3. Numerical integration of the HH model of the squid axon¶
Book chapters
See Chapter 2 Section 2 on general information about the Hodgkin-Huxley equations and models.
Python classes
The hodgkin_huxley.HH
module contains all code required for this exercise.
At the beginning of your exercise solutions, import the contained functions by running
from neurodynex.hodgkin_huxley.HH import *
You can then simply run the exercise functions by executing
HH_Step() # example Step-current injection
HH_Sinus() # example Sinus-current injection
HH_Ramp() # example Ramp-current injection
3.1. Exercise¶
Use the function HH_Step()
to simulate a HH
neuron stimulated by a current step of a given amplitude. The goal of
this exercise is to modify the provided python functions and use the
numpy
and matplotlib
packages to answer the following questions.
3.1.1. Question¶
What is the lowest step current amplitude for generating at least one spike? Hint: use binary search on I_amp
, with a \(0.1\mu A\) resolution.
3.1.2. Question¶
What is the lowest step current amplitude to generate repetitive firing?
3.2. Exercise¶
Use the function HH_Ramp()
to simulate a HH neuron stimulated by a ramping curent.
3.2.1. Question¶
What is the minimum current required to make a spike when the current is slowly increased (ramp current waveform) instead of being increased suddenly?
3.3. Exercise¶
To solve this exercise, you will need to change the actual implementation of the model. Download directly the source file HH.py. When starting Python in the direcory containing the downloaded file, you can import functions from it directly by the following:
from HH import *
Then use any text editor to make changes in the HH.py
file.
Hint: If you are using iPython, you will have to reload the module after making changes, by typing:
reload(HH)
3.3.1. Question¶
What is the current threshold for repetitive spiking if the density of sodium channels is increased by a factor of 1.5? To solve this, change the maximum conductance of sodium channel in HH_Neuron()
.