Version history

{
  "scope": "Functional connectomics reveals general wiring rule in mouse visual cortex.",
  "claim_text": "MICrONS V1 — functional-similarity (signal correlation) predicts E→E wiring more strongly than tuning-curve similarity.",
  "raw_fields": {
    "n": null,
    "doi": "10.1038/s41586-025-08840-3",
    "claim": "MICrONS V1 — functional-similarity (signal correlation) predicts E→E wiring more strongly than tuning-curve similarity.",
    "cite_key": "Ding2025",
    "evidence": "Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to",
    "effect_size": null,
    "text_access": "fulltext",
    "study_system": "Functional connectomics reveals general wiring rule in mouse visual cortex.",
    "argument_role": "supporting",
    "replication_status": null,
    "claim_source_sentence": "Signal correlations—the Pearson correlation between two neurons' responses to visual stimuli—provide a more general measure of functional similarity than orientation or direction tuning and have been shown to predict connectivity in V1 L2/3 better than orientation or direction tuning.",
    "source_provenance_status": "ok",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
  },
  "section_id": "section_03",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_03_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-03-paired-recording",
  "source_kind": "review_finding",
  "source_path": "evidence/section_03_evidence_package.json",
  "source_refs": [
    "paper:paper-7ac22819c082"
  ],
  "source_span": "Signal correlations—the Pearson correlation between two neurons' responses to visual stimuli—provide a more general measure of functional similarity than orientation or direction tuning and have been shown to predict connectivity in V1 L2/3 better than orientation or direction tuning.",
  "study_system": "Functional connectomics reveals general wiring rule in mouse visual cortex.",
  "evidence_refs": [
    {
      "ref": "paper:paper-7ac22819c082"
    }
  ],
  "section_title": "3. Paired-recording evidence in mouse — connection probabilities and synaptic strengths between pyramidal cells within a column, layer-by-layer (Lefort, Petersen, Adesnik, Feldmeyer, Markram-style work in mouse)",
  "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"
  },
  "evidence_summary": "Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "unevaluated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-03-paired-recording",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_03_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}