Version history

{
  "kind": "infographic",
  "prompt": "Whether single-area canonical bump-attractor dynamics are sufficient to describe primate PFC delay-period activity: Wimmer/Compte 2014 supports it via diffusion–behavior coupling; Spaak/Constantinidis 2021 finds dynamics more complex than canonical bump models over long delays; Murray/Bondy/Constantinidis 2017 supports a stable mnemonic subspace consistent with attractor coding.",
  "provider": "other",
  "raw_fields": {
    "papers": [
      {
        "n": 0,
        "doi": "10.1038/nn.3645",
        "value": "supports diffusing-bump description",
        "method": "single-unit recording + bump-attractor model fit",
        "metric": "support for canonical diffusing bump-attractor dynamics in PFC delay activity",
        "n_analyzed": "monkey single units (Funahashi-style oculomotor delayed-response)",
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "single units recorded from monkey dlPFC",
        "scope_region": "dorsolateral prefrontal cortex",
        "study_system": "rhesus monkey dlPFC, oculomotor delayed-response",
        "taxonomic_level": "subregion",
        "scope_population": "delay-tuned PFC units",
        "value_source_sentence": "Our results support a diffusing bump representation for spatial working memory instantiated in persistent prefrontal activity.",
        "experimental_conditions": "spatial working memory delay; model-derived trial-by-trial predictions"
      },
      {
        "n": 2,
        "doi": "10.1093/cercor/bhab079",
        "value": "more complex than canonical bump attractor (single-network) model",
        "method": "single-unit recording across long delays",
        "metric": "support for canonical bump-attractor description of long-delay PFC activity",
        "n_analyzed": "neurons across 2 macaques",
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "macaque subjects with multiple recorded units",
        "scope_region": "FEF and dlPFC",
        "study_system": "macaque FEF and dlPFC, long (5-15 s) delays",
        "taxonomic_level": "subregion",
        "scope_population": "memory-active single units",
        "value_source_sentence": "These dynamics are more complex than the dynamics of a canonical bump attractor network model (either decaying or nondecaying) but more constrained than the dynamics of fully heterogeneous memory models.",
        "experimental_conditions": "long-delay oculomotor delayed-response"
      },
      {
        "n": 0,
        "doi": "10.1073/pnas.1619449114",
        "value": "supports stable mnemonic subspace consistent with attractor coding",
        "method": "population state-space analysis",
        "metric": "support for stable attractor-like mnemonic subspace in PFC",
        "n_analyzed": "hundreds of single neurons across monkeys, two tasks",
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "single units recorded from monkey lateral PFC",
        "scope_region": "lateral prefrontal cortex",
        "study_system": "monkey lateral PFC, two parametric WM tasks",
        "taxonomic_level": "subregion",
        "scope_population": "all recorded PFC neurons",
        "value_source_sentence": "We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces.",
        "experimental_conditions": "oculomotor delayed-response and vibrotactile delayed discrimination"
      }
    ],
    "audit_issues": [
      {
        "dimension": "scope_region",
        "description": "Three macaque PFC recordings cover dlPFC (Wimmer 2014), FEF+dlPFC (Wimmer 2021), and lateral PFC (Murray 2017). Region scope is not identical.",
        "entries_affected": [
          "10.1038/nn.3645",
          "10.1093/cercor/bhab079",
          "10.1073/pnas.1619449114"
        ]
      },
      {
        "dimension": "metric_definition",
        "description": "All three rows report qualitative levels of support for attractor descriptions, not commensurable numerical scores.",
        "entries_affected": [
          "10.1038/nn.3645",
          "10.1093/cercor/bhab079",
          "10.1073/pnas.1619449114"
        ]
      }
    ],
    "audit_verdict": "CAVEAT",
    "comparison_id": "primate-pfc-bump-attractor-support",
    "comparison_name": "Cross-study evidence for canonical bump-attractor description of primate PFC persistent activity",
    "comparison_type": "cross-study conflict",
    "what_it_reveals": "Whether single-area canonical bump-attractor dynamics are sufficient to describe primate PFC delay-period activity: Wimmer/Compte 2014 supports it via diffusion–behavior coupling; Spaak/Constantinidis 2021 finds dynamics more complex than canonical bump models over long delays; Murray/Bondy/Constantinidis 2017 supports a stable mnemonic subspace consistent with attractor coding.",
    "homogeneity_check": {
      "caveats": [
        "All three studies are macaque PFC recordings during oculomotor-class WM tasks, but recording regions (dlPFC vs FEF+dlPFC vs lateral PFC), delay lengths, and analysis methods differ; values are qualitative claims rather than commensurable numbers."
      ],
      "n_definition_uniform": "false",
      "scope_region_uniform": "false",
      "taxonomic_level_uniform": "true",
      "scope_population_uniform": "true"
    },
    "suggested_plot_type": "forest plot",
    "mandatory_caption_caveats": [
      "Recording regions differ (dlPFC; FEF+dlPFC; lateral PFC); delay lengths and analysis pipelines differ; values are qualitative summaries, not numerical scores."
    ]
  },
  "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-a0b693ce262c",
    "paper:paper-e929cf9acfc8",
    "paper:paper-f7b4b82f9b06"
  ],
  "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"
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  "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"
}