Version history

{
  "scope": "balanced cortical network model for mouse/rat V1 L2/3; theoretical study",
  "claim_text": "A balanced-network model with completely random recurrent excitatory connectivity reproduces strong V1 orientation selectivity in mice and rats, demonstrating that feature-similarity-based wiring is not strictly required for selectivity in salt-and-pepper V1.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1523/jneurosci.6284-11.2012",
    "claim": "A balanced-network model with completely random recurrent excitatory connectivity reproduces strong V1 orientation selectivity in mice and rats, demonstrating that feature-similarity-based wiring is not strictly required for selectivity in salt-and-pepper V1.",
    "cite_key": "Hansel2012",
    "evidence": "Computational analysis of a balanced L2/3 network with random recurrent connectivity and random feedforward orientation preferences, applied to mouse/rat V1 architecture.",
    "effect_size": "Strong orientation tuning emerges in the balanced regime without like-to-like recurrence",
    "text_access": "abstract_only",
    "study_system": "balanced cortical network model for mouse/rat V1 L2/3; theoretical study",
    "argument_role": "supporting",
    "replication_status": "single_study",
    "claim_source_sentence": "Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime."
  },
  "section_id": "section_05",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_05_evidence_package.json",
  "effect_size": "Strong orientation tuning emerges in the balanced regime without like-to-like recurrence",
  "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-4ebfd9a19bd5"
  ],
  "source_span": "Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations.",
  "study_system": "balanced cortical network model for mouse/rat V1 L2/3; theoretical study",
  "evidence_refs": [
    {
      "ref": "paper:paper-4ebfd9a19bd5"
    }
  ],
  "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": "Computational analysis of a balanced L2/3 network with random recurrent connectivity and random feedforward orientation preferences, applied to mouse/rat V1 architecture.",
  "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"
}