Details

kind
infographic
provider
other
section_id
section_11
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_11_evidence_package.json
target_ref
wiki_page:computationalreviewrecurrence-11-pattern-completion
review_repo
ComputationalReviewRecurrence
section_ref
wiki_page:computationalreviewrecurrence-11-pattern-completion
source_path
evidence/section_11_evidence_package.json
section_title
11. Physiological signature III — pattern completion, replay, and sequence generation as recurrent-circuit read-outs in mouse cortex
generation_status
complete
review_bundle_ref
analysis_bundle:ab-d9c479db9be9
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_11_evidence_package.json
commit_sha
79ce062d54a924ce05953ec90aa9d26044d2b48f
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (4)
prompt
Small numbers (single-digit to low-tens) of mouse cortical pyramidal neurons are causally sufficient to drive perceptual decisions, with the most extreme number coming from V1 'pattern-completion' neurons whose activation recalls a larger ensemble.
raw_fields
{
  "papers": [
    {
      "n": null,
      "doi": "10.7554/eLife.58889",
      "value": "~14",
      "method": "2p holographic optogenetics + 2p Ca imaging",
      "metric": "minimum neurons sufficient for perceptual detection",
      "n_analyzed": null,
      "ci_or_error": "saturates at ~37",
      "text_access": "abstract_only",
      "n_definition": "L2/3 pyramidal neurons activated per ensemble",
      "scope_region": "primary somatosensory cortex (barrel)",
      "study_system": "awake mouse barrel cortex",
      "taxonomic_level": "broad excitatory cell category",
      "scope_population": "L2/3 pyramidal neurons",
      "value_source_sentence": "By precisely titrating the number of neurons stimulated, we demonstrate that the lower bound for perception of cortical activity is ~14 pyramidal neurons.",
      "experimental_conditions": "all-optical 2p activation + detection task"
    },
    {
      "n": null,
      "doi": "10.1016/j.cell.2019.05.045",
      "value": "2",
      "method": "2p holographic optogenetics + Ca imaging",
      "metric": "pattern-completion neurons sufficient to bias visual behavior",
      "n_analyzed": null,
      "ci_or_error": null,
      "text_access": "abstract_only",
      "n_definition": "pattern-completion neurons activated to recall an ensemble",
      "scope_region": "primary visual cortex",
      "study_system": "awake mouse V1",
      "taxonomic_level": "fine functional subtype",
      "scope_population": "pattern-completion neurons within ensembles",
      "value_source_sentence": "activation of only two pattern completion neurons from behaviorally relevant ensembles improved performance, by reliably recalling the whole ensemble.",
      "experimental_conditions": "visual go/no-go discrimination"
    }
  ],
  "audit_issues": [
    {
      "dimension": "scope_region",
      "description": "S1 barrel cortex (Dalgleish/eLife 58889) vs. V1 (Carrillo-Reid/Cell 2019).",
      "entries_affected": [
        "10.7554/eLife.58889",
        "10.1016/j.cell.2019.05.045"
      ]
    },
    {
      "dimension": "scope_population",
      "description": "Targeting differs: random L2/3 pyramidal neurons (Dalgleish) vs. pre-identified pattern-completion neurons within a behaviourally-relevant ensemble (Carrillo-Reid). Therefore the '~14 vs. 2' difference reflects target-selection sophistication, not a baseline biological threshold.",
      "entries_affected": [
        "10.7554/eLife.58889",
        "10.1016/j.cell.2019.05.045"
      ]
    },
    {
      "dimension": "metric_definition",
      "description": "Dalgleish's '~14' is the minimum count sufficient to drive detection; Carrillo-Reid's '2' is the number of pattern-completion neurons whose activation biases an existing visual discrimination — a different operational definition.",
      "entries_affected": [
        "10.7554/eLife.58889",
        "10.1016/j.cell.2019.05.045"
      ]
    }
  ],
  "audit_verdict": "CAVEAT",
  "comparison_id": "perceptual-threshold-cortical-activation",
  "comparison_name": "Minimum number of mouse cortical neurons whose holographic activation drives behavior",
  "comparison_type": "convergent evidence",
  "what_it_reveals": "Small numbers (single-digit to low-tens) of mouse cortical pyramidal neurons are causally sufficient to drive perceptual decisions, with the most extreme number coming from V1 'pattern-completion' neurons whose activation recalls a larger ensemble.",
  "homogeneity_check": {
    "caveats": [
      "Cell 2019 uses V1 'pattern-completion' neurons; eLife 2020 uses randomly targeted L2/3 pyramidal neurons in S1 — the metric is conceptually compared but not directly equivalent (selective vs random targeting).",
      "Behavioral tasks differ: visual go/no-go (Cell 2019) vs perceptual detection (eLife 2020)."
    ],
    "n_definition_uniform": "false",
    "scope_region_uniform": "false",
    "taxonomic_level_uniform": "false",
    "scope_population_uniform": "false"
  },
  "suggested_plot_type": "forest plot",
  "mandatory_caption_caveats": [
    "The ~14 vs. 2 difference is largely driven by neuron-selection strategy (random L2/3 vs. selected pattern-completion neurons), not by a difference in baseline cortical sensitivity.",
    "Tasks differ (perceptual detection vs. visual go/no-go discrimination); cortical areas differ (S1 vs. V1)."
  ]
}
source_refs
[
  "paper:paper-d2118dd5013a",
  "paper:paper-e37e0b6bad65"
]
source_policy
{
  "mode": "public_source_pointer_with_short_context",
  "notes": [
    "Local review repositories are read-only inputs.",
    "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
  ],
  "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}

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