurons whose trial responses the average tuning curve predicts with r above 0.5 (11% of all neurons)
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.quantities[10]
quantityover 85% — share of area RL neurons in the unresponsive 'none' class
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.quantities[11]
quantity34% — of neurons in the 'none' class - the largest single class in the survey
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.quantities[12]
quantityonly 10% — of the mouse visual cortex in the class the standard model predicts well
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.quantities[13]
quantity77% — of neurons responding to AT LEAST ONE of the stimuli - so 23% respond to none of them
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.quantities[14]
quantity94% — of Sst neurons in V1 responsive to drifting gratings
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.quantities[15]
quantityover 85% — of neurons in area RL falling in the 'none' class
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.quantities[16]
variableimaging depths (independent)
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content_tags.variables[1]
variablemean response (dependent)
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content_tags.variables[2]
variabletuning metrics (dependent)
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.variables[3]
variablerunning state (covariate)
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content_tags.variables[4]
variablepupil position and area (covariate)
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content_tags.variables[5]
variablerunning
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.variables[7]
variableclass
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.variables[8]
definitionlifetime sparseness
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content_tags.definitions[0]
definitionpopulation sparseness
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content_tags.definitions[1]
definitionreliability
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content_tags.definitions[3]
definitionresponse class
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content_tags.definitions[4]
key-termsnone, class, model, bias, quality control, survey, receptive field, natural, gratings, Vip, Sst, Cre line, V1, RL, visual cortex, running, sparseness
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.
conceptnone — not defined in this paper's own words; the class of neurons that DID NOT RESPOND RELIABLY TO ANY OF THE STIMULI - 34% of the survey, and the largest single class
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concepts[0]
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.
correlation r as written: Pearson correlation
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content_tags.statistics[0]
Kolmogorov-Smirnov test as written: Kolmogorov-Smirnov (KS) test
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content_tags.statistics[1]
Bonferroni correction as written: Bonferroni correction for the number of comparisons
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content_tags.statistics[2]
chi-squared test as written: chi-square test
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content_tags.statistics[3]
Sidak multiple-comparisons correction p
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content_tags.statistics[4]
Gaussian mixture model clustering with Bayesian Information Criterion model selection as written: Gaussian Mixture Model clustering ... average Bayesian Information Criterion
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content_tags.statistics[5]
K-nearest-neighbors classification (decoding) as written: K Nearest-Neighbors classifiers
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content_tags.statistics[6]
cross-validation as written: five-fold cross-validation
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content_tags.statistics[7]
ridge regression as written: linear ridge regression models
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content_tags.statistics[8]
polynomial regression
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content_tags.statistics[9]
coefficient of variation
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content_tags.statistics[10]
interquartile range as written: quartiles of the data ... 1.5 times the interquartile range
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content_tags.statistics[11]
bootstrap as written: bootstrapped samples
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content_tags.statistics[12]
permutation/shuffle test as written: stimulus trials were shuffled to break trial-wise correlations
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content_tags.statistics[13]
lifetime sparseness
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[14]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typeexistence — a second reading reaching the same value; recorded: existence
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.
alternatives.claim_type
methodrun every animal through one standardised pipeline with QUALITY CONTROL MEASURES NOT DEPENDENT UPON STIMULUS-DRIVEN ACTIVITY, so that entry into the dataset is decided before any response is measured — a second reading DIFFERING from the recorded value; recorded: calcium-imaging
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alternatives.method
phenomenona population of cortical neurons that respond reliably to none of the stimuli a field has spent decades building — a second reading DIFFERING from the recorded value; recorded: functional organization of mouse visual cortex
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alternatives.phenomenon

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.

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