Electrical stimulation is an effective method for artificially modulating the activity of the nervous system. However, current stimulation paradigms fail to reproduce the stochastic and asynchronous properties of natural neural activity. In Formento and D'Anna et al., A biomimetic electrical stimulation strategy to induce asynchronous stochastic neural activity, Journal of Neural Engineering, 2020 (https://doi.org/10.1088/1741-2552/aba4fc), we introduced a novel biomimetic stimulation (BioS) strategy that overcomes these limitations. This repository contains the code of the neural simulations performed in the manuscript.
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Dependencies
- python 3.7
- numpy
- matplotlib
- neuron (--with-python)
- python 3.7
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Configuration
The folder /mod_files contains the NEURON AXNODE.mode file developed by C. McIntyre et al. 2002 modelling the membrane dynamics of the afferent fibers implemented here. This file needs to be compiled. For this purpose issue the following bash commands:
#!shell
cd BioS/ # make sure to be in the repo direcotry
nrnivmodl ./mod_files
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Running a simulation
The different simulations described in the associated paper can be executed by running the python3 run_* scripts. Refer to the comments inside each script to see the required arguments that need to be passed at launch time. For example to simulate the effect of BioS (as in Figure 2 of the manuscript) issue the following bash command:
#!shell
cd BioS/ # make sure to be in the repo direcotry
python3 run_afferent_stimulation.py --bios --stim-amp -0.027