NEURON Simulation package GEORGE MOUKARZEL OCT 12 TH

NEURON Simulation package GEORGE MOUKARZEL OCT 12 TH 2017

NEURON Simulation package Background Overview Simulation Conclusion

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

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

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

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

Overview NEURON advantages: 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

Overview NEURON mathematical basis: Hodgkin-Huxley model

Overview NEURON mathematical basis: Cable equation

Overview NEURON mathematical basis: Spatial discretization and segmentation

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

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

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)= e 1: e 2; 0<xmin<xmax<1 )

Simulation Starting the simulator: single neuron

Simulation Inserting membrane properties: Insert hh gnabar_hh: The maximum specific sodium channel conductance [Default value = 0. 120 S/cm 2] gkbar_hh: The maximum specific potassium channel conductance [Default value = 0. 036 S/cm 2] gl_hh: The maximum specific leakage conductance [Default value = 0. 0003 S/cm 2] ena: The reversal potential for the sodium channel [Default value = 50 m. V] ek: The reversal potential for the potassium channel [Default value = -77 m. V] el_hh: The reversal potential for the leakage channel [Default value = -54. 3 m. V] Insert pas

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

Simulation Outlook of the program:

Simulation Running the program:

Simulation Running the program:

Simulation Running the program:

Simulation A more sophisticated model: Creating the topology:

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

Simulation A more sophisticated model: Stimulating current:

Simulation Running the simulation:

Simulation Running the simulation:

Simulation User interface:

Simulation Other examples:

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: 3 D positioning Connecting several neurons together with Net. Con Model description: NMODL (more ion channels, Calcium pump, synapses, …)

References The NEURON Simulation Environment, M. L. Hines and N. T Carnevale https: //www. neuron. yale. edu/neuron/ http: //web. mit. edu/neuron_v 7. 4/nrntuthtml/index. html

Questions?