Version history

{
  "kind": "infographic",
  "prompt": "Two independent estimates of what fraction of cortical/hippocampal recurrent E→E connectivity participates in attractor-network structure: ~20% in hippocampal CA3a (Hopfield-style autoassociative interpretation) and ~20% in primate FEF / ~7.5% in primate dlPFC (bump-attractor interpretation). Both report values on the same scale (fraction of recurrent E→E connections) but from different anatomical/inferential methods.",
  "provider": "other",
  "raw_fields": {
    "papers": [
      {
        "n": 70000,
        "doi": "10.1101/lm.730207",
        "value": "0.2",
        "method": "Hopfield/Amit capacity framework applied to CA3a anatomy",
        "metric": "recurrent connection probability c among pyramidal cells (autoassociative network)",
        "n_analyzed": "70,000 neurons in CA3a (anatomy)",
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "neurons in CA3a subregion (anatomy-based count)",
        "scope_region": "hippocampus CA3a",
        "study_system": "rat CA3a recurrent network (Hopfield/autoassociative interpretation)",
        "taxonomic_level": "subregion",
        "scope_population": "pyramidal neurons",
        "value_source_sentence": "In applying this to CA3, we focus on CA3a, the subregion where recurrent connections are most numerous (c = 0.2) and approximate randomness.",
        "experimental_conditions": "anatomy-based estimate"
      },
      {
        "n": 2,
        "doi": "10.1371/journal.pcbi.1012867",
        "value": "0.20 (FEF) and 0.075 (dlPFC)",
        "method": "RNN training with varying bump-attractor connectivity fraction vs. recorded noise correlations",
        "metric": "fraction of recurrent E→E weights drawn from bump-attractor connectivity",
        "n_analyzed": "2 prefrontal regions across multiple monkeys",
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "prefrontal subregions modelled",
        "scope_region": "prefrontal cortex (FEF and dlPFC)",
        "study_system": "macaque FEF and dlPFC",
        "taxonomic_level": "subregion",
        "scope_population": "all PFC neurons in two regions",
        "value_source_sentence": "We found that models initialized with approximately 20% and 7.5% bump attractor connectivity closely matched the noise correlation properties of the frontal eye field and dorsolateral prefrontal cortex, respectively.",
        "experimental_conditions": "RNN model fitted to measured noise-correlation properties"
      }
    ],
    "audit_issues": [
      {
        "dimension": "study_system",
        "description": "Row 1: rat CA3a anatomical estimate of recurrent c=0.2 (Hopfield/autoassociative). Row 2: macaque FEF and dlPFC RNN-fitted bump-attractor connectivity fractions (0.20 / 0.075). Different species, different brain systems, and different methodologies.",
        "entries_affected": [
          "10.1101/lm.730207",
          "10.1371/journal.pcbi.1012867"
        ]
      },
      {
        "dimension": "metric_definition",
        "description": "Both quantities are 'fractions of recurrent E→E connectivity' but interpret different attractor families (autoassociative vs. continuous bump).",
        "entries_affected": [
          "10.1101/lm.730207",
          "10.1371/journal.pcbi.1012867"
        ]
      }
    ],
    "audit_verdict": "CAVEAT",
    "comparison_id": "recurrent-attractor-connectivity-fraction",
    "comparison_name": "Fraction of recurrent E→E connectivity associated with attractor-network structure",
    "comparison_type": "convergent evidence",
    "what_it_reveals": "Two independent estimates of what fraction of cortical/hippocampal recurrent E→E connectivity participates in attractor-network structure: ~20% in hippocampal CA3a (Hopfield-style autoassociative interpretation) and ~20% in primate FEF / ~7.5% in primate dlPFC (bump-attractor interpretation). Both report values on the same scale (fraction of recurrent E→E connections) but from different anatomical/inferential methods.",
    "homogeneity_check": {
      "caveats": [
        "Hippocampal CA3a (anatomy-based) vs. primate PFC (RNN-based inference from noise correlations) are different methodologies; the underlying attractor families (autoassociative Hopfield vs. continuous bump) differ; numeric values are commensurable as fractions of recurrent E→E connectivity but interpretation differs."
      ],
      "n_definition_uniform": "false",
      "scope_region_uniform": "false",
      "taxonomic_level_uniform": "true",
      "scope_population_uniform": "false"
    },
    "suggested_plot_type": "grouped bar",
    "mandatory_caption_caveats": [
      "Cross-species (rat vs. macaque) and cross-region (CA3a vs. PFC); attractor families compared are different (autoassociative Hopfield vs. continuous bump).",
      "Row 1 is an anatomy-based estimate; row 2 is an RNN-fit to noise-correlation properties. The numerical equivalence of '0.2' across rows is partly coincidental."
    ]
  },
  "section_id": "section_13",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "target_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-13-attractor-network-models",
  "source_path": "evidence/section_13_evidence_package.json",
  "source_refs": [
    "paper:paper-89ce835df6b0",
    "paper:paper-c491238def42"
  ],
  "section_title": "13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides",
  "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"
  },
  "generation_status": "complete",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}