Version history

{
  "scope": "SSN mathematical model with analytical solutions",
  "claim_text": "The SSN model can support bistable states, oscillatory activity, and persistent activity—not just sensory integration phenomena",
  "raw_fields": {
    "n": 0,
    "doi": "10.1073/pnas.1700080115",
    "claim": "The SSN model can support bistable states, oscillatory activity, and persistent activity—not just sensory integration phenomena",
    "evidence": "Analytical study and numerical simulations of SSN model",
    "effect_size": "SSN undergoes supercritical Hopf bifurcation generating global oscillations",
    "text_access": "fulltext",
    "study_system": "SSN mathematical model with analytical solutions",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "Here, we show that the stabilized supralinear network (SSN) model, which was originally proposed for sensory integration phenomena such as contrast invariance, normalization, and surround suppression, can give rise to dynamic cortical features of working memory, persistent activity, and rhythm generation.",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "We show that the SSN model can undergo a supercritical Hopf bifurcation and generate global oscillations."
  },
  "section_id": "section_12_evidence_package",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV/blob/df9fc7e8d455b084152c9d713558dae0013cef21/evidence/section_12_evidence_package.json",
  "effect_size": "SSN undergoes supercritical Hopf bifurcation generating global oscillations",
  "review_repo": "ComputationalReviewPV",
  "section_ref": "wiki_page:computationalreviewpv-12",
  "source_kind": "review_finding",
  "source_path": "evidence/section_12_evidence_package.json",
  "source_refs": [
    "paper:paper-5989bc007d71"
  ],
  "source_span": "Here, we show that the stabilized supralinear network (SSN) model, which was originally proposed for sensory integration phenomena such as contrast invariance, normalization, and surround suppression, can give rise to dynamic cortical features of working memory, persistent activity, and rhythm generation.",
  "study_system": "SSN mathematical model with analytical solutions",
  "evidence_refs": [
    {
      "ref": "paper:paper-5989bc007d71"
    }
  ],
  "section_title": "Computational Models of PV Circuit Function",
  "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": "df9fc7e8d455b084152c9d713558dae0013cef21",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV"
  },
  "evidence_summary": "Analytical study and numerical simulations of SSN model",
  "review_bundle_ref": "analysis_bundle:ab-e6261c8263e7",
  "replication_status": "replication_unknown",
  "review_package_ref": "analysis_bundle:ab-e6261c8263e7",
  "source_artifact_ref": "wiki_page:computationalreviewpv-12",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV/blob/df9fc7e8d455b084152c9d713558dae0013cef21/evidence/section_12_evidence_package.json",
  "commit_sha": "df9fc7e8d455b084152c9d713558dae0013cef21",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewPV"
}