Associative learning enhances population coding by inverting inter-neuronal correlation patterns

Jeanne, Sharpee, Gentner · Neuron · 2013 · neuroscience · 10.1016/j.neuron.2013.02.023 · record Jeanne2013-associative-learning-enhances-population-cod
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 'mechanism': the paper does not merely report that learning improves population coding but identifies the causal route — inversion of the signal/noise correlation relationship — and shows the improvement scales with population size ('This learning-dependent enhancement increases with population size.'; 'Associative learning inverts the relationship between signal correlation and noise correlation in pairs of CLM neurons.'). Four limitations are recorded because the paper is unusually explicit about them. I did NOT tag a contradicts relation to Gu et al. (2011), whose perceptual-learning result differs: the paper attributes the divergence to species, region, modality and learning type rather than asserting the prior finding is wrong, and the sentence stating the difference does not name them. The paper claims novelty — a negative signal/noise correlation relationship had not previously been demonstrated — so replication status is 'untested'. 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.
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Other fields define these differently the same word, defined another way in another discipline
cross-validation 3 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 12 concept tags on this paper, 1 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
noise correlation signal correlation population code task-relevant task-irrelevant classification ratio CLM motifs occasion setting multinomial logistic regression shuffled wide spiking when noise correlation runs opposite to tuning similarity, a downstream reader can subtract the responses of dissimilarly tuned neurons, cancelling the shared noise while adding the signal, so the same spikes carry more information learning-driven changes to feed-forward synaptic weights (with excitatory-inhibitory noise cancellation) decorrelate similarly tuned neurons and preserve or increase correlation among dissimilarly tuned ones, producing the inverted correlation structure the trigger for the correlation change is the learned stimulus-to-action mapping (the motif signalling which port to peck), not reward pairing, familiarity or exposure, which were equated across motif classes starling associative learning encoding fidelity operant urethane pooling trial-to-trial songbird motif inverted correlation structure wide spiking (WS) and narrow spiking (NS) neurons task-relevant motif task-irrelevant motif behavioral relevance number of neurons motif assignment analysis window task relevance of the motif population size Spearman correlation Spearman correlation coefficient Pearson correlation Pearson product-moment correlation coefficient ANCOVA ANCOVA motif class × regression slope interaction repeated measures ANOVA Friedman test Kruskal-Wallis test t-test Wilcoxon signed-rank test multinomial logistic regression (MNLR) cross-validation trial shuffling (permutation control) we shuffled the trial ordering of each neuron in each dataset Lilliefors normality test Lilliefors test Hartigan's dip test for bimodality Hartigan’s dip test principal components analysis principal components analysis on waveform shape standard error of the mean means ± standard errors of the mean multinomial logistic regression decoding trial shuffling two-way ANOVA interaction electrophysiology

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

The paper

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alternatives 3 · claim_relations 7 · claim_type 1 · concept_edges 5 · concepts 1 · content_tags 65 · contribution 1 · controls 1 · limitations 9 · mechanisms 8 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 4 · transfers 3

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 5 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studyhow associative learning changes the pattern of correlations between neurons, and what that does to population coding
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object_of_studythe paper's words
research_questionwhether learning acts on emergent population properties that cannot be observed in single neurons
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research_questionthe paper's words
contributionshows learning inverts the relationship between signal and noise correlations, but only for stimuli that guide behaviour, and that this improves population decoding increasingly with population size
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contributionthe paper's words
claim_typemechanism
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claim_typethe paper's words
phenomenoninter-neuronal noise correlations in population coding
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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
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mechanisms[2]the tagger's words
mechanismspositively correlated noise between similarly tuned neurons impairs coding because pooling cannot remove shared noise without removing signal
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mechanisms[3]1.0the tagger's words
mechanismspositively correlated noise between oppositely tuned neurons improves coding because subtraction removes noise and strengthens signal
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mechanisms[4]1.0the tagger's words
mechanismstrial shuffling separates the contribution of single-neuron plasticity from that of correlation change, since it leaves individual responses untouched
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mechanisms[5]1.0the tagger's words
mechanismsthe design equalises reward across all trained motifs so only the stimulus-response association differs
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mechanisms[6]1.0the tagger's words
mechanismstesting each motif in isolation confirms the behavioural asymmetry is about which response the motif signals, not about reward
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mechanisms[7]1.0the tagger's words
limitationsThe narrow-spiking (putative interneuron) sample was too small for reliable pair analysis, so the population results rest on wide-spiking pairs only.
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what this limits: every pairwise-correlation result: WE FOCUS ON WS NEURONS ... BECAUSE OUR SAMPLE OF NS NEURONS WAS NOT SUFFICIENT (N = 17 PAIRS FROM 36 SINGLE NEURONS) TO PERFORM RELIABLE ANALYSIS
limitations[0]the tagger's words
limitationsThe claim that correlation-structure plasticity is confined to putative excitatory neurons is underpowered.
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what this limits: the cell-type specificity claim specifically: THIS SUGGESTS THAT THE PLASTICITY ... PRIMARILY EXISTS WITHIN WS (PUTATIVE EXCITATORY) NEURONS, ALTHOUGH MORE DATA ARE NECESSARY - the negative half rests on the underpowered sample
limitations[1]the tagger's words
limitationsRecordings were made under urethane anaesthesia, unlike the awake comparison study, and the circuitry producing the stimulus-specific flexibility is not identified.
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what this limits: the comparison with the awake primate study: A METHODOLOGICAL DIFFERENCE BETWEEN OUR STUDY AND THAT OF (GU, ET. AL. 2011) IS THAT WE USED ANESTHETIZED ANIMALS WHILE GU AND COLLEAGUES USED AWAKE ANIMALS
limitations[2]the tagger's words
limitationsThe local or extrinsic circuit mechanism controlling the correlation structure remains to be established.
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what this limits: any circuit account of the effect: FURTHER EXPERIMENTS WILL BE NECESSARY TO ELUCIDATE THE CIRCUITRY THAT YIELDS THIS STIMULUS-SPECIFIC FLEXIBILITY
limitations[3]the tagger's words
limitationsnarrow-spiking neurons were excluded because the sample was too small for reliable analysis
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duplicate of: limitations[0] · what this limits: the same scope as limitations[0] - narrow-spiking neurons were excluded because the sample was too small for reliable analysis, so the population results are the broad-spiking ones
limitations[4]1.0the tagger's words
limitationsrecordings were made under urethane anaesthesia, unlike the study whose result this contradicts
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what this limits: the same scope as limitations[2], with the authors' position: WE THINK IT IS HIGHLY UNLIKELY THAT ANESTHESIA COULD ACCOUNT FOR THE DIFFERENCES BETWEEN OUR RESULTS FOR TWO REASONS
limitations[5]1.0the tagger's words
limitationsthe single-neuron firing rate difference is modest relative to the behavioural change it accompanies
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what this limits: any single-neuron reading of the learning effect: THE MODULATION OF SINGLE NEURON FIRING RATES IS SUBTLE ... ESPECIALLY IN LIGHT OF THE ANIMALS' ROBUST CHANGES IN BEHAVIORAL PERFORMANCE
limitations[6]1.0the tagger's words
limitationsthe effect in pairs of neurons is described as modest, and only grows large in bigger populations
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what this limits: the size of the correlation effect at the scale it was measured: A MODEST EFFECT IN PAIRS OF NEURONS GROWS CONSIDERABLY WITH POPULATION SIZE, and for irrelevant motifs the effect IS SMALL AND DOES NOT DEPEND ON POPULATION SIZE
limitations[7]1.0the tagger's words
limitationsthe comparison with the contradicting study differs in species, brain region, sensory modality and type of learning
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what this limits: the disagreement with the primate study, whose differing parameters are enumerated: THIS STUDY DIFFERED FROM OURS IN MULTIPLE WAYS (E.G. SPECIES, BRAIN REGION, AND SENSORY MODALITY) and type of learning
limitations[8]1.0the tagger's words
transfersthe concept of 'occasion setting' is imported to reinterpret the CLM motif representation as a predictive mapping onto the learned response rather than a sensory trace
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transfers[0]the tagger's words
transfersoccasion setting - the idea that a stimulus signals which response is appropriate rather than predicting reward itself - used to reinterpret what the cortical representation is for
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transfers[1]1.0the tagger's words
transfersmultinomial logistic regression, chosen because stimulus identity is categorical and cannot be parameterised
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transfers[2]1.0the tagger's words
controlsthe trial-shuffling repeated 50 times, with the AVERAGE classification probability used rather than a single shuffle
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eliminates: correlations introduced by the shuffle itself - one shuffle can create spurious structure, so a single shuffled baseline could be beaten or missed by chance · outcome (stated by the tagger, no quotation): held
controls[0]0.95the tagger's words
conceptsCLM
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also in: CLM
concepts[6]0.85the paper's words
sensesnoise correlation is shared trial-to-trial variability; signal correlation is tuning similarity - and the coding consequence of the first depends entirely on the sign of the second
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senses[0]1.0the tagger's words
sensestask-relevant means the motif signalled which response was correct; task-irrelevant motifs were equally familiar and equally rewarded, so relevance here is purely a stimulus-response association
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senses[1]1.0the tagger's words
sensesthe classification ratio is defined so that above one means correlations help and below one means they hurt, making the sign of the effect readable directly
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senses[2]1.0the tagger's words
sensesthe CLM representation is interpreted as a predictive mapping of the learned response rather than a sensory trace - occasion setting rather than encoding
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senses[3]1.0the tagger's words
alternativesclaim_type: mechanism
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a second reading reaching the same value. Recorded: mechanism — note: a second reading reaching the same value from a different sentence. Not applied - a person decides.
alternatives.claim_typethe tagger's words
alternativesmethod: two-alternative operant training on song motifs, then simultaneous recording of up to 8 neurons and multinomial logistic regression decoding with and without trial shuffling
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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: learning-dependent plasticity of inter-neuronal correlation structure
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a second reading DIFFERING from the recorded value. Recorded: inter-neuronal noise correlations in population coding — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.phenomenonthe tagger's words
claim_relationsbuilds-on → Oram et al. (1998)
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claim_relations[0]the tagger's words
claim_relationsreplicates → Cohen and Kohn (2011); Gu et al. (2011); Kohn and Smith (2005); Zohary et al. (1994)
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claim_relations[1]the tagger's words
claim_relationsreplicates → Jeanne et al. (2011); Gentner and Margoliash (2003); Thompson and Gentner (2010)
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claim_relations[2]the tagger's words
claim_relationscontradicts → the inference from earlier work that stimulus-reward association drives auditory cortical plasticity
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differs in: THE AXIS IS TASK RELEVANCE OF THE STIMULUS. 'ONLY THE TASK-RELEVANT motifs - those that birds learned to associate with a particular pecking location - elicited neural population activity with a NEGATIVE relationship between signal and noise correlations. In contrast, task-irrelevant motifs ... elicited neural population activity' [with the opposite sign]. The sign of the correlation relationship depends on learned relevance, so a claim about signal-noise correlations is only well-formed once the stimulus class is stated.
claim_relations[3]1.0the tagger's words
claim_relationsconfirms → the theoretical prediction that changing the sign of the signal-noise correlation relationship could underlie attention or learning
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claim_relations[4]1.0the tagger's words
claim_relationsqualifies → Gu et al., who found perceptual learning did not alter the slope of this relationship
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claim_relations[5]1.0the tagger's words
claim_relationssupports → theoretical work showing correlations help when signal and noise correlations have opposite sign
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claim_relations[6]1.0the tagger's words
concept_edgesnoise correlation —contrasted-with→ signal correlation
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differs in: THE AXIS DETERMINES THE SIGN OF THE EFFECT, NOT ITS SIZE. 'noise correlations can either enhance or impair coding fidelity, depending on how the noise correlation relates to tuning similarity (KNOWN AS SIGNAL CORRELATION) for each pair of neurons.' A claim that noise correlations help or hurt is not well-formed until this relation is stated - so two papers reporting opposite effects can both be right, and a join on 'noise correlation' alone would make them contradict. · strength of the relation: 1 · note: SIGN: correlated noise IMPAIRS coding between similarly tuned neurons and IMPROVES it between oppositely tuned ones. the same edge has opposite consequences depending on the second variable
concept_edges[0]1.0the tagger's words
concept_edgesEVIDENCE CORRECTED (RE-GROUNDED) · task-relevant —contrasted-with→ task-irrelevant
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EVIDENCE CORRECTED: RE-GROUNDED. The old span said the motifs were heard equally often. This sentence names both AND GIVES THE DIFFERENTIA - 'the primary DIFFERENCE ... was thus the learned association…' between motifs and behavioural choices - so the contrast carries what it consists in. — strength of the relation: 1 · note: matched on familiarity and reward by design, differing only in whether the motif signalled a response - which is what makes the comparison interpretable Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span names only 'task-relevant motifs'; the task-irrelevant motifs are implied by 'only'.
concept_edges[1]1.0the tagger's words
concept_edgesnoise correlation —causes→ population code
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strength of the relation: 0.9 · note: and the effect cannot be predicted from the average responses of individual neurons, which is the paper's motivating point Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the RELATIONSHIP between signal and noise correlations affects the fidelity of representations; causes is loose.
concept_edges[2]1.0the tagger's words
concept_edgespopulation code —operationalised-as→ classification ratio
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strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'encoding', not 'population code'.
concept_edges[3]1.0the tagger's words
concept_edgestask-relevant —contrasted-with→ task-irrelevant
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strength of the relation: 1 · note: RE-GROUNDS another row of this record: the old span named ONE endpoint.
concept_edges[5]1.0the tagger's words
content_tagsSpearman correlation
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content_tags.statistics[0]the tagger's words
content_tagsPearson correlation
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content_tags.statistics[1]the tagger's words
content_tagsANCOVA
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also in: ANCOVA
content_tags.statistics[2]the tagger's words
content_tagsrepeated measures ANOVA
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content_tags.statistics[3]the tagger's words
content_tagsFriedman test
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also in: Friedman test
content_tags.statistics[4]the tagger's words
content_tagsKruskal-Wallis test
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content_tags.statistics[5]the tagger's words
content_tagst-test
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also in: t-test
content_tags.statistics[6]the tagger's words
content_tagsWilcoxon signed-rank test
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content_tags.statistics[7]the tagger's words
content_tagsmultinomial logistic regression
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content_tags.statistics[8]the tagger's words
content_tagscross-validation
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content_tags.statistics[9]the tagger's words
content_tagstrial shuffling (permutation control)
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content_tags.statistics[10]the tagger's words
content_tagsLilliefors normality test
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content_tags.statistics[11]the tagger's words
content_tagsHartigan's dip test for bimodality
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content_tags.statistics[12]the tagger's words
content_tagsprincipal components analysis
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content_tags.statistics[13]the tagger's words
content_tagsstandard error of the mean
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content_tags.statistics[14]the tagger's words
content_tagsmultinomial logistic regression decoding
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content_tags.statistics[15]1.0the tagger's words
content_tagstrial shuffling
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content_tags.statistics[16]1.0the tagger's words
content_tagstwo-way ANOVA interaction
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content_tags.statistics[17]1.0the tagger's words
content_tagsFriedman test
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also in: Friedman test
content_tags.statistics[18]0.85the tagger's words
content_tagsFor motifs that had guided the birds' choices, the usual positive coupling between signal and noise correlation was inverted to a negative one.
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content_tags.findings[0]the tagger's words
content_tagsFor task-irrelevant and novel motifs the signal-noise correlation relationship stayed positive.
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content_tags.findings[1]the tagger's words
content_tagsMean noise correlation did not differ between motif classes.
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content_tags.findings[2]the tagger's words
content_tagsThe magnitude of signal correlation was likewise unaffected by task relevance.
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content_tags.findings[3]the tagger's words
content_tagsTask-relevant motifs showed a higher classification ratio than task-irrelevant or novel motifs.
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content_tags.findings[4]the tagger's words
content_tagsThe coding benefit conferred by correlations was flat with population size for irrelevant and novel motifs but grew steadily with population size for task-relevant motifs.
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content_tags.findings[5]the tagger's words
content_tagsFiring rates were modestly higher for task-relevant than task-irrelevant motifs, and similar for irrelevant and novel motifs.
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content_tags.findings[6]the tagger's words
content_tagsThe authors claim priority for observing a negative signal-noise correlation relationship in cortex.
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content_tags.findings[7]the tagger's words
content_tagslearning produces stimulus-specific changes in inter-neuronal correlations that enhance recognition of behaviourally relevant signals, and the enhancement grows with population size
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content_tags.findings[8]1.0the tagger's words
content_tagscorrelations improve encoding when signal and noise correlations have opposite sign and impair it when they share sign
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content_tags.findings[9]1.0the tagger's words
content_tagstask-relevant motifs showed a higher classification ratio than task-irrelevant or novel motifs
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content_tags.findings[10]1.0the tagger's words
content_tagsthe benefit of correlations grows with population size for task-relevant motifs and not for the others
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content_tags.findings[11]1.0the tagger's words
content_tagsenhanced coding of task-relevant motifs comes from both single-neuron response properties and correlations between neurons
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content_tags.findings[12]1.0the tagger's words
content_tagssingle-neuron firing rate differences were modest relative to the behavioural change
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content_tags.findings[13]1.0the tagger's words
content_tagstask-irrelevant motifs were indistinguishable from novel motifs in correlation structure despite being equally familiar and rewarded
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content_tags.findings[14]1.0the tagger's words
content_tagsa negative relationship between signal and noise correlations had not previously been demonstrated
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content_tags.findings[15]1.0the tagger's words
content_tagsnoise correlation
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content_tags.definitions[0]the tagger's words
content_tagssignal correlation
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content_tags.definitions[1]the tagger's words
content_tagsclassification ratio
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content_tags.definitions[3]the tagger's words
content_tagsmotif
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also in: motif
content_tags.definitions[4]the tagger's words
content_tagsinverted correlation structure
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content_tags.definitions[5]the tagger's words
content_tagswide spiking (WS) and narrow spiking (NS) neurons
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content_tags.definitions[6]the tagger's words
content_tagsclassification ratio
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content_tags.definitions[7]1.0the tagger's words
content_tagstask-relevant motif
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content_tags.definitions[9]the tagger's words
content_tagstask-irrelevant motif
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content_tags.definitions[10]the tagger's words
content_tagsbehavioral relevance
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content_tags.variables[0]the tagger's words
content_tagsnumber of neurons
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content_tags.variables[1]the tagger's words
content_tagsnoise correlation
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content_tags.variables[2]the tagger's words
content_tagssignal correlation
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content_tags.variables[3]the tagger's words
content_tagsclassification ratio
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content_tags.variables[4]the tagger's words
content_tagsmotif assignment
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content_tags.variables[5]the tagger's words
content_tagsanalysis window
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content_tags.variables[6]the tagger's words
content_tagstask relevance of the motif
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content_tags.variables[8]1.0the tagger's words
content_tagspopulation size
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content_tags.variables[9]1.0the tagger's words
content_tagsr = −0.15, p = 0.051 — correlation between signal correlation and noise correlation across neuron pairs for task-relevant motifs (negative)
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content_tags.quantities[0]the tagger's words
content_tagsr = 0.19 and r = 0.23 — the same signal-noise coupling for task-irrelevant and for novel motifs (positive)
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content_tags.quantities[1]the tagger's words
content_tagsp = 7.9×10 −5 — significance of the difference in regression slopes between motif classes
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content_tags.quantities[2]the tagger's words
content_tags0.082 / 0.100 / 0.087 — mean noise correlation for task-relevant, task-irrelevant and novel motifs - small and positive in every class
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content_tags.quantities[3]the tagger's words
content_tagsn = 176 pairs from 98 single neurons — the analysed sample of wide-spiking neuron pairs (narrow-spiking sample of 17 pairs was too small to analyse)
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content_tags.quantities[4]the tagger's words
content_tags51% vs 25% — decoding accuracy of the example two-neuron classifier against chance for four motifs
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content_tags.quantities[5]the tagger's words
content_tags5–6 neurons — population size at which the coding benefit of the learned correlation structure becomes substantial
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content_tags.quantities[6]the tagger's words
content_tagsNine — number of wild-caught European starlings trained on the two-alternative choice task
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content_tags.quantities[7]the tagger's words
content_tags176 pairs from 98 wide-spiking neurons, against 17 pairs from 36 narrow-spiking — the analysed sample and the excluded one
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content_tags.quantities[8]1.0the tagger's words
content_tagsEVIDENCE CORRECTED (RE-GROUNDED - THE POINTER WAS WRONG, NOT THE CLAIM) · 3.03 versus 2.74 Hz, p = 0.0080 — the modest single-neuron firing rate difference
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EVIDENCE CORRECTED: RE-GROUNDED - THE POINTER WAS WRONG, NOT THE CLAIM. The old evidence was a hedging remark - 'The modulation of single neuron firing rates is subtle' - carrying none of 3.03, 2.74 or 0.0080. The sentence that states the comparison is now the evidence.
content_tags.quantities[9]0.8the tagger's words
content_tagsp = 1.8e-8 for the interaction — the dependence of the coding effect on the sign relationship
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content_tags.quantities[10]1.0the tagger's words
content_tagssets of up to 8 neurons — the population sizes available for the scaling analysis
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content_tags.quantities[11]0.8the tagger's words

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