Version history

{
  "scope": "Functional connectomics reveals general wiring rule in mouse visual cortex.",
  "claim_text": "MICrONS — feature-preference like-to-like at synapse scale; RF-center distance only matters at axonal trajectory scale.",
  "raw_fields": {
    "n": null,
    "doi": "10.1038/s41586-025-08840-3",
    "claim": "MICrONS — feature-preference like-to-like at synapse scale; RF-center distance only matters at axonal trajectory scale.",
    "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": "At the synaptic scale, only like-to-like feature preference (not smaller spatial RF centre distance) is associated with increased synaptic connectivity in mouse V1; like-to-like effects for signal correlation and feature weight similarity were widespread across many area and layer combinations at both axonal and synaptic scales.",
    "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": "At the synaptic scale, only like-to-like feature preference (not smaller spatial RF centre distance) is associated with increased synaptic connectivity in mouse V1; like-to-like effects for signal correlation and feature weight similarity were widespread across many area and layer combinations at both axonal and synaptic scales.",
  "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"
}