1. NEURON Simulation package
George Moukarzel
Oct 12th 2017

2. NEURON Simulation package
Background
Overview
Simulation
Conclusion

3. Background NEURON: simulation environment software
Modeling individual or network of neurons
Models individual neurons automatically subdivided into individual compartments
Runs on MAC, Windows and UNIX
Written in C
Scripting language: HOC or Python

4. Background Simplifying Investigating Simulating Creation Analysis
Programming
Investigating
Cellular mechanisms
Network mechanisms
Individual cells
Network of neurons
Simulating

5. Background Michael Hines: Senior research scientist at Yale University
Member in Society for Neuroscience
Background: Physics and biophysics
Interest in neural simulation

6. Overview NEURON: handling problems in which:
Membrane properties are spatially inhomogeneous
Membrane currents are complex
Simulate equations describing nerve cells
Hodgkin-Huxley model
Cable equation
Spatial discretization

7. Overview NEURON advantages: NEURON weak points:
User deals directly with concepts that are familiar at neuroscience level
Contains functions that are tailored specifically for controlling the simulation and graphing the results of real neurophysiological problems
Efficient computational engine due to the use of special methods taking advantage of the structure of nerve equations
Efficient simulation speed
NEURON weak points:
Performance degrades very slowly with complexity of morphology and membrane mechanisms

8. Overview
NEURON mathematical basis:
Hodgkin-Huxley model

9. Overview
NEURON mathematical basis:
Cable equation

10. Overview NEURON mathematical basis:
Spatial discretization and segmentation

11. Overview Integration methods used in NEURON Backward Euler
Implicit
Crank-Nicoloson
Combination of forward Euler method and backward Euler method

12. Simulation Getting started:
Download for free from
Creating a hoc file:
Simple text editor (e.g. Notepad)
Write the desired code
Save as .hoc

13. Simulation Getting started: Establishing the model topology
create
connect
Assign properties
nseg: compartmentalization
L: length in μm
diam: diameter in μm
Ra: membrane resistance - default 35.4 Ω cm
Ranging variables ( rangevar(xmin:xmax)= e1:e2; 0<xmin<xmax<1 )

14. Simulation
Starting the simulator: single neuron

15. Simulation Inserting membrane properties: Insert hh Insert pas
gnabar_hh: The maximum specific sodium channel conductance [Default value = S/cm2]
gkbar_hh: The maximum specific potassium channel conductance [Default value = S/cm2]
gl_hh: The maximum specific leakage conductance [Default value = S/cm2]
ena: The reversal potential for the sodium channel [Default value = 50 mV]
ek: The reversal potential for the potassium channel [Default value = -77 mV]
el_hh: The reversal potential for the leakage channel [Default value = mV]
Insert pas

16. Simulation Adding point processes:
del: delay of onset of stimulus in ms
dur: duration of stimulus in ms
amp: amplitude of stimulus in nA

17. Simulation
Outlook of the program:

18. Simulation
Running the program:

19. Simulation
Running the program:

20. Simulation
Running the program:

21. Simulation
A more sophisticated model:
Creating the topology:

22. Simulation A more sophisticated model:
Specifying anatomical and physiological properties:

23. Simulation
A more sophisticated model:
Stimulating current:

24. Simulation
Running the simulation:

25. Simulation
Running the simulation:

26. Simulation
User interface:

27. Simulation
Other examples:

28. Conclusion NEURON: simulation software using hoc
Simplifies neural simulation by creating and accessing variables
No need to worry about default values and/or variable units
Easy to use
Use of blocks and for loops: avoid repetition
More applications:
3D positioning
Connecting several neurons together with NetCon
Model description: NMODL (more ion channels, Calcium pump, synapses, …)

29. References
The NEURON Simulation Environment, M.L. Hines and N.T Carnevale

30. Questions?