Welcome to NeuRon Virtualizer (NRV)’s documentation!

NRV (or NeuRon Virtualizer) is a pythonic framework to enable fast and user-friendly simulations of the Peripheral Nervous System. Axons models are simulated with the NEURON software, and extracellular fields are computed either from analytic equations such as point source approximation or with a more detailed description of the nerve and electrode geometry and Finite Elements Method, either using COMSOL (additional commercial license required) or the FENICS project. All computations are performed with the quasi-static approximation of the Maxwell equations, no ephaptic coupling. Stimulation waveform can be of random shapes, and any kinds of electrode can be combined to model complex stimulation strategies.

NRV is optimized for large population of axons, from generating correct population following a specific diameter distribution, through automatic placement to computation and post-processing of the axons’ activity when a stimulus is applied. Parallel computation, interface with NEURON and COMSOL and FENICS is automatically handled by NRV.

NRV was developed by contributors from the CELL research group at the Laboratory ETIS (UMR CNRS 8051), ENSEA - CY Cergy Paris University, until june 2023 and is now developed and maintained by the Bioelectronics group of laboratory IMS (UMR CNRS 5218), INP Bordeaux, U. Bordeaux.

Contents:

• Introduction to NRV
• Installation
  • Dependencies
  • Installing NRV
• Scientific foundations
  • Framework overview
  • fmod-section: computation of 3D extracellular potentials
  • nmod section: generating and simulating axons
  • Optimizing a setup
  • References
• Tutorials
  • Tutorial 1 - First steps into NRV: a simple axon
  • Tutorial 2 - Evaluation of propagation velocity with NRV
  • Tutorial 3 - Stimulating single fibers with NRV
  • Tutorial 4 - Stimulating nerves with NRV
  • Tutorial 5 - First optimization problem using NRV
• User’s Guide
  • Chapters of the User’s Guide
  • Note on object saving
  • Note on object instantiation
• Examples
  • Example 1: Propagation of spike along the axon
  • Example 2: Activation function
  • Example 3: Activation function for a LIFE
  • Example 4: Conductance model dynamic with stimulation
  • Example 5: Conduction block with kHz stimulation
  • Example 6: Generate a fascicle with all contexts
  • Example 7: DC Propagation block
  • Example 8: KES propagation Block of unmyelinated fiber
  • Example 9: Prepulse waveform stimulation
  • Example 10: slowly rising pulse stimulation
  • Example 11: combining stimulus in NRV
  • Example 12: Plotting myelinated fibers structural parameters
  • Example 13: Plotting available axon diameter distributions in NRV
  • Example 14: Activation function with a cuff-like electrode
  • Example 15: Activation thresholds with LIFE and cuff-like electrodes
  • Example O-01: Optimization Pulse Stimulus on Nerve
  • Example O-02: understanding context modifiers: stimulus_CM
  • Example O-03: understanding context modifiers: biphasic_stimulus_CM
  • Example O-04: understanding context modifiers: biphasic_stimulus_CM
  • Example O-05: Ploting Optimization - context modifiers
• API documentation
  • nrv Framework
• Version updates
  • [1.1.1] - XXXX
  • [1.1.0] - 2024-05-27
  • [1.0.1] - 2024-01-26
  • [1.0.0] - 2024-01-12
  • [0.9.16] - 2023-12-12
• Developer’s corner
  • Contribution - forking and Pull-Requests
  • NRV testing
  • Public roadmap

Indices and tables

• Index

• Module Index

• Search Page