Simulation of single-compartment Hodgkin-Huxley models for different
classes of cortical neurons

The models are described in:

   Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., 
   Bal, T., Fregnac, Y., Markram, H. and Destexhe, A.
   Minimal Hodgkin-Huxley type models for different classes of
   cortical and thalamic neurons.
   Biological Cybernetics 99: 427-441, 2008.

Intrinsic currents: INa, IKd for action potentials, IM for
spike-frequency adaptation, ICaL for high-threshold calcium current,
ICaT for the low-threshold calcium current, intracellular calcium

demo_PY_RS.oc : simplified model of the "regular-spiking" neuron
demo_IN_FS.oc : simplified model of the "fast-spiking" interneuron
demo_PY_IB.oc : simplified model of the "intrinsically bursting" neuron
demo_PY_IBR.oc : simplified model of the "repetive bursting" neuron
demo_PY_LTS.oc : simplified model of the "low-threshold spike" neurons


Simply auto-launch from ModelDB (after NEURON is installed) or
download and extract the archive.  Then under


cd to the extracted folder.  Type at the shell prompt:

nrngui mosinit.hoc


Drag and drop the extracted folder onto the mknrndll icon.  Drag and
drop the mosinit.hoc file onto the nrngui icon.


Run mknrndll and cd to the extracted folder. Select to make the
nrnmech.dll.  Use windows explorer to view the contents of the
expanded folder from the archive. Double click on the mosinit.hoc

Once the simulation is started:

Click on a button to select a cell.  Reposition the windows and press
the Init & Run button.

For example selecting Bursting Pyramidal Cell and then Init & Run
should produce a figure like figure 5B from the paper:

fig 5B appears here

20111102 The file IL_gutnick.mod was updated to use derivimplicit in the
solver.  See
for details.

20120216 the files IM_cortex.mod, IT_huguenard.mod, and
cadecay_destexhe.mod had solver methods updated from euler to cnexp,
cnexp, and derivimplicit respectively.  The IL_gutnick.mod was changed
from derivimplicit to cnexp. See phpBB link above.