Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)

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Accession:127992
We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads. Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity. We focus on the discrete Markov process descriptions, routinely employed in experimental identification of voltage-gated channels and synaptic receptors.
Reference:
1 . Linaro D, Storace M, Giugliano M (2011) Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation PLOS 7:e1001102 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex U1 pyramidal intratelencephalic L2-5 cell; Neocortex U1 pyramidal pyramidal tract L5B cell;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; C or C++ program; Python;
Model Concept(s): Ion Channel Kinetics; Simplified Models; Methods;
Implementer(s): Linaro, Daniele [daniele.linaro at unige.it];
Search NeuronDB for information about:  Neocortex U1 pyramidal pyramidal tract L5B cell; Neocortex U1 pyramidal intratelencephalic L2-5 cell; I Na,t; I K;
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HHcn
C
matlab
mod-files
python
supplem
README.txt
                            
This directory contains files to reproduce results presented in the
paper:

Linaro, D., Storace, M., and Giugliano, M.  "Accurate and fast
simulation of channel noise in conductance-based model neurons by
diffusion approximation".

The subdirectories contain the following items.

1. C++ contains the source files for simulating the open-close
kinetics of sodium and potassium ion channels. The programs allow to
simulate the models discussed in the paper in the condition of voltage
clamp.

2. mod-files contains the mod-files that can be used in NEURON to
simulate the full models.

3. python contains a script, HHneuron.py, that allows to simulate the
models implemented through mod-files.

4. matlab contains some auxiliary Matlab scripts that can be used to
read data files and produce raster plots.

5. supplem contains the Matlab scripts employed to generate the
figures and analysis, included as Supplemental Material.

For any question, feel free to contact: daniele.linaro@unige.it,
marco.storace@unige.it, and michele.giugliano@ua.ac.be

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