Versions:

• V 0.7 19.7.2013
• V 0.6 23.7.2012
• V 0.3 14.4.2010
• V 0.1 8.12.2009

NRNSIM or NeuRoN SIMulation is a Python tool that facilitates the execution of simulations with the Neuron environment (http://www.neuron.yale.edu). NRNSIM uses Neuron's Python interface to provide access to Neuron. NRNSIM however facilitates the simulation of neurons by providing easy to use Python objects. NRNSIM is designed to extend Neuron with Python easily. NRNSIM provides one example application and this is for the simulation of the extracellular stimulation of neurons. This application can be configured with semi-arbitrary descriptions of electric fields changing in space and time and it will configure Neuron in such a way, that the current neuron geometry will respond as if it was inside this field. The electric fields and the type of Neuron mechanism for representation of the field in Neuron can be chosen as a Python object. At the moment, stimulation with Neuron IClamps and the 'extracellular' mechanism can be configured.

NRNSIM is composed by a collection of Python scripts. In the current version these scripts are:

• nsrun.py: This is the initialization script. This should be executed for every simulation. In this version the script should be manually edited to load the respective 'application' scripts. This script should always call nrnsim.py.

• nrnsim.py: The main script. This contains the basic functions most 'application' script should call, such as those to initialize Neuron and to set the default time parameters. The main functions are:

  • settime(newdt,newtmax): Set time parameters in milliseconds.

  • nrngeom(name): Load the Neuron geometry given (.hoc file)

  • nrninit(): Prepare Neuron for simulation

  • updatemech(): Should be called if the Neuron mechanisms changed

  • sim(): initiates the Neuron simulation.

• section.py: Provides classes to wrap Neuron sections and section list providing a cleaner interface to obtain information about their parameters. The main functions are:

  • Section.points(): returns a pair of 3D points with the start and end of a neuron section.

  • Section.segments(): Retrieve all the pt3d defined segments in an array where each row is a point and a diameter.

  • Section.dx(): Calculate the spatial differential of the section using the Neuron parameters of length and number of segments.

  • Section.setpoints(p0,p1): Change the section initial and end coordinates.

  • Section.ra(): Obtain the section's axial resistance in Ohm/m.

  • Section.rm(): Obtain the section's membrane resistance in Ohm*cm.

  • Section.Ra(val): Set or get section's axial resistance in Ohm*cm.

  • Section.Rm(val): Set or get section's membrane resistance in Ohm*cm^2.

• smech.py: Implements the NEURON calls of different methods for extra and some intracellular stimulation. Supported methods as Python objects are:

  • ActivatingFunction: Implements the activation function [1] as a pair of stimulating IClamp objects in the two tips of each section. Current values are calculated as in [2,3].

  • ExtracellularMechanism: Uses Neuron's extracellular mechanism to set the extracellular potential for a set of sections. Each Neuron section is divided in segments. For every segment on every section, a potential value over time must be provided.

  • CurrentClamp: A simple to use wrapper for Neuron's IClamp mechanism.

• fields.py: Provides objects for signals and fields. A signal is a variable depending on time. A field is a 3d variable depending on time defined for a set of points. Classes provided are:

  • StepSignal: Given a time vector and a start and end generates a time series in a vector that resembles a step function.

  • BipolarSignal: A square bipolar function where a start, switching time and end time should be provided.

  • CosSignal: A cosine signal with and amplitude, period, start of the signal and end of the signal.

  • RLCSignal: Emulates the electric field induced by the inductor in a Resistor, Inductor, Capacitor circuit. It emulates the electric field produced by a transcranial magnetic stimulation coil.

  • Field: A general class to represent a field. Field objects can take as input a signal object to scale them over time.

  • HomogField: produces a spatially homogeneous field but variable in time field for a given time changing signal.

One example application script is provided with NRNSIM:

• multi.py: Simulates multiple types of 1D neural geometries, under geometrical transformations and multiple stimulation methods. The file multi.py can be used as a template for other applications. multi.py provides a graphical user interface to change the magnitude of and duration of the stimulation field. On execution of nsrun.py, parameters can be given in the command line to load an specific hoc file and an extra Python script. This script can modify the default amplitude, duration, orientation of the field and timesteps.

NRNSIM requires that Neuron is installed with Python support and that Neuron is visible to the Python interpreter, this is explained in http://www.neuron.yale.edu/neuron/download

For execution the regular Python interpreter can be used but IPython is recommended. To run the default multi.py application with IPython < 0.11 use the command:

ipython -pylab -i -c '%run -i nsrun.py file'

Where file is the root name of a set of files:

• file.hoc: a Neuron geometry. Cell parameters can also be set.
• file.ses: a Neuron session file.
• file.py: a configuration file with the stimulation parameters.
• file.cin.hoc: alternatively, a file with hoc's init() function. This can be used to set arbitrary initial potentials for different cells.

IPython 0.11 and superior changed the interaction of IPython with QT gui (which is required by the tool). To use IPython then using a separate kernel is recommended to do this call instead:

ipython console --pylab qt -i -c "%run -i nsrun.py file"

alternatively simple IPython can be used (no kernel mode) calling:

ipython -i -c "%run -i nsrun.py file"

but in this case IPython's plotting routines cannot be used.

[1] Frank Rattay. Analysis of models for external stimulation of axons. IEEE Transactions on Biomedical Engineering, 33(10):974-977, 1986.

[2] S. S. Nagarajan, D. M. Durand, and E. N. Warman. Efects of induced electric filds on finite neuronal structures: a simulation study. IEEE transactions on bio-medical engineering, 40(11):1175-1188, 1993.

[3] Assaf Rotem and Elisha Moses. Magnetic stimulation of one-dimensional neuronal cultures. Biophysical Journal, 94(12):5065-5078, 2008.

Copyright Andres Agudelo-Toro (https://sites.google.com/site/aagudelotoro/). All source code can be used for scientific purposes. The author holds no liability.