GitHub - ModelDBRepository/185355: Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
BC.hoc		BC.hoc
BK.mod		BK.mod
CaL.mod		CaL.mod
CaN.mod		CaN.mod
CaT.mod		CaT.mod
FitSimScore_ForallFigs.m		FitSimScore_ForallFigs.m
GC.hoc		GC.hoc
GCinput.py		GCinput.py
HCN.mod		HCN.mod
HIPP.hoc		HIPP.hoc
Ka.mod		Ka.mod
Kir.mod		Kir.mod
MC.hoc		MC.hoc
PP.hoc		PP.hoc
SK.mod		SK.mod
ccanl.mod		ccanl.mod
fig1.jpg		fig1.jpg
fig1.py		fig1.py
fig2.jpg		fig2.jpg
fig2.py		fig2.py
fig3.jpg		fig3.jpg
fig3.py		fig3.py
fig4.py		fig4.py
fig5.py		fig5.py
ichan2.mod		ichan2.mod
inout_pattern.py		inout_pattern.py
main.hoc		main.hoc
netstim125.mod		netstim125.mod
netstimbox.mod		netstimbox.mod
plot_DG_all.py		plot_DG_all.py
printfile.hoc		printfile.hoc
ranstream.hoc		ranstream.hoc
readme.html		readme.html
sim_score.py		sim_score.py

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        <p><h3> Conductance-based computer model of the DG network realizing a spatiotemporal pattern separation task employing either physiological or leaky GC phenotype.</h3> See the original paper for details:</p>
        <p>Yim&nbsp;MY,&nbsp;Hanuschkin&nbsp;A,&nbsp;Wolfart&nbsp;J (2015) Intrinsic rescaling of granule cells restores pattern separation ability of a dentate&nbsp;gyrus&nbsp;network model during epileptic&nbsp;hyperexcitability. Hippocampus 25:297-308.</p>
        <p> <a href="http://onlinelibrary.wiley.com/doi/10.1002/hipo.22373/abstract"> http://onlinelibrary.wiley.com/doi/10.1002/hipo.22373/abstract </a></p>
        <p>&nbsp;</p>
        <p>Dr. Man Yi&nbsp;Yim&nbsp;/ 2015</p>
        <p>Dr. Alexander Hanuschkin / 2011</p>
        <p>&nbsp;</p>


        <!-- <p>---------------</p> -->
        <p><h3>To run the NEURON simulation and data analysis under Unix system:</h3></p>
        <p>&nbsp;</p>
        <p>1. Compile the mod files using the command</p>
        <p>&gt;&nbsp;nrnivmodl</p>
        <p>&nbsp;</p>
        <p>2. Run the simulation (select the figure you want to simulate by setting <i>fig</i> in main.hoc before running)</p>
        <p>&gt; ./x86_64/special&nbsp;main.hoc</p>
        <p>if your computer is running the 64-bit version, or</p>
        <p>&gt; ./i686/special&nbsp;main.hoc</p>
        <p>for the 32-bit.</p>
        <p>&nbsp;</p>
        <p>3. Open ipython or other command cells for Python, and run the data analysis</p>
        <p>&gt; ipython</p>
        <p>&gt; run fig1.py</p>
        <p>&nbsp;</p>
        <p> Alternatively, you can set the <i>idname</i> of the following python codes and run the codes separately.</p>
        <p>&nbsp;</p>
        <p>a) To plot the network activity in a trial (e.g. Fig 1C,D), run the python code plot_DG_all.py</p>
        <p><img src="fig1.jpg" alt="fig1.jpg" /></p>
        <p>&nbsp;</p>
        <p>b) To plot the activity of a neuron (e.g. Fig 1B), run the python code GCinput.py</p>
        <p><img src="fig2.jpg" alt="fig2.jpg" /></p>
        <p>&nbsp;</p>
        <p>c) To plot the network input and GC output (Fig 1E), run the python code inout_pattern.py</p>
        <p>&nbsp;</p>
        <p>4. To make a scatter plot of similarity scores and fit the data (Fig 1E) , run the python code sim_score.py and then the matlab code FitSimScore_ForallFigs.m</p>
        <p><img src="fig3.jpg" alt="fig3.jpg" /></p>
        <p>&nbsp;</p>
        <!-- <p>---------------</p> -->
<h3> File description </h3>
        <p><b>Main code: run this code for the simulation</b></p>
        <p>main.hoc</p>
        <p>&nbsp;</p>
        <p><b>Printing code: format of the file output</b></p>
        <p>printfile.hoc</p>
        <p>&nbsp;</p>
        <p><b>Neuron models: morphology,&nbsp;conductances, ion channels and neuronal properties</b></p>
        <p>GC.hoc</p>
        <p>BC.hoc</p>
        <p>MC.hoc</p>
        <p>HIPP.hoc</p>
        <p>&nbsp;</p>
        <p><b>Input models: properties of the inputs</b></p>
        <p>PP.hoc</p>
        <p>ranstream.hoc</p>
        <p>&nbsp;</p>
        <p><b>Conductances: dynamics and properties of&nbsp;conductances</b></p>
        <p>BK.mod</p>
        <p>CaL.mod</p>
        <p>CaN.mod</p>
        <p>CaT.mod</p>
        <p>ccanl.mod</p>
        <p>HCN.mod</p>
        <p>ichan2.mod</p>
        <p>Ka.mod</p>
        <p>Kir.mod</p>
        <p>SK.mod</p>
        <p>&nbsp;</p>
        <p><b>Spike generators:</b></p>
        <p>netstimbox.mod</p>
        <p>netstim125.mod</p>
        <p>&nbsp;</p>
        <p><b>Python-Matlab-Analysis:</b></p>
        <p>FitSimScore_ForallFigs.m&nbsp;fits the&nbsp;sim&nbsp;score data points by the method of least</p>
        <p>squares.</p>
        <p>plot_DG_all.py plots DG neurons' activity.</p>
        <p>GCinput.py extracts and plots the inputs to a selected GC.</p>
        <p>inout_pattern.py plots the inputs and GC outputs.</p>
        <p>sim_score.py creates a scatter plot of output&nbsp;vs&nbsp;input&nbsp;sim&nbsp;scores.&nbsp;</p>
        <p>&nbsp;</p>


 <!-- <p>---------------</p> -->
<h3> Introduced changes in Mod files compared to the original DG model of Santhakumar et al. 2005</h3>

<p>In our scripts, the previously existing different potassium equilibrium potentials (Ekf, Eks, Ek..) were reduced to a single common Ek (e.g. GC.hoc, ichan2.mod, ....)).
</p>

<p>CaL.mod<br>
CaN.mod<br>
CaT.mod<br>
These are new mod files for L-, N- and T-type calcium channels written by A. Hanuschkin following the description in Ca ion & L/T/N-Ca channels model of <br>
Aradi I, Holmes WR (1999) J Comput Neurosci 6:215-35.<br>
Note that eCa is calculated during simulation by ccanl.mod (see below). ecat, ecal values set in Santhakumar are not used in our model scripts.</p>

<p>ccanl.mod<br>
Warning by Ted Carnevale 2015:<br>
The expression that this mechanism uses to calculate the contribution of ica to the rate of change of calcium concentration in the shell is <br>
-ica*(1e7)/(depth*FARADAY)<br>
but it should really be<br>
-ica*(1e7)/(depth*2*FARADAY)<br>
because the valence of ca is 2.  The result of this omission is that the mechanism behaves as if the shell is only 1/2 as thick as the value specified by the depth parameter.</p>

<p>ichan2.mod<br>
- added a tonic (leak) GABAA conductance to be modified during epilepsy (see Young CC, Stegen M, Bernard R, Muller M, Bischofberger J, Veh RW, Haas CA, Wolfart J (2009) J Physiol 587:4213-4233 <br>
<a href="http://onlinelibrary.wiley.com/doi/10.1113/jphysiol.2009.170746/abstract">http://onlinelibrary.wiley.com/doi/10.1113/jphysiol.2009.170746/abstract</a>)</p>

<p>Kir.mod<br>
New Mod file<br>
Added an inward rectifier potassium conductance to be modied during epilepsy (see Young CC, Stegen M, Bernard R, Muller M, Bischofberger J, Veh RW, Haas CA, Wolfart J (2009) J Physiol 587:4213-4233)<br>
Channel description and parameters from:<br>
Stegen M, Kirchheim F, Hanuschkin A, Staszewski O, Veh R, and Wolfart J. Cerebral Cortex, 22:9, 2087-2101, 2012.<br>
<a href="http://cercor.oxfordjournals.org/content/22/9/2087.long">http://cercor.oxfordjournals.org/content/22/9/2087.long</a></p>

<p>SK.mod<br>
Correction: use of correct dynamics (see rate() lines: 95-101)</p>

<!-- <p>---------------</p> -->
<h3> Other remarks</h3>
<p>BK.mod<br>
Please note that cai was not assiged here in the original Santhakumar et al. (2005) version (which we used). It should be cai = ncai + lcai + tcai, as noted by <br>
Morgan RJ, Santhakumar V, Soltesz I (2007) Prog Brain Res 163:639-58<br>
The bug was fixed to make the channel properly dependent on the current calcium concentration. See <br>
<a href="https://senselab.med.yale.edu/modeldb/showModel.cshtml?model=124513&file=/dentate_gyrus/CaBK.mod">  https://senselab.med.yale.edu/modeldb/showModel.cshtml?model=124513&file=/dentate_gyrus/CaBK.mod </a></p>

<h3>Changelog</h3>
2022-05: Updated MOD files to contain valid C++ and be compatible with the upcoming versions 8.2 and 9.0 of NEURON.
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