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

 Download zip file   Auto-launch 
Help downloading and running models
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]
Citations  Citation Browser
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: Dependence of the ephaptic interactions with distance"

MODELS: Bundle 3 and Nerve 2.

DATASET:
This dataset contains the code and the data for the subsection "Results: Dependence of the ephaptic interactions with distance".

 - Data: Bundle 3: fig9a.csv; Nerve 2: fig9b.csv.
 - Code: Bundle 3: "./fig9a/code/"; Nerve 2: "./fig9b/code/".

INSTRUCTIONS:

Running the simulations:
1. Run each simulation individually in each folder using "sim_launcher.py".

Data visualization:
2. Once the simulations are run and the outputs are available, run "fig9a.py", or "fig9b.py" for representations of figures 9a or 9b in the manuscript, respectively.
NOTE: There is no script to visualize data directly from the "fig*.csv" files; these are generated by "fig*.py".

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