{
"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."
]
}