Details

kind
infographic
provider
other
section_id
section_12_evidence_package
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewPV/blob/df9fc7e8d455b084152c9d713558dae0013cef21/evidence/section_12_evidence_package.json
target_ref
wiki_page:computationalreviewpv-12
review_repo
ComputationalReviewPV
section_ref
wiki_page:computationalreviewpv-12
source_path
evidence/section_12_evidence_package.json
section_title
Computational Models of PV Circuit Function
generation_status
complete
review_bundle_ref
analysis_bundle:ab-e6261c8263e7
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
Raw fields (4)
prompt
PV interneuron biophysics are optimized for high-frequency firing at near-theoretical energy efficiency. Changes to membrane properties (like PNN degradation increasing capacitance) have outsized effects on firing, but the models show capacitance alone cannot explain experimental observations, pointing to additional altered parameters.
raw_fields
{
  "papers": [
    {
      "n": 0,
      "doi": "10.1016/j.neuron.2018.02.024",
      "value": "1.6",
      "method": "axon recordings with biophysical modeling of Na+/K+ channel gating",
      "metric": "AP energy relative to theoretical minimum",
      "n_analyzed": null,
      "ci_or_error": null,
      "text_access": "fulltext",
      "n_definition": "axon recordings from PV+ basket cells",
      "scope_region": "hippocampus",
      "study_system": "PV+ basket cell axons, rat hippocampus",
      "taxonomic_level": "PV basket cells",
      "scope_population": "PV+ basket cell axons",
      "value_source_sentence": "Surprisingly, the energy required for the AP was, on average, only ∼1.6 times the theoretical minimum.",
      "experimental_conditions": "direct axon patch-clamp recordings"
    },
    {
      "n": 0,
      "doi": "10.1007/s10827-023-00849-9",
      "value": "25-50%",
      "method": "Hodgkin-Huxley to morphologically detailed models",
      "metric": "Capacitance increase from PNN degradation",
      "n_analyzed": null,
      "ci_or_error": null,
      "text_access": "fulltext",
      "n_definition": "computational models (HH to multi-compartment)",
      "scope_region": "neocortex (model)",
      "study_system": "single to multi-compartment PV neuron models",
      "taxonomic_level": "PV fast-spiking interneurons",
      "scope_population": "PV fast-spiking cells",
      "value_source_sentence": "Tewari et al. (2018) found that degradation of PNNs induced a 25%-50% increase in membrane capacitance cm and a reduction in the firing rates of PV-cells.",
      "experimental_conditions": "simulated PNN degradation via capacitance increase"
    }
  ],
  "comparison_id": "pv-energy-efficiency",
  "comparison_name": "Energy Efficiency of Fast-Spiking PV Interneuron Action Potentials",
  "comparison_type": "convergent evidence",
  "what_it_reveals": "PV interneuron biophysics are optimized for high-frequency firing at near-theoretical energy efficiency. Changes to membrane properties (like PNN degradation increasing capacitance) have outsized effects on firing, but the models show capacitance alone cannot explain experimental observations, pointing to additional altered parameters.",
  "homogeneity_check": {
    "caveats": [
      "Paper A is experimental (axon recordings) while Paper B is purely computational",
      "Different brain regions: hippocampus vs neocortex",
      "Different metrics: energy efficiency vs capacitance change - convergent on PV biophysics theme"
    ],
    "n_definition_uniform": "false",
    "scope_region_uniform": "false",
    "taxonomic_level_uniform": "true",
    "scope_population_uniform": "true"
  },
  "suggested_plot_type": "grouped bar"
}
source_refs
[
  "paper:paper-d6a5d21b353b",
  "paper:paper-e022e0b961a0"
]
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"
}

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