Imperial College London

Nick S Jones

Faculty of Natural SciencesDepartment of Mathematics

Professor of Mathematical Sciences



+44 (0)20 7594 1146nick.jones




301aSir Ernst Chain BuildingSouth Kensington Campus






BibTex format

author = {Lubba, CT and Le, Guen Y and Jarvis, S and Jones, N and Cork, S and Eftekhar, A and Schultz, S},
doi = {10.1007/s12021-018-9383-z},
journal = {Neuroinformatics},
pages = {63--81},
title = {PyPNS: multiscale simulation of a peripheral nerve in Python},
url = {},
volume = {17},
year = {2019}

RIS format (EndNote, RefMan)

AB - Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help.We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modeled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modeled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin- Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.
AU - Lubba,CT
AU - Le,Guen Y
AU - Jarvis,S
AU - Jones,N
AU - Cork,S
AU - Eftekhar,A
AU - Schultz,S
DO - 10.1007/s12021-018-9383-z
EP - 81
PY - 2019///
SN - 1539-2791
SP - 63
TI - PyPNS: multiscale simulation of a peripheral nerve in Python
T2 - Neuroinformatics
UR -
UR -
VL - 17
ER -