A category-free neural population supports evolving demands during decision-making

Raposo, Kaufman, Churchland · Nature Neuroscience · 2014 · neuroscience · 10.1038/nn.3865 · record Raposo2014-a-category-free-neural-population-supports-e
tag layer · index · concepts · authors · עברית
Where this record's text came from. faceted 2026-08 by reading the locally held text (xml); every evidence string verified as an exact substring of that text before merging. Claim type is 'existence' for the positive claim that a single category-free population supports evolving demands; the PAIRS result is itself a null result ('Novel tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories'), and a tagger applying rule 4 strictly could code this paper as null-result instead. The paper also builds two new analyses, so method-building is a secondary shape. Inactivation used both muscimol and DREADD, and the causal claim is restricted to visual decisions - auditory decisions were spared. 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.
11 quotations on this page contain a reference number where the paper printed a superscript. This journal style prints citations as raised numerals — parameters12,13 — and the extraction flattened them into the line, so the sentence reads parameters 12 , 13 . The digits are citation markers and not part of what the author wrote. The quotes keep them for the same reason they keep every other extraction fault: a quote is reproduced exactly or it proves nothing.
How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Other fields define these differently the same word, defined another way in another discipline
principal component analysis 5 fields · selectivity 3 fields · SVM 3 fields · categories 3 fields · clustering 3 fields · population 3 fields · and 4 more →

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 29 concept tags on this paper, none is grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
category categories neuron neurons PPC choice modality selectivity mixed selectivity PAIRS clustering population decoder weights random noise criteria pie charts averaging dimension variance movement decision inactivation DREADD visual auditory multisensory tolerance because single neurons are jointly (and randomly) modulated by choice and modality, downstream readouts can weight the same population differently to recover whichever variable is currently needed random distribution of parameters across neurons lets an arbitrary subset be linearly combined to estimate a needed parameter, removing the need for pre-patterned connections to downstream targets moving population activity into different dimensions at different moments lets PPC gate which signals reach which downstream areas PPC inactivation impairs visual choices by lowering the signal-to-noise of the incoming visual evidence, not by changing the animal's decision strategy Projection Angle Index of Response Similarity Variance Alignment choice divergence choice preference excess rate decision formation DREADD (Designer Receptor Exclusively Activated by Designer Drug) impairment ratio PPC inactivation control sessions Mann-Whitney U test non-parametric tests (Mann-Whitney U ) bootstrap Monte Carlo simulation Monte Carlo simulations ROC analysis / area under the curve the area under the ROC curve (AUC) principal component analysis principal component analysis (PCA) support vector machine classifier the linear Support Vector Machine (SVM) generalized linear model regression Generalized Linear Model regression (GLM regression sign test two-sided sign test kurtosis the kurtosis of the distribution of weights maximum likelihood estimation (psychometric function fitting) following the maximum likelihood methods described by Wichmann & Hill confidence interval Wilson binomial confidence interval bootstrap aggregation (bagging) a standard technique called bootstrap aggregation SVM p-values from Monte Carlo simulations electrophysiology

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

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1038/nn.3865. The reading view exists for the papers whose licence permits it.

Every tag on this page

alternatives 3 · claim_relations 11 · claim_type 1 · concept_edges 2 · content_tags 78 · contribution 1 · controls 7 · limitations 10 · mechanisms 7 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 27 · transfers 5

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studywhether multiple behaviors rely on distinct categories of neurons specialized to represent particular variables, or on a single population that is leveraged in different ways
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object_of_studythe paper's words
research_questionwhether neural populations are category-free - whether response features are distributed randomly across neurons rather than clustering into types
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research_questionthe paper's words
contributiontask parameters and temporal response features were distributed randomly across neurons, without evidence of categories, and the network explored different dimensions during decision and movement
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contributionthe paper's words
claim_typeexistence
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claim_typethe paper's words
phenomenonpopulation coding in posterior parietal cortex during multisensory decision-making
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phenomenonthe paper's words
methodelectrophysiology
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]the tagger's words
mechanismsmechanisms
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mechanisms[1]the tagger's words
mechanismsmechanisms
Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.
mechanisms[2]the tagger's words
mechanismsmechanisms
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mechanisms[3]the tagger's words
mechanismswhen parameters are distributed randomly across neurons, an arbitrary group of them can be linearly combined to estimate the parameter needed at a given moment, which obviates the need for precisely pre-patterned connections and means all information is transmitted
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mechanisms[4]1.0the tagger's words
mechanismsthe average angle between the choice decoder and the modality decoder was 86.5 degrees, ONLY SLIGHTLY LESS THAN A PERFECTLY ORTHOGONAL 90
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mechanisms[5]1.0the tagger's words
mechanismsand the neurons retained their choice preference whether the stimulus was auditory, visual or multisensory, which the paper calls tolerance
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mechanisms[6]1.0the tagger's words
limitationsCategories defined by cell type or connectivity rather than response features were not tested
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what this limits: the category-free claim, which is relative to the features tested: NEURAL CATEGORIES DEFINED BY OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION
limitations[0]the tagger's words
limitationsNo clustering test can cover every conceivable way the data might cluster
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what this limits: the strength of the clustering null: IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER, though the test is described as reasonably general
limitations[1]the tagger's words
limitationsThe choice/modality test leaves categories defined by other features unexamined
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what this limits: the same scope as limitations[0]: THIS LEAVES OPEN THE POSSIBILITY THAT THERE ARE CATEGORIES DEFINED BY OTHER FEATURES OF THE DATA
limitations[2]the tagger's words
limitationsDecoding was from a small sample of neurons in a noise-limited task
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what this limits: the decoding accuracy: DECODING WAS PERFORMED ON A SAMPLE OF TENS OF NEURONS OUT OF MANY THOUSANDS, ON A TASK WHERE THE ANIMAL'S PERFORMANCE IS LIKELY NOISE LIMITED
limitations[3]the tagger's words
limitationsResidual visual performance means the inactivation may have been incomplete or other structures may contribute
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what this limits: the inactivation result: ANIMALS RETAINED SOME ABILITY TO MAKE VISUAL JUDGMENTS DESPITE THE INACTIVATION, SUGGESTING EITHER THAT INACTIVATION WAS INCOMPLETE, OR THAT STRUCTURES IN ADDITION TO PPC SUPPORT THE TASK
limitations[4]the tagger's words
limitationsneural categories defined by other properties, such as cell type or connectivity, might reveal specialization
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what this limits: categories defined by selectivity to task parameters or by time dependent response features, which is what this test covers
limitations[5]1.0the tagger's words
limitationsPAIRS is not only a test for clustering but more generally for non-uniformity of the distribution of tuning, and it is probably not possible to detect all conceivable ways in which the data might cluster
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what this limits: the category-free conclusion, whose test is broader than its name: PAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY OF THE DISTRIBUTION OF TUNING ACROSS NEURONS
limitations[6]1.0the tagger's words
limitationsanimals retained some ability to make visual judgments despite the inactivation, suggesting either that inactivation was incomplete, or that structures in addition to PPC support the task
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what this limits: the same scope as limitations[4], with the effect size: SHALLOWER PSYCHOMETRIC FUNCTIONS ON INACTIVATION DAYS (sigma_saline = 2.64 +/- 0.39; sigma_muscimol = 4.67 +/- 0.67)
limitations[7]1.0the tagger's words
limitationsrelatively close neuron pairs were occasionally observed and differed from chance in 1 of 5 cases, so a small fraction of neurons do have pairs in feature space
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what this limits: the no-clustering claim, per animal: PAIRS INDEX FOR RATS 1-5: -0.011, 0.108, -0.038, 0.011, -0.007; p-values 0.621, 0.001, 0.209, 0.491, 0.857 - one of five deviates
limitations[8]1.0the tagger's words
limitationsauditory responses in PPC, though apparently not necessary for this task, may be invoked by other decision tasks
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what this limits: the negative inactivation result for audition: AUDITORY RESPONSES IN PPC, THOUGH APPARENTLY NOT NECESSARY FOR THIS TASK, MAY BE INVOKED BY OTHER DECISION TASKS
limitations[9]1.0the tagger's words
transfersa linear Support Vector Machine, explicitly labelled a standard machine-learning technique, is imported as the decoder for single-trial neural population activity
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transfers[0]the tagger's words
transferstolerance, and the reading that a linear decoder working under many conditions shows a representation has been untangled
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transfers[1]1.0the tagger's words
transfersa linear support vector machine used as the decoder, and bootstrap aggregation to combine a thousand trained classifiers
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transfers[2]0.9the tagger's words
transfersthe prediction that random mixtures let arbitrary groups of neurons be linearly combined, imported as the hypothesis to be tested rather than assumed
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transfers[3]1.0the tagger's words
transfersnearest-neighbor angles compared against randomly oriented vectors, turned into a clustering test for neural response profiles
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transfers[4]1.0the tagger's words
controlssynthetic categories introduced into the population, with noise derived from the real neural data
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eliminates: the lack of categories being a failure of the analysis to see structure - with faux neurons carrying exactly the same amount of noise as their originals, a strikingly different PAIRS distribution was evident · outcome (stated by the tagger, no quotation): held
controls[0]1.0the tagger's words
controlsthe distribution of decoder weights compared with random weightings
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eliminates: the decode resting on rare neurons with pure selectivity - the weights were nonzero for all neurons and did not differ from what would be expected by chance · outcome (stated by the tagger, no quotation): held
controls[1]1.0the tagger's words
controlsauditory decisions measured alongside the impaired visual ones
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eliminates: reduced motivation or confusion about the stimulus-response contingency, since the sparing of auditory decisions rules both out · outcome (stated by the tagger, no quotation): held
controls[2]1.0the tagger's words
controlsa second inactivation method that permits visualization of the agent to determine its spread
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eliminates: the impairment coming from spread into neighboring visual areas - and the rat with more expression posterior to PPC had WEAKER visual impairment · outcome (stated by the tagger, no quotation): held
controls[3]1.0the tagger's words
controlsthe Variance Alignment analysis repeated on visual against multisensory trials within the decision epoch
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eliminates: noise making two aligned state spaces look misaligned - this positive control gave strong alignment, which is the reassurance that the decision-movement misalignment is real · outcome (stated by the tagger, no quotation): held
controls[4]1.0the tagger's words
controlsmodulation during the stimulus compared against modulation during movement, neuron by neuron
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eliminates: the misalignment being two separate groups of active neurons - the two were strongly correlated, so it is a population-level phenomenon · outcome (stated by the tagger, no quotation): held
controls[5]1.0the tagger's words
controlsthe excess rate analysis over sliding windows of the stimulus
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eliminates: snap judgments or evidence leak as the explanation for the impairment - the analysis indicates inactivation reduced the signal-to-noise of incoming evidence instead · outcome: held
controls[6]1.0the tagger's words
sensesTHE TARGET IS THE CATEGORY ASSUMPTION ITSELF: individual neurons are OFTEN SEEN AS MEMBERS OF HIGHLY SPECIALIZED CATEGORIES, and THE ASSUMPTION OF NEURAL CATEGORIES IS REFLECTED IN MANY EXPERIMENTAL DESIGNS AND ANALYSIS METHODS, even far downstream of early sensory areas
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senses[0]1.0the tagger's words
sensesAND THE DEVICES THAT CARRY IT ARE NAMED. In sampling: neurons MUST MEET CERTAIN RESPONSE CRITERIA TO BE INCLUDED FOR STUDY, and IMPLICIT IN THIS APPROACH IS THE IDEA THAT THE CELL'S RESPONSE DURING ONE STIMULUS IDENTIFIES IT AS A MEMBER OF THE CATEGORY BEING EXAMINED
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senses[1]1.0the tagger's words
sensesin analysis: PIE CHARTS, A COMMON WAY OF SUMMARIZING POPULATION DATA, EXPLICITLY ASSIGN NEURONS TO CATEGORIES - and AVERAGING OVER MANY NEURONS LIKEWISE REFLECTS THE ASSUMPTION THAT EACH NEURON IS AN EXEMPLAR OF A CATEGORY, DIFFERENT FROM OTHER CATEGORY MEMBERS MAINLY BECAUSE OF NOISE
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senses[2]1.0the tagger's words
senseswith the alternative stated as a hypothesis about DISTRIBUTION rather than about types: neurons REFLECT RANDOM COMBINATIONS OF PARAMETERS, leading to populations in which NEURONS' RESPONSES DEFY CATEGORIZATION - and the advantage is that an ARBITRARY GROUP OF THEM CAN BE LINEARLY COMBINED to estimate the parameter needed at a given moment
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senses[3]1.0the tagger's words
sensesand the earlier test is described by what it could not cover: Quotation withheld A MORE GENERAL TEST IS THUS REQUIRED
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senses[4]1.0the tagger's words
sensesA NEW STATISTIC NAMED, DEFINED, AND BOUNDED BY ITS OWN AUTHORS: PAIRS, PROJECTION ANGLE INDEX OF RESPONSE SIMILARITY - 1 IF ALL NEURONS HAVE AT LEAST K IDENTICAL PARTNERS, AND 0 IF CLUSTERING IS ONLY AS STRONG AS EXPECTED BY CHANCE
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senses[5]1.0the tagger's words
sensesand its limits stated in the same paragraph that introduces it: PAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY of the distribution of tuning, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER
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senses[6]1.0the tagger's words
sensesA SIGNIFICANT P-VALUE READ AS SUPPORTING THE NULL: no evidence for clear categories in any animal, with p-values 0.236, 0.253, 0.399, 0.004 - and THE ONE SIGNIFICANT P-VALUE INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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senses[7]1.0the tagger's words
sensesTHE NULL VALIDATED BY MANUFACTURING WHAT WAS ABSENT: the lack of categories WAS NOT SIMPLY BECAUSE NEURAL VARIABILITY CAUSED OUR ANALYSIS TO MISS STRUCTURE - when SYNTHETIC CATEGORIES were introduced with noise derived from the real data, A STRIKINGLY DIFFERENT PAIRS DISTRIBUTION WAS EVIDENT
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senses[8]1.0the tagger's words
sensesand the manufacture is exact rather than approximate - the faux partners were made by RESAMPLING TRIALS WITH REPLACEMENT from real neurons, so THESE FAUX NEURONS HAD EXACTLY THE SAME AMOUNT OF NOISE AS THEIR ORIGINATING NEURONS
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senses[9]1.0the tagger's words
senseswith the exceptions counted rather than dismissed: relatively close neuron pairs were occasionally observed but DIFFERED FROM CHANCE IN ONLY 1 OF 5 CASES, and THE VAST MAJORITY OF NEURONS EACH REFLECT A UNIQUE COMBINATION OF RESPONSE FEATURES
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senses[10]1.0the tagger's words
sensesTHE SCOPE OF THE NULL, STATED BY THE AUTHORS: neural categories defined by OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION - so `PPC is category-free` holds for categories defined by task-parameter selectivity and by time-dependent response features, and for nothing else
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senses[11]1.0the tagger's words
sensesand the recommendation that follows is procedural, not factual: FUTURE STUDIES CAN TEST DIRECTLY FOR THE EXISTENCE OF CATEGORIES, AND DESIGN APPROPRIATE ANALYSES IF NEURONS ARE SHOWN TO REFLECT RANDOM COMBINATIONS of task parameters
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senses[12]1.0the tagger's words
sensesTHE SAMPLING CHOICE IS ITSELF THE ARGUMENT: WE RECORDED FROM ALL ENCOUNTERED NEURONS AND USED IDENTICAL STIMULI FOR EACH - the traditional approach of selection criteria and customized stimuli IS SUCCESSFUL IN IDENTIFYING NEURONS WITH STRONG CHOICE SIGNALS, BUT MAY LEAVE UNEXAMINED NEURONS WITH SUBTLER CHOICE SIGNALS THAT NONETHELESS SHAPE THE EVOLVING DECISION
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senses[13]1.0the tagger's words
sensesstated again as a difference from the primate literature - the stimulus WAS NOT OPTIMIZED FOR EACH NEURON'S PREFERENCE and neurons WERE NOT SELECTED BASED ON TUNING PROPERTIES
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senses[14]1.0the tagger's words
sensesMIXED SELECTIVITY MAKES TWO CONDITIONS INDISTINGUISHABLE IN ONE CELL: rare neurons had PURE choice or PURE modality selectivity, but for the majority THE MIXING SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS
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senses[15]1.0the tagger's words
sensesand the absence of correlation is explicitly refused as proof: LACK OF CORRELATION DOES NOT CONCLUSIVELY RULE OUT THE EXISTENCE OF FUNCTIONAL CATEGORIES - selectivity for task features could still define categories, if the points formed an X or clusters symmetric about the origin
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senses[16]1.0the tagger's words
senseswith every member of the population contributing: the neural weights WERE NONZERO FOR ALL NEURONS, and the distribution of weights DID NOT DIFFER FROM THOSE THAT WOULD BE EXPECTED BY CHANCE if neurons reflected random weightings
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senses[17]1.0the tagger's words
sensesAN INACTIVATION WHOSE SPARED CONDITION IS THE CONTROL: impairment was SPECIFIC TO VISUAL TRIALS with NO CONSISTENT EFFECT ON AUDITORY DECISIONS, and THE SPARING OF AUDITORY DECISIONS REVEALS THAT INACTIVATION DID NOT REDUCE THE ANIMAL'S MOTIVATION, OR INTRODUCE CONFUSION about the stimulus-response contingency
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senses[18]1.0the tagger's words
sensesand a confound answered by the wrong direction of its own effect: THE MORE POSTERIOR EXPRESSION WAS APPARENTLY NOT THE SOURCE OF THE IMPAIRMENT BECAUSE THE RAT WITH MORE EXPRESSION POSTERIOR TO PPC HAD WEAKER VISUAL IMPAIRMENT
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senses[19]1.0the tagger's words
senseswith the surprise stated rather than smoothed: our inactivation results WERE SURPRISING IN THAT AUDITORY DECISIONS WERE SPARED DESPITE A CLEAR SIGNATURE OF THE DEVELOPING CHOICE IN PPC NEURONS - a signal that is present, reliable, and not necessary
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senses[20]1.0the tagger's words
sensesTHE NETWORK EXPLORES DIFFERENT DIMENSIONS AT DIFFERENT MOMENTS: dimensions that CAPTURED CONSIDERABLE VARIANCE DURING THE MOVEMENT CAPTURED FAR LESS VARIANCE DURING DECISION FORMATION, arguing AGAINST ALIGNED STATE SPACES
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senses[21]1.0the tagger's words
sensesand the null is protected by a POSITIVE CONTROL - the same analysis on visual against multisensory trials revealed STRONG ALIGNMENT, which PROVIDES REASSURANCE THAT THE MISALIGNMENT of decision and movement INDICATES A REAL DIFFERENCE IN NEURAL COVARIANCE
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senses[22]1.0the tagger's words
sensesand it is not two groups of cells taking turns: modulation during the stimulus was STRONGLY CORRELATED with modulation during movement, so THE MISALIGNMENT IS A POPULATION-LEVEL PHENOMENON, NOT DUE TO HAVING SEPARATE GROUPS OF NEURONS
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senses[23]1.0the tagger's words
sensesvisible in single cells as a reversal - one neuron has an ELEVATED response before a leftwards choice and is THEN SUPPRESSED DURING THE LEFTWARDS MOVEMENT used to report that same choice
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senses[24]1.0the tagger's words
sensesA CONTROL ANIMAL BORROWED FROM ANOTHER STUDY, WITH ITS OWN NULL RECORDED: one animal had stimulating fibers implanted and laser stimulation on half its trials - THE ANIMAL EXPRESSED NO LIGHT-ACTIVATED ION CHANNELS IN ITS BRAIN, AND LASER STIMULATION HAD NO EFFECT ON NEURAL ACTIVITY OR BEHAVIOR
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senses[25]1.0the tagger's words
sensesand the structure that survives the randomness is named TOLERANCE - although individual neurons reflected RANDOM COMBINATIONS of task components, THE OBSERVED RESPONSES WERE NONETHELESS STRUCTURED, retaining their choice preference across modalities
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senses[26]1.0the tagger's words
alternativesclaim_type: null-result
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a second reading DIFFERING from the recorded value. Recorded: existence — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.claim_typethe tagger's words
alternativesmethod: a statistic on the angles between neurons' response vectors and their nearest neighbours, compared against randomly oriented vectors - PAIRS, Projection Angle Index of Response Similarity
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a second reading DIFFERING from the recorded value. Recorded: electrophysiology — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.methodthe tagger's words
alternativesphenomenon: mixed selectivity - individual neurons modulated by more than one task parameter at once, so that different conditions can give identical firing rates
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a second reading DIFFERING from the recorded value. Recorded: population coding in posterior parietal cortex during multisensory decision-making — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.phenomenonthe tagger's words
claim_relationscontradicts → the widespread assumption of functionally specialised neural categories (refs 1, 5-11, 32)
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claim_relations[0]the tagger's words
claim_relationsextends → prior work testing for categories defined by response timing (ref 19)
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claim_relations[1]the tagger's words
claim_relationsbuilds-on → theoretical work on random mixed representations (refs 12-14)
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claim_relations[2]the tagger's words
claim_relationsbuilds-on → the established rat multisensory rate-discrimination task (refs 20, 21)
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claim_relations[3]the tagger's words
claim_relationscritiques → the assumption of neural categories, which is built into how neurons are sampled and into how populations are summarized
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differs in: pie charts and averaging, which assign neurons to categories and treat each neuron as an exemplar differing from others mainly because of noise
claim_relations[4]1.0the tagger's words
claim_relationsextends → earlier work that argued against categories but tested only for categories defined by response sequence
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claim_relations[5]1.0the tagger's words
claim_relationssupports → theoretical work predicting that randomly distributed parameters let an arbitrary group of neurons be linearly combined for whatever estimate is needed
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claim_relations[6]1.0the tagger's words
claim_relationsdistinguishes-from → the primate approach of selecting neurons by tuning and optimising the stimulus per neuron, which finds strong choice signals and leaves subtler ones unexamined
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differs in: THE AXIS IS HOW THE NEURONS AND STIMULI WERE CHOSEN. 'Quotation withheld.' The older method is granted its successes and denied its denominator - it finds what it selects for, so a literature built on it cannot report what fraction of the population is involved.
claim_relations[7]1.0the tagger's words
claim_relationsqualifies → its own null, which covers categories defined by task-parameter selectivity and temporal response features and not those defined by cell type or connectivity
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differs in: THE AXIS IS WHICH PROPERTY DEFINES THE CATEGORY. 'Quotation withheld.' The paper's headline null - no categories - is bounded by the definition of category it used, IN THE SENTENCE AFTER THE CLAIM. Its [4] criticises pie charts and averaging for assigning neurons to categories; here it says which categories its own null covers.
claim_relations[8]1.0the tagger's words
claim_relationssupports → an analogy with inferotemporal cortex, where tolerance is read as evidence that representations have been untangled
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claim_relations[9]1.0the tagger's words
claim_relationspredicts → that future studies test directly for the existence of categories and design their analyses accordingly
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claim_relations[10]1.0the tagger's words
concept_edgesclustering —contrasted-with→ PAIRS
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strength of the relation: 1 · note: its limits stated where it is introduced - NOT ONLY A TEST FOR CLUSTERING BUT MORE GENERALLY FOR NON-UNIFORMITY, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says PAIRS also tests non-uniformity beyond clustering; contrasted-with is loose.
also in: clustering · PAIRS
concept_edges[8]1.0the tagger's words
concept_edgeschoice —distinct-from→ modality
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strength of the relation: 1 · note: MIXED SELECTIVITY MAKES TWO CONDITIONS INDISTINGUISHABLE IN ONE CELL - the mixing SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says pure choice or modality selectivity is rare and most neurons mix both; distinct-from is loose.
also in: choice · modality
concept_edges[16]1.0the tagger's words
content_tagsMann-Whitney U test
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content_tags.statistics[0]the tagger's words
content_tagsbootstrap
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also in: bootstrap
content_tags.statistics[1]the tagger's words
content_tagsMonte Carlo simulation
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content_tags.statistics[2]the tagger's words
content_tagsROC analysis / area under the curve
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content_tags.statistics[3]the tagger's words
content_tagsprincipal component analysis
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content_tags.statistics[4]the tagger's words
content_tagssupport vector machine classifier
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content_tags.statistics[5]the tagger's words
content_tagsgeneralized linear model regression
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content_tags.statistics[6]the tagger's words
content_tagssign test
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also in: sign test
content_tags.statistics[7]the tagger's words
content_tagskurtosis
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also in: kurtosis
content_tags.statistics[8]the tagger's words
content_tagsmaximum likelihood estimation (psychometric function fitting)
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content_tags.statistics[9]the tagger's words
content_tagsconfidence interval
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content_tags.statistics[10]the tagger's words
content_tagsbootstrap aggregation (bagging)
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content_tags.statistics[11]the tagger's words
content_tagsPAIRS
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also in: PAIRS
content_tags.statistics[12]1.0the tagger's words
content_tagsSVM
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also in: SVM
content_tags.statistics[13]1.0the tagger's words
content_tagsp-values from Monte Carlo simulations
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content_tags.statistics[14]1.0the tagger's words
content_tagsPPC INACTIVATION RELIABLY IMPAIRED VISUAL DECISIONS, by Mann-Whitney U test at p < 0.001 pooled across animals
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content_tags.findings[0]the tagger's words
content_tagsQuotation withheld
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content_tags.findings[1]the tagger's words
content_tagsMost PPC neurons carried joint information about both stimulus modality and the animal's choice rather than either one alone.
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content_tags.findings[2]the tagger's words
content_tagsThe nearest-neighbor angle distribution of neurons' feature vectors was statistically indistinguishable from that of randomly oriented vectors.
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content_tags.findings[3]the tagger's words
content_tagsA linear decoder read the animal's choice from single-trial population responses significantly above chance in every rat.
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content_tags.findings[4]the tagger's words
content_tagsThe same choice decoder did not estimate stimulus modality better than chance.
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content_tags.findings[5]the tagger's words
content_tagsQuotation withheld
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content_tags.findings[6]the tagger's words
content_tagsThe choice and modality decoder vectors were nearly orthogonal, meaning the two parameters occupy essentially independent dimensions of population activity.
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content_tags.findings[7]the tagger's words
content_tagsDimensions that captured most variance during movement captured little variance during decision formation, so the network occupies different covariance patterns in the two epochs.
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content_tags.findings[8]the tagger's words
content_tagsthis analysis, BY CONTRAST, REVEALED STRONG ALIGNMENT - index 0.840 at p < 10 to the minus 4, PRESENT IN ALL RATS TESTED
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content_tags.findings[9]the tagger's words
content_tagsQuotation withheld
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content_tags.findings[10]the tagger's words
content_tags80.1% of neurons, 218 of 272 units, HAD A MULTISENSORY RESPONSE THAT WAS BETTER PREDICTED BY THE AUDITORY AND VISUAL RESPONSES THAN BY THE MULTISENSORY MEAN, assessed on left-out data
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content_tags.findings[11]the tagger's words
content_tagsTHE ASSUMPTION OF NEURAL CATEGORIES IS REFLECTED IN MANY EXPERIMENTAL DESIGNS AND ANALYSIS METHODS, even those focusing on structures far downstream of early sensory areas
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content_tags.findings[12]1.0the tagger's words
content_tagsneurons MUST MEET CERTAIN RESPONSE CRITERIA TO BE INCLUDED FOR STUDY, and IMPLICIT IN THIS APPROACH IS THE IDEA THAT THE CELL'S RESPONSE DURING ONE STIMULUS IDENTIFIES IT AS A MEMBER OF THE CATEGORY BEING EXAMINED
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content_tags.findings[13]1.0the tagger's words
content_tagsPIE CHARTS, A COMMON WAY OF SUMMARIZING POPULATION DATA, EXPLICITLY ASSIGN NEURONS TO CATEGORIES, and AVERAGING OVER MANY NEURONS LIKEWISE REFLECTS THE ASSUMPTION THAT EACH NEURON IS AN EXEMPLAR OF A CATEGORY
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content_tags.findings[14]1.0the tagger's words
content_tagsearlier work ARGUED AGAINST CATEGORIES, BUT ONLY TESTED FOR CATEGORIES DEFINED BY RESPONSE SEQUENCE. A MORE GENERAL TEST IS THUS REQUIRED
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content_tags.findings[15]1.0the tagger's words
content_tagsPAIRS is 1 IF ALL NEURONS HAVE AT LEAST K IDENTICAL PARTNERS, AND 0 IF CLUSTERING IS ONLY AS STRONG AS EXPECTED BY CHANCE
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content_tags.findings[16]1.0the tagger's words
content_tagsPAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY of the distribution of tuning, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER
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content_tags.findings[17]1.0the tagger's words
content_tagsno evidence for clear categories was present in any animal, and THE ONE SIGNIFICANT P-VALUE INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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content_tags.findings[18]1.0the tagger's words
content_tagsthe lack of categories WAS NOT SIMPLY BECAUSE NEURAL VARIABILITY CAUSED OUR ANALYSIS TO MISS STRUCTURE - with SYNTHETIC CATEGORIES introduced, A STRIKINGLY DIFFERENT PAIRS DISTRIBUTION WAS EVIDENT
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content_tags.findings[19]1.0the tagger's words
content_tagsTHESE FAUX NEURONS HAD EXACTLY THE SAME AMOUNT OF NOISE AS THEIR ORIGINATING NEURONS
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content_tags.findings[20]1.0the tagger's words
content_tagsclose neuron pairs DIFFERED FROM CHANCE IN ONLY 1 OF 5 CASES, and THE VAST MAJORITY OF NEURONS EACH REFLECT A UNIQUE COMBINATION OF RESPONSE FEATURES
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content_tags.findings[21]1.0the tagger's words
content_tagsneural categories defined by OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION
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content_tags.findings[22]1.0the tagger's words
content_tagsWE RECORDED FROM ALL ENCOUNTERED NEURONS AND USED IDENTICAL STIMULI FOR EACH, where the traditional approach MAY LEAVE UNEXAMINED NEURONS WITH SUBTLER CHOICE SIGNALS THAT NONETHELESS SHAPE THE EVOLVING DECISION
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content_tags.findings[23]1.0the tagger's words
content_tagsthe mixing SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS
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content_tags.findings[24]1.0the tagger's words
content_tagsLACK OF CORRELATION DOES NOT CONCLUSIVELY RULE OUT THE EXISTENCE OF FUNCTIONAL CATEGORIES
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content_tags.findings[25]1.0the tagger's words
content_tagsthe neural weights were NONZERO FOR ALL NEURONS and did not differ from those EXPECTED BY CHANCE if neurons reflected random weightings
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content_tags.findings[26]1.0the tagger's words
content_tagsTHE SPARING OF AUDITORY DECISIONS REVEALS THAT INACTIVATION DID NOT REDUCE THE ANIMAL'S MOTIVATION, OR INTRODUCE CONFUSION about the stimulus-response contingency
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content_tags.findings[27]1.0the tagger's words
content_tagsTHE RAT WITH MORE EXPRESSION POSTERIOR TO PPC HAD WEAKER VISUAL IMPAIRMENT compared to the other rat
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content_tags.findings[28]1.0the tagger's words
content_tagsdimensions that CAPTURED CONSIDERABLE VARIANCE DURING THE MOVEMENT CAPTURED FAR LESS VARIANCE DURING DECISION FORMATION
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content_tags.findings[29]1.0the tagger's words
content_tagsthe positive control revealed STRONG ALIGNMENT, which PROVIDES REASSURANCE THAT THE MISALIGNMENT of decision and movement INDICATES A REAL DIFFERENCE IN NEURAL COVARIANCE
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content_tags.findings[30]1.0the tagger's words
content_tagsTHE MISALIGNMENT IS A POPULATION-LEVEL PHENOMENON, NOT DUE TO HAVING SEPARATE GROUPS OF NEURONS
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content_tags.findings[31]1.0the tagger's words
content_tagsTHE ANIMAL EXPRESSED NO LIGHT-ACTIVATED ION CHANNELS IN ITS BRAIN, AND LASER STIMULATION HAD NO EFFECT ON NEURAL ACTIVITY OR BEHAVIOR
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content_tags.findings[32]1.0the tagger's words
content_tagsProjection Angle Index of Response Similarity
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content_tags.definitions[0]the tagger's words
content_tagsVariance Alignment
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content_tags.definitions[1]the tagger's words
content_tagschoice divergence
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content_tags.definitions[2]the tagger's words
content_tagschoice preference
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content_tags.definitions[3]the tagger's words
content_tagsexcess rate
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also in: excess rate
content_tags.definitions[4]the tagger's words
content_tagsmixed selectivity
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content_tags.definitions[5]the tagger's words
content_tagsdecision formation
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content_tags.definitions[6]the tagger's words
content_tagsDREADD (Designer Receptor Exclusively Activated by Designer Drug)
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content_tags.definitions[7]the tagger's words
content_tagsimpairment ratio
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content_tags.definitions[8]the tagger's words
content_tagsPAIRS
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also in: PAIRS
content_tags.definitions[9]1.0the tagger's words
content_tagsclustering
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also in: clustering
content_tags.definitions[10]1.0the tagger's words
content_tagstolerance
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also in: tolerance
content_tags.definitions[11]1.0the tagger's words
content_tagscategories
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also in: categories
content_tags.definitions[12]0.9the tagger's words
content_tagsPPC inactivation
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content_tags.variables[0]the tagger's words
content_tagscontrol sessions
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content_tags.variables[1]the tagger's words
content_tagsselectivity
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also in: selectivity
content_tags.variables[7]1.0the tagger's words
content_tagsweights
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also in: weights
content_tags.variables[8]1.0the tagger's words
content_tagsvariance
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also in: variance
content_tags.variables[9]1.0the tagger's words
content_tags80.1% (218 of 272 units) — neurons whose multisensory response was better predicted by a linear combination of auditory and visual responses than by the multisensory mean
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content_tags.quantities[0]the tagger's words
content_tags68.2% — median share of multisensory response variance accounted for by a linear combination of the unisensory responses
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content_tags.quantities[1]the tagger's words
content_tags33.8% — proportion of neurons with statistically significant modality preference
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content_tags.quantities[2]the tagger's words
content_tags86.5° — average angle between the choice decoder and the modality decoder, near the orthogonal 90°
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content_tags.quantities[3]the tagger's words
content_tags68.9%, 61.2%, 59.4%, 80.2%, 70.1% — choice-decoding accuracy in the five rats at 700-800 ms after stimulus onset, outside the training window
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content_tags.quantities[4]the tagger's words
content_tagsPAIRS index = −0.052; p = 0.632 — clustering statistic for the example rat's feature vectors, indistinguishable from chance (no categories)
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content_tags.quantities[5]the tagger's words
content_tagsindex = 0.840, p < 10 −4 — Variance Alignment index for visual versus multisensory decision-epoch activity, the aligned positive control
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content_tags.quantities[6]the tagger's words
content_tagsr = 0.709, r = 0.655, r = 0.577, r = 0.666, r = 0.763 — per-rat correlation between each neuron's stimulus-epoch and movement-epoch modulation depth
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content_tags.quantities[7]the tagger's words
content_tags86.5° — the average angle between the choice decoder and the modality decoder, ONLY SLIGHTLY LESS THAN A PERFECTLY ORTHOGONAL 90
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content_tags.quantities[8]1.0the tagger's words
content_tags0.004 — the one significant PAIRS p-value, which INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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content_tags.quantities[9]1.0the tagger's words
content_tags1 of 5 cases — the number of animals in which close neuron pairs differed from chance at all
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content_tags.quantities[10]1.0the tagger's words
content_tags3.3 — the signal-to-noise criterion a neuron had to exceed to be included - firing-rate range divided by maximal s.e.m.
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content_tags.quantities[11]1.0the tagger's words

Interpretive choices

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© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824