CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)

 Download zip file   Auto-launch 
Help downloading and running models
Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994).
1 . Pinsky PF, Rinzel J (1994) Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J Comput Neurosci 1:39-60 [PubMed]
2 . Pinsky PF, Rinzel J (1995) Erratum for Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J Comput Neurosci 2:275-275
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s): I Na,t; I L high threshold; I K,Ca;
Gap Junctions:
Simulation Environment: NEURON; XPPAUT;
Model Concept(s): Active Dendrites;
Implementer(s): Lytton, William [bill.lytton at];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; I Na,t; I L high threshold; I K,Ca;
% $Id: README,v 1.5 2004/02/10 15:51:41 billl Exp $

## Running with NEURON or CoreNEURON

* Make sure NEURON installation is in $PATH and $PYTHONPATH

export PATH=$HOME/install/bin/:$PATH
export PYTHONPATH=$HOME/install/lib/python/:$PYTHONPATH

* Compile mod files for CoreNEURON

nrnivmodl-core mod_corenrn/

* Compile mod files NEURON

# neuron only + coreneuron mod files
cp mod_corenrn/* mod_nrn/
nrnivmodl mod_nrn

* Now run coreneuron and save results

./x86_64/special -python
mv v.dat coreneuron.v.dat

* Now run neuron and save results

sed -i -e 's/coreneuron\.enable = True/coreneuron\.enable = False/g'
./x86_64/special -python
mv v.dat neuron.v.dat

* Compare results

diff coreneuron.v.dat neuron.v.dat

## Original instructions

To run under linux/unix:
  cd b04feb12
  $CPU/special batch_.hoc -
To run under mswin:
  unzip the archive
  run mknrndll and find the directory b04feb12 that came from the zip file and make
  the nrnmech.dll file
  start the simulation by double clicking on mosinit.hoc
This is a pretty good replica of simulations shown in Fig. 2A (jcomputnsci1:39F2A.gif)
and Fig. 3 (jcomputnsci1:39F3.gif) of:
  author = 	"Pinsky, P and Rinzel, J",
  title = 	"Intrinsic and Network Rhythomogenesis in a Reduced
		  Traub Model for CA3 Neurons",
  journal = 	"J Computational Neuroscience",
  year = 	"1994",
  volume = 	"1",
  pages = 	"39-60",

The simulation provided will create 3 graphs: 
  Fig. 3 using Neuron (upper left)
  Fig. 3 using xppaut (upper right)
  Fig. 2A using Neuron (black) and xppaut (red) -- xppaut is same as in journal
The xppaut results are read into Neuron from a data file.  

The correct simulation is available in the download as booth_bose.ode
This program can be run under Bard Ermentraut's simulator xppaut 
The booth_bose.ode provided here is slightly modified from the original obtained
from Victoria Booth's home page; the originals include more extensive simulations:

I have not perfectly replicated the simulation: the morphology of the bursts are
very close to being correct but the timing of bursts in the 1500 ms simulation is 
off (as can be seen by zooming in on the final burst). This is due to the precise
timing of threshold crossings (see next paragraph).   The data for the xppaut, 
accurate replica is provided in xpp.dat.  The columns are 't vs vd cad hs ns sd cd
qd gkq gkc'.  The first 3 state variables from this data file are opened in Neuron
by vectors of the same name (time is in tvec).

The difficulty in porting this program to Neuron can be seen by looking at the
FInitializeHandler and nafPR.mod.  In the hoc file, we unset state variables (set
to zero) using FInitializeHandler("unset()").  Notice that even h_nafPR, which
would typically be set to a value near 1 to start (ie sodium channel fully
deinactivated) is set to 0 and must therefore gradually relax to 1 during the
first few ms of the simulation.  In nafPR.mod, we see that the sodium conductance
is not dependent on state variable 'm' but instead on the steady-state value
'minf'.  Here I found that I got different results depending on my implementation
of nafPR.  Another likely location for differences in the simulations arises in
the handling of the thresholds for the calcium-sensitive potassium channels.  Iahp
(here called rkq under Neuron -- historical -- and Kahp under xppaut) has
discontinuity at a Ca (chi) level of 500 ('min(0.00002*Cad,0.01)' in
booth_bose.ode); Kc (kcRT03 bzw. KC) at 250 -- 'min(Cad/250.0,1.0)'.  Precise
simulation results depend on exactly when these are handled.

I'm sure that these limitations can be overcome but I suspect that it would
require looking closely at the BREAKPOINT mechanism to make sure that values are
made available at the same time step in both xppaut and Neuron (both simulations
are using CVODE/CVODES)

I also have not incorporated the 'p' variable which sets the dendrite/soma
area ratio.  It is called pp and set to 0.5 but not incorporated in the geometry
setting routines.

## Changelog

20170914 A vecst.mod from Ruth Betterton derived from Wang Buzaki's
         vecst.mod makes model work under mswin.

20210222 Instructions for running with CoreNEURON from Pramod Kumbhar.

20220516 Updated MOD files to contain valid C++ and be compatible with the
         upcoming versions 8.2 and 9.0 of NEURON.