Higher-order wiring rule in mouse V1 E→E: shared-input convergence is more func…

{
  "n": null,
  "doi": "10.1038/s41586-025-08840-3",
  "claim": "Higher-order wiring rule in mouse V1 E→E: shared-input convergence is more functionally homogeneous than pairwise like-to-like predicts.",
  "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": "Postsynaptic neurons that received common synaptic inputs in the MICrONS dataset were even more similar than the like-to-like model predicted, suggesting that the V1 E→E connectivity organization includes higher-order structure beyond simple pairwise like-to-like rules.",
  "source_provenance_status": "ok",
  "replication_evidence_dois": [],
  "effect_size_source_sentence": null
}

[
  "paper:paper-7ac22819c082"
]

Postsynaptic neurons that received common synaptic inputs in the MICrONS dataset were even more similar than the like-to-like model predicted, suggesting that the V1 E→E connectivity organization includes higher-order structure beyond simple pairwise like-to-like rules.

[
  {
    "ref": "paper:paper-7ac22819c082"
  }
]

{
  "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"
}

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