GitHub - ModelDBRepository/185858: Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)

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Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)

modeldb.science/185858

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Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
IC.mod		IC.mod
IKM.mod		IKM.mod
Ih.mod		Ih.mod
MyExp2SynBB.mod		MyExp2SynBB.mod
MyExp2SynNMDABB.mod		MyExp2SynNMDABB.mod
aux_fun.inc		aux_fun.inc
cagk.mod		cagk.mod
cal.mod		cal.mod
calts.mod		calts.mod
can.mod		can.mod
cat.mod		cat.mod
conf.py		conf.py
declist.hoc		declist.hoc
decnqs.hoc		decnqs.hoc
decvec.hoc		decvec.hoc
default.hoc		default.hoc
drline.hoc		drline.hoc
gabab.mod		gabab.mod
geom.py		geom.py
ghk.inc		ghk.inc
grvec.hoc		grvec.hoc
icalts.mod		icalts.mod
ihlts.mod		ihlts.mod
init.hoc		init.hoc
kap.mod		kap.mod
kcalts.mod		kcalts.mod
kdmc.mod		kdmc.mod
kdr.mod		kdr.mod
kdrbwb.mod		kdrbwb.mod
km.mod		km.mod
labels.hoc		labels.hoc
labels.py		labels.py
local.hoc		local.hoc
mglur.mod		mglur.mod
misc.h		misc.h
misc.mod		misc.mod
mpisim.py		mpisim.py
nafbwb.mod		nafbwb.mod
nax.mod		nax.mod
netcfg.cfg		netcfg.cfg
nqs.hoc		nqs.hoc
nqs.py		nqs.py
nrnoc.hoc		nrnoc.hoc
onepyr.cfg		onepyr.cfg
onepyr.py		onepyr.py
pyinit.py		pyinit.py
python.hoc		python.hoc
pywrap.hoc		pywrap.hoc
readme.html		readme.html
screenshot.png		screenshot.png
screenshot1.png		screenshot1.png
simctrl.hoc		simctrl.hoc
simdat.py		simdat.py
syncode.hoc		syncode.hoc
vecst.mod		vecst.mod
xgetargs.hoc		xgetargs.hoc

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<html><pre>

    These simulations were used in the following article:

Neymotin SA, McDougal RA, Bulanova AS, Zeki M, Lakatos P, Terman D,
Hines ML, Lytton WW.  Calcium regulation of HCN channels supports
persistent activity in a multiscale model of neocortex.
Neuroscience 2016.
<a href="http://www.sciencedirect.com/science/article/pii/S0306452215011367">http://www.sciencedirect.com/science/article/pii/S0306452215011367</a>

The code in this folder generates Fig. 4 (isolated pyramidal neuron
persistent activity) and Fig. 5 (network persistent activity).

The simulations were tested/developed on LINUX systems, but may run on
Microsoft Windows or Mac OS.

To run the demo, you will need the NEURON simulator (version 7.4 and
up; available at <a href="http://www.neuron.yale.edu">http://www.neuron.yale.edu</a>) compiled with python and
MPI enabled. You will need Matplotlib to draw the output (
<a href="http://matplotlib.org/">http://matplotlib.org/</a> ).

Instructions to setup the model in unix/linux:
  unzip the file
  cd CaHDemo
nrnivmodl

Please
see <a href="http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html">http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html</a>
for more help or help in other operating systems.

The nrnivmodl command will produce an architecture-dependent folder
with a script called special.  On 64 bit systems the folder is x86_64.

---------------------------------------------------------------------

Simulation of isolated pyramidal cell persistent activity (Figure 4).
Run the following code in a terminal from within the directory
containing the model files:
<b>
python -i onepyr.py onepyr.cfg
</b>
This simulation takes ~35 seconds on Linux with Intel(R) Core(TM) i7-4940MX
CPU @ 3.10GHz and ~52 seconds on a 2012 MacBook Pro and produces this:

<img src="./screenshot.png" alt="screenshot" width="550">
---------------------------------------------------------------------
Run network simulation from a terminal with:
mpiexec -np 8 nrniv -python -mpi mpisim.py netcfg.cfg

The network simulation uses MPI for parallelization. The simulation
can take a long time to run, depending on your hardware setup. This
simulation saves output data to the data subdirectory.  simdat.py
loads the data and draws the output.

To plot the output from this simulation, run the following:
python -i simdat.py
which will display results similar to figure 5 in the paper:
<img src="./screenshot1.png" alt="screenshot1" width="550">


Note: The -np 8 above specifies that mpiexec should use 8 cores. This
number should change depending on your hardware setup. If changing 8
to 24 cores (-np 24), make sure to update the defnCPU parameter on
line 38 of simdat.py; this is because mpisim.py saves 1 output file
per core.  The simulation takes ~28 minutes on Linux with 8 cores
(Intel(R) Core(TM) i7-4940MX CPU @ 3.10GHz) and runs in ~11 minutes on
Linux with 24 cores (Intel(R) Xeon(R) CPU E5-4610 0 @ 2.40GHz).
---------------------------------------------------------------------

For questions/comments email:  
  samn at neurosim dot downstate dot edu
   or
  robert dot mcdougal at yale dot edu

20160915 This updated version from the Lytton lab allows their models
which contain misc.mod and misc.h to compile on the mac.

20171010 An update from Robert McDougal to run on mswin as well as linux, mac.

20171011 Thanks Adam Newton for noticing missing misc.h.  Added a screenshot.

20171012 Adam supplied a misc.mod that helps simdat.py complete and
Robert added a line to mpisim.py so it could run from Python with a
command like:
mpiexec -n 8 python mpisim.py netcfg.cfg

20171012a Robert suggested an edit to simdat.py that helps it run more
consistently.

20220523 Updated MOD files to contain valid C++ and be compatible with
the upcoming versions 8.2 and 9.0 of NEURON.
</pre></html>