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- Live5/17/2026, 4:35:28 PM
510a51a49bbcContent snapshot
{ "kind": "infographic", "prompt": "Different computational models predict substantially different gamma frequency ranges depending on the balance of ING vs PING mechanisms, brain region, and model complexity. This highlights that 'gamma' is not a single phenomenon but spans a wide frequency range (30-140 Hz) with distinct mechanistic regimes.", "provider": "other", "raw_fields": { "papers": [ { "n": 0, "doi": "10.1523/ENEURO.0452-25.2026", "value": "100-140", "method": "spiking network model constrained by experimental data", "metric": "ING gamma frequency range", "n_analyzed": null, "ci_or_error": null, "text_access": "fulltext", "n_definition": "network simulations", "scope_region": "medial entorhinal cortex", "study_system": "mouse mEC computational model", "taxonomic_level": "PV fast-spiking interneurons", "scope_population": "fast-spiking interneurons", "value_source_sentence": "Simulations revealed that weak excitatory input to interneurons supports fast ING-dominated rhythms (∼100–140 Hz).", "experimental_conditions": "weak excitatory input to interneurons" }, { "n": 0, "doi": "10.1523/ENEURO.0452-25.2026", "value": "60-100", "method": "spiking network model constrained by experimental data", "metric": "PING gamma frequency range", "n_analyzed": null, "ci_or_error": null, "text_access": "fulltext", "n_definition": "network simulations", "scope_region": "medial entorhinal cortex", "study_system": "mouse mEC computational model", "taxonomic_level": "PV fast-spiking interneurons and pyramidal cells", "scope_population": "E-I network", "value_source_sentence": "Simulations revealed that...strengthening excitatory drive induces a transition to slower PING-dominated oscillations (60–100 Hz).", "experimental_conditions": "strengthened excitatory drive to interneurons" }, { "n": 3000, "doi": "10.1371/journal.pcbi.1012259", "value": "40", "method": "morphology-constrained cortical network model", "metric": "gamma frequency in E-I network", "n_analyzed": null, "ci_or_error": null, "text_access": "fulltext", "n_definition": "neurons in network model", "scope_region": "motor cortex cross-sectional layer", "study_system": "mouse motor cortex model (WT)", "taxonomic_level": "PV fast-spiking and pyramidal neurons", "scope_population": "pyramidal and fast-spiking interneurons", "value_source_sentence": "Our findings reveal a dynamic interplay between pyramidal and fast-spiking interneurons leading to the emergence of gamma activity (∼40 Hz).", "experimental_conditions": "wild-type morphological parameters" }, { "n": 0, "doi": "10.1523/jneurosci.3041-11.2011", "value": "30-80", "method": "biophysically detailed compartmental model with asynchronous GABA release", "metric": "gamma oscillation frequency range", "n_analyzed": null, "ci_or_error": null, "text_access": "abstract_only", "n_definition": "cortical circuit model neurons", "scope_region": "cortex (generic)", "study_system": "biophysically detailed cortical circuit model", "taxonomic_level": "PV fast-spiking interneurons", "scope_population": "PV-pyramidal network", "value_source_sentence": "Parvalbumin (PV)-expressing, fast-spiking interneurons interacting with pyramidal neurons generate cortical gamma oscillations (30–80 Hz) that synchronize cortical activity during cognitive processing.", "experimental_conditions": "sensory stimulus-induced gamma" } ], "comparison_id": "gamma-frequency-across-models", "comparison_name": "Gamma Oscillation Frequency Ranges Across Computational PV Models", "comparison_type": "cross-study conflict", "what_it_reveals": "Different computational models predict substantially different gamma frequency ranges depending on the balance of ING vs PING mechanisms, brain region, and model complexity. This highlights that 'gamma' is not a single phenomenon but spans a wide frequency range (30-140 Hz) with distinct mechanistic regimes.", "homogeneity_check": { "caveats": [ "Different brain regions: mEC, motor cortex, generic cortex", "Different model complexity: from simplified spiking to biophysically detailed compartmental", "ING vs PING vs combined mechanisms produce different frequency bands in the same model", "3000-neuron model vs unspecified network sizes" ], "n_definition_uniform": "false", "scope_region_uniform": "false", "taxonomic_level_uniform": "true", "scope_population_uniform": "true" }, "suggested_plot_type": "grouped bar" }, "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", "source_refs": [ "paper:paper-80c3155dbabc", "paper:paper-8a7c68d828e6", "paper:paper-ba61b70940a1" ], "section_title": "Computational Models of PV Circuit Function", "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" }, "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" }