GitHub - ModelDBRepository/144499: Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)

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Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)

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README

StochasticHH_ObjectOriented.py
Author: Alan Leggitt
5-11-2012

This code is a re-implementation of Matlab code written by Joshua
Goldwyn. The original Matlab code can be found at

<a href="http://faculty.washington.edu/etsb/tutorials.html">http://faculty.washington.edu/etsb/tutorials.html</a>

This version runs best using Pylab from the Enthought Python
Distribution 7.2 For more information visit www.enthought.com

For more information, contact Alan Leggitt at alan.leggitt@ucsf.edu


Using the Code
--------------

To get started use the following commands in a Pylab terminal (see
<a href="http://enthought.com/products/epdgetstart.php">http://enthought.com/products/epdgetstart.php</a>):

run StochasticHH_ObjectOriented
n = Neuron()
n()

This will output two figures, one of voltage vs. time,
<img src="./screenshot1.jpg" alt="screenshot 1">

the other of Fraction of ion channels vs. time, similar to Fig 1B:
<img src="./screenshot2.jpg" alt="screenshot 2">

setting the second parameter in the time array to 30.01 (default
100.1) for a faster run and setting the model to Markov chain:

run StochasticHH_ObjectOriented
n=Neuron(timeArray=arange(0,30.01, 0.01), noiseModel="MarkovChain")
n()

we get a result similar to Fig 1A, although for expedience a current
injection is included here:
<img src="./screenshot3.jpg" alt="screenshot 3">

The Neuron class has some default values: a time array of 0 to 100 in
increments of 0.01 ms, an input current array with a 10 ms step
current, a noise standard deviation of 10, an area of 100 cm**2, and a
noise model (no noise). Any of these parameters can be changed, using
the following guidelines

Parameters
----------

timeArray - array of times
inputCurrent - an array of input current values, must be the same
               length as time
noiseSTD - the standard deviation of the noise
area - the area of the neuron
noiseModel - model for the input noise, must be a string
	ODE - no input noise
	Current - noise added to current input
	Subunit - noise added to subunit variables
	VClamp - voltage clamp conductance noise, Linaro et al model
	FoxLuSystemSize - system size conductance noise, Fox and Lu
                          model
	MarkovChain - Markov Chain model

For example...

run StochasticHH_ObjectOriented
t = arange(0,100,0.01)
n = Neuron(timeArray=t,inputCurrent=sin(t),noiseSTD=5,area=50,noiseModel='Current')
n()

Will generate the same two plots using the above parameters. 

Note: Any number of these parameters can be specified. Those that are
left unspecified will use the default values.


Functions
---------

The Neuron class has the following functions

solveStochasticModel - solve the Hodgkin Huxley model using the noise
                       model
plotVoltage - plot the voltage vs. time
plotChannelFractions - plot fractions of both ion channel types
                       vs. time

The Neuron class also has a call function installed, so that running
the following

run StochasticHH_ObjectOriented
n = Neuron()
n()

Will automatically call all three of the above functions. These
functions can also be called manually. For example...

run StochasticHH_ObjectOriented
n = Neuron()
n.solveStochasticModel()
n.plotVoltage()
n.plotChannelFractions()

Note: solveStochasticModel must be called before either plotting
function.


Plotting
--------

Each plotting function can also be called with an axis specified. For
example...

run StochasticHH_ObjectOriented
fig = figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
n = Neuron()
n.solveStochasticModel()
n.plotVoltage(ax=ax1)
n.plotChannelFractions(ax=ax2)

Will plot each output plot on a separate subplot. 

These plotting functions use default linestyles. The default for
voltage is a solid black line.
The default for fraction of Na Channels is a blue line and the default
for fraction of K Channels is a green line.
These linestyle parameters can also be changed, which allows for super
imposing different neuron plots. For example...

run StochasticHH_ObjectOriented
fig = figure()
ax = fig.add_subplot(111)
n1 = Neuron()
n2 = Neuron(noiseModel='Current')
n1.solveStochasticModel()
n2.solveStochasticModel()
n1.plotVoltage(ax=ax,lineStyle='k')
n2.plotVoltage(ax=ax,lineStyle='r')

20120515 python script updated with state variable permutation bug fix.
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