MICrONS general wiring rule for E→E: functional-similarity-dependent connection…

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  "n": null,
  "doi": "10.1038/s41586-025-08840-3",
  "claim": "MICrONS general wiring rule for E→E: functional-similarity-dependent connection probability and multiplicity in mouse V1/RL across L2–L5.",
  "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",
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  "study_system": "Functional connectomics reveals general wiring rule in mouse visual cortex.",
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  "claim_source_sentence": "In mouse V1 and RL, the MICrONS functional-connectome dataset reveals like-to-like effects: connection probability and synapse multiplicity scale with functional similarity (signal correlation, feature weight similarity, RF-center distance) between excitatory neurons across layers and areas.",
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In mouse V1 and RL, the MICrONS functional-connectome dataset reveals like-to-like effects: connection probability and synapse multiplicity scale with functional similarity (signal correlation, feature weight similarity, RF-center distance) between excitatory neurons across layers and areas.

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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