Computational Models of PV Circuit Function

Computational Models of PV Circuit Function

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• ^{1Citationpaper:paper-5135d667ceacThe preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference} The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference} The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference} The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}...

• ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference} The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference} The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference} The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}0 The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}1...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}2 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}3 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}4 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}5 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}6 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}7 The transition from minimal to detailed models has accelerated with large-scale morphological reconstruction efforts. Semi-automated strategies for developing multicompartment models incorporating realistic dendritic morphology, heterogeneous channel distributions, and synaptic integration properties have been applied to hippocampal interneuron subtypes, demonstrating that morphological detail substantially alters p...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}8 The dominant theoretical framework for understanding PV interneuron function at the circuit level is excitatory–inhibitory (E-I) balance theory and its extensions into the stabilized supralinear network (SSN) and inhibition-stabilized network (ISN) regimes. The SSN model demonstrates that supralinear input-output functions in individual neurons, combined with recurrent excitation and inhibition, are sufficient to ge...

• ^{2Citationpaper:paper-1cf664621a30The preceding sections established the biophysical, synaptic, and circuit properties of PV interneurons through experimental observation. Yet translating these observations into mechanistic understanding requires formal models that generate testable predictions, reveal emergent properties invisible to intuition, and identify which experimental parameters most critically determine circuit behavior. Computational mode...content/12_computational_models.md:line 4Open reference}9 The dominant theoretical framework for understanding PV interneuron function at the circuit level is excitatory–inhibitory (E-I) balance theory and its extensions into the stabilized supralinear network (SSN) and inhibition-stabilized network (ISN) regimes. The SSN model demonstrates that supralinear input-output functions in individual neurons, combined with recurrent excitation and inhibition, are sufficient to ge...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 The dominant theoretical framework for understanding PV interneuron function at the circuit level is excitatory–inhibitory (E-I) balance theory and its extensions into the stabilized supralinear network (SSN) and inhibition-stabilized network (ISN) regimes. The SSN model demonstrates that supralinear input-output functions in individual neurons, combined with recurrent excitation and inhibition, are sufficient to ge...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1 The dominant theoretical framework for understanding PV interneuron function at the circuit level is excitatory–inhibitory (E-I) balance theory and its extensions into the stabilized supralinear network (SSN) and inhibition-stabilized network (ISN) regimes. The SSN model demonstrates that supralinear input-output functions in individual neurons, combined with recurrent excitation and inhibition, are sufficient to ge...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2 The dominant theoretical framework for understanding PV interneuron function at the circuit level is excitatory–inhibitory (E-I) balance theory and its extensions into the stabilized supralinear network (SSN) and inhibition-stabilized network (ISN) regimes. The SSN model demonstrates that supralinear input-output functions in individual neurons, combined with recurrent excitation and inhibition, are sufficient to ge...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 The SSN/ISN framework generates several distinctive, testable predictions. Paradoxical responses — where activating inhibitory neurons leads to a net decrease in inhibitory firing — are a hallmark of ISN operation ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4. The dynamical regime of sensory cortex has been described as stable dynamics around a single stimulus-tuned attractor, where noise variability patterns are sha...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5 ISN detection: experimental paradigms versus model requirements (C43). Whether current experimental methods can reliably detect ISN operation remains contested. ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 reported widespread inhibition stabilization using optogenetic perturbation. However, ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7 demonstrated that detecting the ISN regime requires large-scale perturbation of the inhibitory population — reduced two-pop...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8 ISN detection: experimental paradigms versus model requirements (C43). Whether current experimental methods can reliably detect ISN operation remains contested. ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9 reported widespread inhibition stabilization using optogenetic perturbation. However, ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 demonstrated that detecting the ISN regime requires large-scale perturbation of the inhibitory population — reduced two-pop...

• ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1 Paradoxical responses: unique ISN signature or general network property? (C44). Paradoxical responses may not be unique to ISN: modeling work shows such responses can arise in non-ISN networks with moderate excitation ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2. If paradoxical responses are not diagnostic, the experimental evidence for widespread inhibition stabilization ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 requires reassessment.

• ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4 Paradoxical responses: unique ISN signature or general network property? (C44). Paradoxical responses may not be unique to ISN: modeling work shows such responses can arise in non-ISN networks with moderate excitation ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5. If paradoxical responses are not diagnostic, the experimental evidence for widespread inhibition stabilization ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 requires reassessment.

• ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7 Whether ISN operation is uniform across cortical layers or shows a laminar gradient remains unresolved. Multilayer cortical column modeling suggests L2/3 does not operate in the ISN regime while L4 and L5 do, producing a gradient of inhibition stabilization across cortical depth ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8 — contrasting with interpretations of ISN as a uniform cortical property ^{4Citationpaper:paper-28b3df6a6c8cThe computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9. Homeostatic...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 Whether ISN operation is uniform across cortical layers or shows a laminar gradient remains unresolved. Multilayer cortical column modeling suggests L2/3 does not operate in the ISN regime while L4 and L5 do, producing a gradient of inhibition stabilization across cortical depth ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1 — contrasting with interpretations of ISN as a uniform cortical property ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2. Homeostatic...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 Whether ISN operation is uniform across cortical layers or shows a laminar gradient remains unresolved. Multilayer cortical column modeling suggests L2/3 does not operate in the ISN regime while L4 and L5 do, producing a gradient of inhibition stabilization across cortical depth ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4 — contrasting with interpretations of ISN as a uniform cortical property ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5. Homeostatic...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 Whether ISN operation is uniform across cortical layers or shows a laminar gradient remains unresolved. Multilayer cortical column modeling suggests L2/3 does not operate in the ISN regime while L4 and L5 do, producing a gradient of inhibition stabilization across cortical depth ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7 — contrasting with interpretations of ISN as a uniform cortical property ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8. Homeostatic...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9 Whether ISN operation is uniform across cortical layers or shows a laminar gradient remains unresolved. Multilayer cortical column modeling suggests L2/3 does not operate in the ISN regime while L4 and L5 do, producing a gradient of inhibition stabilization across cortical depth ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 — contrasting with interpretations of ISN as a uniform cortical property ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1. Homeostatic...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2 The generation of gamma-frequency oscillations (30–100 Hz) represents perhaps the most intensively modeled aspect of PV circuit function. Two principal mechanisms have been formalized: interneuron network gamma (ING), driven by mutual inhibition among interconnected PV cells, and pyramidal-interneuron network gamma (PING), driven by reciprocal excitatory–inhibitory loops [Wang2010neurophysiological, Tiesinga2009cort...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 The generation of gamma-frequency oscillations (30–100 Hz) represents perhaps the most intensively modeled aspect of PV circuit function. Two principal mechanisms have been formalized: interneuron network gamma (ING), driven by mutual inhibition among interconnected PV cells, and pyramidal-interneuron network gamma (PING), driven by reciprocal excitatory–inhibitory loops [Wang2010neurophysiological, Tiesinga2009cort...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{5Citationpaper:paper-e022e0b961a0The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 In the ING mechanism, gamma frequency is determined primarily by IPSC decay time constants and PV-to-PV coupling strength. Fast-spiking interneurons autonomously generate fast gamma oscillations via ING, with excitation strength tuning ING-PING transitions ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1. The ING framework predicts that gamma frequency scales inversely with IPSC decay kinetics and requires strong I→I coupling for coherent oscill...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}2 ING and PING gamma mechanisms occupy distinct parameter spaces. (A) In the ING mechanism, oscillation frequency depends on IPSC decay time constant and I→I coupling strength; strong coupling generates fast gamma (>80 Hz). (B) In the PING mechanism, frequency depends on excitatory drive magnitude and E→I synaptic delay; frequencies cluster in the classical gamma range (30–80 Hz). (C) Feature comparison highlights...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}3 ING and PING gamma mechanisms occupy distinct parameter spaces. (A) In the ING mechanism, oscillation frequency depends on IPSC decay time constant and I→I coupling strength; strong coupling generates fast gamma (>80 Hz). (B) In the PING mechanism, frequency depends on excitatory drive magnitude and E→I synaptic delay; frequencies cluster in the classical gamma range (30–80 Hz). (C) Feature comparison highlights...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}4 ING and PING gamma mechanisms occupy distinct parameter spaces. (A) In the ING mechanism, oscillation frequency depends on IPSC decay time constant and I→I coupling strength; strong coupling generates fast gamma (>80 Hz). (B) In the PING mechanism, frequency depends on excitatory drive magnitude and E→I synaptic delay; frequencies cluster in the classical gamma range (30–80 Hz). (C) Feature comparison highlights...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}5 ING and PING gamma mechanisms occupy distinct parameter spaces. (A) In the ING mechanism, oscillation frequency depends on IPSC decay time constant and I→I coupling strength; strong coupling generates fast gamma (>80 Hz). (B) In the PING mechanism, frequency depends on excitatory drive magnitude and E→I synaptic delay; frequencies cluster in the classical gamma range (30–80 Hz). (C) Feature comparison highlights...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}6 PV-to-PV recurrent inhibition: essential for gamma or dispensable? (C45). ING models predict that mutual inhibition among PV cells is required for gamma oscillation, and ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}7 showed fast-spiking interneurons autonomously generate gamma via ING with I→I coupling as a critical parameter. However, ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}8 demonstrated that genetic ablation of synaptic inhibition onto PV interneurons in...

• ^{6Citationpaper:paper-15511df91dc7The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}9 PV-to-PV recurrent inhibition: essential for gamma or dispensable? (C45). ING models predict that mutual inhibition among PV cells is required for gamma oscillation, and ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}0 showed fast-spiking interneurons autonomously generate gamma via ING with I→I coupling as a critical parameter. However, ^{3Citationpaper:paper-b8f4cf874563The computational study of PV interneuron physiology begins with biophysically detailed single-cell models. The Wang–Buzsáki (WB) model, a two-variable conductance-based framework incorporating fast sodium and delayed-rectifier potassium currents, captured the essential fast-spiking phenotype of hippocampal interneurons and became the foundation for network-level gamma oscillation models [Wang2010neurophysiological]...content/12_computational_models.md:line 8Open reference}1 demonstrated that genetic ablation of synaptic inhibition onto PV interneurons in...

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