MICrONS-derived functional connectome — distance-dependent connection probabili…

{
  "n": null,
  "doi": "10.1038/s41586-025-08840-3",
  "claim": "MICrONS-derived functional connectome — distance-dependent connection probability and functional like-to-like wiring rule for mouse V1/RL E neurons.",
  "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": "Connection probability drops off with distance, so proofreading was focused on spatially clustered cells in two cylindrical columns spanning cortical layers 2–5 in V1 and RL; the analysis tested the relationship between connection probability and functional similarity metrics (signal correlation, feature weight similarity, RF center distance).",
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Connection probability drops off with distance, so proofreading was focused on spatially clustered cells in two cylindrical columns spanning cortical layers 2–5 in V1 and RL; the analysis tested the relationship between connection probability and functional similarity metrics (signal correlation, feature weight similarity, RF center distance).

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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