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PyPNS is an open source Python package for simulating electrical phenomena in a peripheral nerve. Axons are modelled as chains of 1D compartments using NEURON (Hines and Carneval 1997). The surrounding medium is modelled as being electro-quasistatic and resistive, it relies on precomputed potential fields generated in finite element solvers (FEM). Currently the extracellular space can be homogeneous, nerve in saline or nerve in a cuff. PyPNS is useful to generate artificial recordings from spontaneously firing and/ or stimulated nerves and can further be used to investigate the efficiency of stimulation methods.

  • Axons are either myelinated (adapted from McIntyre et al. 2002) or unmyelinated (Hodgkin and Huxley 1952)
  • Axons are placed automatically with a variable degree of tortuosity with geometrical properties fit to imaged data 
  • For efficient computation of extracellular potentials (for both stimulation and recording) in the inhomogeneous and anisotropic medium surrounding the nerve, precomputed potential distributions can be imported from finite element solvers (FEM) once and then reused

 

Simplified geometry of a peripheral nerve used by PyPNS. A Nerve in saline. B Nerve in cuff. Single dots indicate unique current source positions for which a FEM simulation was run.

 

An experimental evoked compound action potential (ECAP) of a rat vagus nerve was approached reasonably well by the simulated ECAP obtained from PyPNS in a cuff medium. A Complete ECAP. B Only myelinated fibres. C Only unmyelinated C fibres.

 

PyPNS is being developed by Carl H Lubba at the Department of Bioengineering, Imperial College London.

PyPNS can be downloaded via the following link (GitHub): LINK to GitHub

 

More information on PyPNS can be found in the following paper:

Lubba, C. H., Guen, Y. Le, Jarvis, S., Jones, N. S., Cork, S. C., Eftekhar, A., & Schultz, S. R. (2018). PyPNS: Multiscale Simulation of a Peripheral Nerve in Python. Neuroinformatics. https://doi.org/10.1007/s12021-018-9383-z

References

Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerves. Journal of Physiology, 117, 500–544. https://doi.org/10.1016/S0092-8240(05)80004-7

McIntyre, C. C., Richardson, A. G., & Grill, W. M. (2002). Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle. Journal of Neurophysiology, 87(2), 995–1006. https://doi.org/10.1152/jn.00353.2001

Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerves. Journal of Physiology, 117, 500–544. https://doi.org/10.1016/S0092-8240(05)80004-7

 

Internal case number 8156

PyPNS

A Python Peripheral Nerve Simulator

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