Giant axon from the squid: Loligo pealei
We could implement this model in Python:
from neuron import h, gui
axon = h.Section(name='axon')
axon.L = 2e4
axon.diam = 100
axon.nseg = 43
axon.insert(h.hh)
But for this exercise, let's instead use the CellBuilder tool to create the model:
Save the model in hhaxon.ses
using :menuselection:`NEURONMainMenu --> File --> savesession`
If starting from a fresh launch of python, you can load the saved ses file by loading NEURON and its GUI: from neuron import h. gui
and then selecting :menuselection:`NEURONMainMenu --> File --> loadsession`
Alternatively you can use NEURON to execute hhaxon.ses
- Change to the appropriate directory in your terminal
- Start python, and at the >>> prompt enter the commands
from neuron import h, gui
h.load_file('hhaxon.ses')
Stimulate with current pulse and see a propagated action potential.
The basic tools you'll need from the :ref:`NEURON Main Menu <NEURONMainMenu>`
:menuselection:`Tools --> Point Processes --> Manager -->` :ref:`Point Manager <pointman>` to specify stimulation
:menuselection:`Graph -->` :ref:`Voltage axis <voltage_axis>` and :menuselection:`Graph -->` :ref:`Shape plot <shape_plot>` to create graphs of v vs t and v vs x.
:menuselection:`Tools -->` :ref:`RunControl <runctrl>` to run the simulation
:menuselection:`Tools --> Movie Run` to see a smooth evolution of the space plot in time
Change excitability by adjusting sodium channel density.
Tool needed: :menuselection:`Tools --> Distributed Mechanisms --> Viewers -->` :ref:`Shape Name <shapename>`
Use two current electrodes to stimulate both ends at the same time
Up to this point, the model has used a very fine spatial grid calculated from the Cell Builder's d_lambda rule
Change nseg to 15 and see what happens