Version history

{
  "scope": "computational model trained on natural-movie statistics; compared to mouse V1 connectivity data",
  "claim_text": "A recurrent neural network trained to predict upcoming natural visual inputs spontaneously develops connectivity patterns matching V1 wiring biases, including like-to-like connections among excitatory neurons and stronger functional similarity among highly connected pairs.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1016/j.cub.2024.11.073",
    "claim": "A recurrent neural network trained to predict upcoming natural visual inputs spontaneously develops connectivity patterns matching V1 wiring biases, including like-to-like connections among excitatory neurons and stronger functional similarity among highly connected pairs.",
    "cite_key": "KlavinskisWhiting2025",
    "evidence": "Recurrent neural network trained to predict natural visual scenes; the resulting wiring is compared to experimentally measured V1 connectivity biases.",
    "effect_size": "Temporal-prediction model reproduces multiple known V1 wiring biases without explicit similarity rules",
    "text_access": "abstract_only",
    "study_system": "computational model trained on natural-movie statistics; compared to mouse V1 connectivity data",
    "argument_role": "supporting",
    "replication_status": "single_study",
    "claim_source_sentence": "This temporal prediction model reproduces the complex relationships between the connectivity of V1 neurons and their orientation and direction preferences, the tendency of highly connected neurons to respond more similarly to natural movies, and differences in the functional connectivity of excitatory and inhibitory V1 populations.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "This temporal prediction model reproduces the complex relationships between the connectivity of V1 neurons and their orientation and direction preferences, the tendency of highly connected neurons to respond more similarly to natural movies, and differences in the functional connectivity of excitatory and inhibitory V1 populations."
  },
  "section_id": "section_05",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json",
  "effect_size": "Temporal-prediction model reproduces multiple known V1 wiring biases without explicit similarity rules",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-05-horizontal",
  "source_kind": "review_finding",
  "source_path": "evidence/section_05_evidence_package.json",
  "source_refs": [
    "paper:paper-d66965754b34"
  ],
  "source_span": "This temporal prediction model reproduces the complex relationships between the connectivity of V1 neurons and their orientation and direction preferences, the tendency of highly connected neurons to respond more similarly to natural movies, and differences in the functional connectivity of excitatory and inhibitory V1 populations.",
  "study_system": "computational model trained on natural-movie statistics; compared to mouse V1 connectivity data",
  "evidence_refs": [
    {
      "ref": "paper:paper-d66965754b34"
    }
  ],
  "section_title": "5. Horizontal long-range intracortical excitatory connections in mouse — patchy L2/3-L5 axons, similarity tuning, distance-decay",
  "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": "Recurrent neural network trained to predict natural visual scenes; the resulting wiring is compared to experimentally measured V1 connectivity biases.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single_study",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-05-horizontal",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}