Model of peripheral nerve with ephaptic coupling (Capllonch-Juan & Sepulveda 2020)

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Accession:263988
We built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues is modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation.
Reference:
1 . Capllonch-Juan M, Sepulveda F (2020) Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves. PLoS Comput Biol 16:e1007826 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Axon;
Brain Region(s)/Organism:
Cell Type(s): Myelinated neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Ephaptic coupling; Stimulus selectivity;
Implementer(s):
MANUSCRIPT: "Modelling the Effects of Ephaptic Coupling on Selectivity and Response Patterns during Artificial Stimulation of Peripheral Nerves", by Miguel Capllonch-Juan and Francisco Sepulveda (2020). PLOS Computational Biology.

SECTION: "Results: Effects of ephaptic coupling on propagation"

MODEL: Nerve 1

DATASET:
This dataset contains the code and the data for the subsection "Results: Effects of ephaptic coupling on propagation". Here are separate folders for EC and no-EC simulations.


 - Data (combined for both EC and no EC): recruitment_data.csv
 - Specific code and data for EC and no EC: "./ec/" and "./noec/", respectively.

INSTRUCTIONS:

Running the simulations:
1. Enter "./ec" and "./noec" and follow the instructions in the "README.md" in each folder to run the simulations.

Data processing:
2. Once this has been done both for EC and noEC, "cd .." and build "recruitment_data.csv" manually from their corresponding "recruitment_data.txt" files (i.e., "./ec/recruitment_data.txt", and "./noec/recruitment_data.txt"). Although the process can be programmed, this was done manually while processing the data for the manuscript.

Visualizing data:
3. Run "figs_3_5.py", or "fig4.py" for representations of figures 3 and 5, or 4 in the manuscript, respectively. "figs_3_5.py" generates Figs 3 and 5 in the manuscript, apart from other figures.

TROUBLESHOOTING
 - Any problems? Please search for your issue or open a new one on the software's GitHub repository: https://github.com/mcapllonch/SenseBackSim/issues