{
"papers": [
{
"n": null,
"doi": "10.64898/2026.01.30.702162",
"value": "3-7",
"method": "supervised latent model",
"metric": "latent variance captured (%)",
"n_analyzed": null,
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "animals (count not in abstract)",
"scope_region": "motor cortex",
"study_system": "motor cortex (2D reaching)",
"taxonomic_level": "broad category",
"scope_population": "all units",
"value_source_sentence": "Despite only capturing 3-7% of total variance and spanning two dimensions, this supervised method outperforms other common methods at an offline decoding task, and explains the long-term stability of neural manifolds observed in previous literature.",
"experimental_conditions": "chronic recordings"
},
{
"n": null,
"doi": "10.1016/j.celrep.2023.112574",
"value": ">10,000 neurons; 30,000,000 synapses",
"method": "biophysical simulation",
"metric": "model scale (neurons; synapses)",
"n_analyzed": null,
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "model neurons",
"scope_region": "M1",
"study_system": "mouse M1 model",
"taxonomic_level": "subcategory",
"scope_population": "all cortical cell types",
"value_source_sentence": "We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses.",
"experimental_conditions": "model"
},
{
"n": null,
"doi": "10.1152/jn.00061.2020",
"value": "14",
"method": "chronic two-photon imaging",
"metric": "days of recording at criticality",
"n_analyzed": null,
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "days",
"scope_region": "motor and premotor cortex",
"study_system": "mouse forelimb motor/premotor cortex",
"taxonomic_level": "subcategory",
"scope_population": "L2/3 and L5 pyramidal",
"value_source_sentence": "In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning.",
"experimental_conditions": "lever-press learning"
}
],
"audit_issues": [
{
"dimension": "metric_definition",
"description": "Row 1: latent variance captured (3–7%). Row 2: model scale (>10,000 neurons; 30,000,000 synapses). Row 3: number of days at criticality (14). These are three unrelated quantities and cannot be plotted on a shared y-axis as a 'grouped bar.'",
"entries_affected": [
"10.64898/2026.01.30.702162",
"10.1016/j.celrep.2023.112574",
"10.1152/jn.00061.2020"
]
},
{
"dimension": "study_system",
"description": "Row 1 is an analysis of recorded motor-cortex data; row 2 is a biophysical model; row 3 is chronic two-photon imaging of lever-press learning. The studies are about different aspects of motor cortex (decoding, simulation, plasticity timecourse).",
"entries_affected": [
"10.64898/2026.01.30.702162",
"10.1016/j.celrep.2023.112574",
"10.1152/jn.00061.2020"
]
}
],
"audit_verdict": "REDESIGN",
"comparison_id": "low_dim_motor_cortex",
"comparison_name": "Dimensionality and stability of motor-cortex latent manifolds",
"comparison_type": "convergent evidence",
"what_it_reveals": "Across mouse motor-cortex studies, latent dimensionality is small but stability and criticality regimes are layer- and method-dependent — bears on whether E→E recurrence enforces a single canonical low-dimensional manifold.",
"homogeneity_check": {
"caveats": [
"Mix of recording, modelling and reaching-task studies; metrics not directly commensurable"
],
"n_definition_uniform": "false",
"scope_region_uniform": "true",
"taxonomic_level_uniform": "false",
"scope_population_uniform": "false"
},
"suggested_plot_type": "grouped bar",
"mandatory_caption_caveats": [
"Rows report three unrelated quantities (% latent variance, model scale, days at criticality) and do not share a common metric.",
"Phase 7 writer: REDESIGN verdict — implement as: Either rebuild the figure around a single common metric (e.g., latent dimensionality of M1 across studies) by re-extracting from each paper, or split into three separate panels with distinct y-axes. As currently configured, the comparison is not meaningful."
]
}