The NEURON Simulation Environment

Hines, Michael L.; Carnevale, Nicholas T.

doi:10.1162/neco.1997.9.6.1179

Cited by 2,364 publications

(1,164 citation statements)

References 23 publications

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“…Simulation of Neuronal Signaling-Simulations of neuronal inputoutput relations were performed with Neuron (version 4.3.1) (31,32). This simulation environment calculates the voltage response of a cell (of given geometry) to current stimuli based on Hodgkin-Huxley-type voltage-gated conductances for Na ϩ and K ϩ (non-inactivating K ϩ currents) and standard values for leakage conductance and passive electrical properties including membrane capacitance and intracellular resistance.…”

Section: Methodsmentioning

confidence: 99%

“…Potential Significance of ID2 for Neuronal Signaling and for Kv1.4-mediated Short Term Plasticity-Because rapid N-type inactivation is a major determinant for efficiency and short term plasticity of synaptic transmission, we probed the significance of the docking domain by simulating the input-output relation of a neuron using Neuron, an environment for solving nerve equations (31,32). For this purpose, a spherical cell was equipped with a leakage K ϩ conductance, a standard voltagegated Na ϩ conductance, and a voltage-gated K ϩ conductance, which either reflected non-inactivating Kv1.4 WT or Kv1.4-(⌬40 -50) channels (for details see "Material and Methods").…”

Section: Structure-function Analysis Delineates Distinct Properties Amentioning

confidence: 99%

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Solution Structure and Function of the “Tandem Inactivation Domain” of the Neuronal A-type Potassium Channel Kv1.4

Wissmann

Bildl

Oliver

et al. 2003

Journal of Biological Chemistry

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Cumulative inactivation of voltage-gated (Kv) K؉ channels shapes the presynaptic action potential and determines timing and strength of synaptic transmission. Kv1.4 channels exhibit rapid "ball-and-chain"-type inactivation gating. Different from all other Kv␣ subunits, Kv1.4 harbors two inactivation domains at its N terminus. Here we report the solution structure and function of this "tandem inactivation domain" using NMR spectroscopy and patch clamp recordings. Inactivation domain 1 (ID1, residues 1-38) consists of a flexible N terminus anchored at a 5-turn helix, whereas ID2 (residues 40 -50) is a 2.5-turn helix made up of small hydrophobic amino acids. Functional analysis suggests that only ID1 may work as a pore-occluding ball domain, whereas ID2 most likely acts as a "docking domain" that attaches ID1 to the cytoplasmic face of the channel. Deletion of ID2 slows inactivation considerably and largely impairs cumulative inactivation. Together, the concerted action of ID1 and ID2 may promote rapid inactivation of Kv1.4 that is crucial for the channel function in short term plasticity.

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Section: Methodsmentioning

confidence: 99%

Section: Structure-function Analysis Delineates Distinct Properties Amentioning

confidence: 99%

Solution Structure and Function of the “Tandem Inactivation Domain” of the Neuronal A-type Potassium Channel Kv1.4

Wissmann

Bildl

Oliver

et al. 2003

Journal of Biological Chemistry

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“…The NEURON simulation environment (Hines and Carnevale, 1997) was used to model MLIs and their axonal synaptic inputs. Values for the biophysical parameters included in the model were taken from previously validated models (Pouzat and Marty, 1999;Mejia-Gervacio et al, 2007).…”

Section: Neuron Simulationsmentioning

confidence: 99%

“…To estimate the average autoR conductance corresponding to a single release site, we simulated autoRmediated synaptic inputs in MLIs using the NEURON platform (Hines and Carnevale, 1997). Model MLIs consisted of two parts: the somatodendritic compartment, modeled as a small cylinder with passive conductances (diameter, 10 µm; length, 10 µm), and the axon, modeled as a thin and long cylinder with both passive and active conductances (diameter, 0.5 µm; length, 200 µm).…”

Section: Gaba Release Sites Are Heterogeneously Distributed Along Thementioning

confidence: 99%

Impact of single-site axonal GABAergic synaptic events on cerebellar interneuron activity

Martín¹,

Jalil²,

Trigo³

2015

J Cell Biol

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“…Networks of fastspiking inhibitory interneurons were modeled using NEURON 5.1 (28), running on a Pentium PC under Linux. Neurons were represented as single compartments.…”

Section: Paired Recordings From Synaptically Connected Basket Cells (mentioning

confidence: 99%

Fast synaptic inhibition promotes synchronized gamma oscillations in hippocampal interneuron networks

Bartos

Vida

Frotscher

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

497

539

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Networks of GABAergic interneurons are of critical importance for the generation of gamma frequency oscillations in the brain. To examine the underlying synaptic mechanisms, we made paired recordings from ''basket cells'' (BCs) in different subfields of hippocampal slices, using transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of the parvalbumin promoter. Unitary inhibitory postsynaptic currents (IPSCs) showed large amplitude and fast time course with mean amplitudeweighted decay time constants of 2.5, 1.2, and 1.8 ms in the dentate gyrus, and the cornu ammonis area 3 (CA3) and 1 (CA1), respectively (33-34°C). The decay of unitary IPSCs at BC-BC synapses was significantly faster than that at BC-principal cell synapses, indicating target cell-specific differences in IPSC kinetics. In addition, electrical coupling was found in a subset of BC-BC pairs. To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20 -110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.G amma frequency oscillations are thought to be of key importance for higher brain functions, such as feature binding and temporal encoding of information (1-5). Experimental and theoretical evidence suggests that local networks of synaptically connected GABAergic interneurons are critically involved in the generation of these oscillations (6-19). First, perisomatic inhibitory interneurons (basket cells) fire action potentials at high frequency during gamma activity in vivo, with single spikes phase-locked to the oscillations of the field potential (6, 7). Second, pharmacologically isolated networks of inhibitory interneurons in vitro can oscillate at gamma frequency in response to metabotropic glutamate receptor activation (8). Finally, models of mutually connected interneurons generate coherent action potential activity in the gamma frequency range in the presence of a tonic excitatory drive (9-19).The mechanisms leading to the generation of coherent gamma oscillations in interneuron networks, however, have remained unclear. Although gamma frequency oscillations can be generated in interneuron network models, coherence is fragile against variation in amplitude and time course of the inhibitory postsynaptic conductance, against heterogeneity of the tonic excitatory drive, and against sparseness of connectivity (11-14). The mechanisms contributing to the control of network frequency are also poorly understood. It is thought that the time course of the inhibitory synaptic conductance change is a major factor (8-14), but the significance of other parameters remains undetermined. Some models suggest t...

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The NEURON Simulation Environment

Cited by 2,364 publications

References 23 publications

Solution Structure and Function of the “Tandem Inactivation Domain” of the Neuronal A-type Potassium Channel Kv1.4

Solution Structure and Function of the “Tandem Inactivation Domain” of the Neuronal A-type Potassium Channel Kv1.4

Impact of single-site axonal GABAergic synaptic events on cerebellar interneuron activity

Fast synaptic inhibition promotes synchronized gamma oscillations in hippocampal interneuron networks

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