This a NEURON + Python2 script associated with the paper:

Ruben A. Tikidji-Hamburyan, Carmen C. Canavier

**Shunting Inhibition Improves Synchronization in Heterogeneous Inhibitory Interneuronal Networks with Type 1 Excitability Whereas Hyperpolarizing Inhibition is Better for Type 2 Excitability **

eNeuro (in press)

Requirements

To use this scripts you need Python 2.7 and python's libraries:

• numpy
• scipy
• matplotlib and LaTeX for correct graphical interface

Under Ubuntu or any other Debian based Linux, run sudo apt-get install python-numpy python-scipy python-matplotlib texlive-full.

You can use yum or zymm under RadHad or SUSE based Linux distributions.

Examples from the paper

To run simulations:

• nrnivmodl
• nrngui -nogui -python network.py [parameters]

[paramters] for different figures are given below

Figure 3A

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /tv=0,1000 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Istdev=1.5e-2 /neuron/Vinit=-50.,20 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=5e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-75.0 /ttFFT=False /tracetail=p2eLFP /N2NHI=False /nrnFRhist/range=-0.05,0.85 /nrnFRhist/bins=9 /nrnFRhist/xnorm=True /nrnFRhist/ymax=0.5 /sortbysk=FR /nrnISI=100 /nrnFRhist/part=True

Figure 3B

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /tv=0,1000 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Istdev=1.5e-2 /neuron/Vinit=-50.,20. /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=5e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-75.0 /ttFFT=False /tracetail=p2eLFP /N2NHI=False /nrnFRhist/range=-0.05,0.85 /nrnFRhist/bins=9 /nrnFRhist/xnorm=True /nrnFRhist/ymax=0.5 /sortbysk=FR /nrnISI=100 /nrnFRhist/part=True

Figure 3C $^2$

nrngui -nogui -python network.py /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=YYYY\*1e-2 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=XXX\*1e-2 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-75.0 /tstop=2500 /cliptrn=500

where XXX is a synaptic conductance and YYYY is a level of noise. The scale 1e-2 converts nA into uA/cm2 and uS into mS/cm2.

Figure 3D $^2$

nrngui -nogui -python network.py /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=YYYY*1e-2 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=XXX*1e-2 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-75.0 /tstop=2500 /cliptrn=500

where XXX is a synaptic conductance and YYYY is a level of noise.

Figure 4A and supplementary movie sp1-20190619163238.mp4 $^1$

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /sortbysk=I /tv=400,500 /ncell=300 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Istdev=1.5e-2 /neuron/Vinit=-68 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=5e-4 /synapse/delay=0.7,3.5 /delay-dist="UNIFORM" /ttFFT=False /tracetail=p2eLFP /N2NHI=False /pop-pp-view=True /PhaseLims=(-76,-20),(0.3,0.6) /pop-pp-view-color=True

Figure 4B and supplementary movie sp2-20190619165631.mp4 $^1$

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /sortbysk=I /tv=400,500 /ncell=300 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Istdev=1.5e-2 /neuron/Vinit=-68 /neuron/Iapp='u',0.02,0.037 /synapse/weight=5e-4 /synapse/delay=0.7,3.5 /delay-dist="UNIFORM" /ttFFT=False /tracetail=p2eLFP /N2NHI=False /pop-pp-view=True /PhaseLims=(-76,-20),(0.3,0.6) /pop-pp-view-color=True

Figure 5A

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /tv=0,1000 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Istdev=1.5e-2 /neuron/Vinit=-50.,20 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=15e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65.0 /ttFFT=False /tracetail=p2eLFP /N2NHI=False /nrnFRhist/range=-0.05,0.85 /nrnFRhist/bins=9 /nrnFRhist/xnorm=True /nrnFRhist/ymax=0.5 /sortbysk=FR /nrnISI=100 /nrnFRhist/part=True

Figure 5B

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /tv=0,1000 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Istdev=1.5e-2 /neuron/Vinit=-50.,20. /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=15e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65.0 /ttFFT=False /tracetail=p2eLFP /N2NHI=False /nrnFRhist/range=-0.05,0.85 /nrnFRhist/bins=9 /nrnFRhist/xnorm=True /nrnFRhist/ymax=0.5 /sortbysk=FR /nrnISI=100 /nrnFRhist/part=True

Figure 5C $^2$

nrngui -nogui -python network.py /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=YYYY*1e-2 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=XXX*1e-2 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65.0 /tstop=2500 /cliptrn=500

where XXX is a synaptic conductance and YYYY is a level of noise.

Figure 5D $^2$

nrngui -nogui -python network.py /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=YYYY*1e-2 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=XXX*1e-2 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65.0 /tstop=2500 /cliptrn=500

where XXX is a synaptic conductance and YYYY is a level of noise.

Figure 6A and supplementary movie sp3-20190619103132.mp4 $^1$

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /sortbysk=I /tv=400,500 /ncell=300 /ncon=\'b\',0.133 /neuron/Type=1 /neuron/Istdev=1.5e-2 /neuron/Vinit=-68 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=15e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65. /ttFFT=False /tracetail=p2eLFP /N2NHI=False /pop-pp-view=True /PhaseLims=(-76,-20),(0.3,0.6) /pop-pp-view-color=True

Figure 6B and supplementary movie sp4-20190619104959.mp4 $^1$

nrngui -nogui -python network.py /gui=ON /git=False /preview=ON /sortbysk=I /tv=400,500 /ncell=300 /ncon=\'b\',0.133 /neuron/Type=2 /neuron/Istdev=1.5e-2 /neuron/Vinit=-68 /neuron/Iapp=\'u\',0.02,0.037 /synapse/weight=15e-4 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/Esyn=-65. /ttFFT=False /tracetail=p2eLFP /N2NHI=False /pop-pp-view=True /PhaseLims=(-76,-20),(0.3,0.6) /pop-pp-view-color=True

Figure 7 A1

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=5.e-06 /singmod/per=200.0

Figure 7 A2

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=5.e-06 /singmod/per=200.0

Figure 7 B1

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=2.e-06 /singmod/per=100.0

Figure 7 B2

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /corefunc=24 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=2.e-06 /singmod/per=100.0

Any point on heatmap Figure 7 C1 $^2$

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=20000.0 /tstop=20000.0 /singmod/gmax=XXXX*1e-2 /singmod/per=1000./YYYY

where XXXX is modulation conductance in $\mu A/cm^2$ and YYYY is modulation frequency in Hz

Any point on heatmap Figure 7 C2 $^2$

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-75.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /singmod/E=-75.0 /singmod/tstart=0 /singmod/tstop=20000.0 /tstop=20000.0 /singmod/gmax=XXXX*1e-2 /singmod/per=1000./YYYY

where XXXX is modulation conductance in $\mu A/cm^2$ and YYYY is modulation frequency in Hz

Figure 8 A1

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Iapp=2.85e-2 /neuron/Istdev=3.e-2 /neuron/Vinit=-68 /gui=ON /git=ON /preview=ON /tv=0,500 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/weight=1e-3 /singmod/tstart=-100 /singmod/tstop=methods['tv'][1] /singmod/gmax=2e-6 /singmod/per=200 /tracetail=p2eLFP /ttFFT=False /p2eLFP_max=250. /sortbysk=T

Figure 8 A2

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Iapp=2.85e-2 /neuron/Istdev=3.e-2 /neuron/Vinit=-68 /gui=ON /git=ON /preview=ON /tv=0,500 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/weight=1e-3 /singmod/tstart=-100 /singmod/tstop=methods['tv'][1] /singmod/gmax=2e-6 /singmod/per=200 /tracetail=p2eLFP /ttFFT=False /p2eLFP_max=250. /sortbysk=T

Figure 8 B1

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Iapp=2.85e-2 /neuron/Istdev=3.e-2 /neuron/Vinit=-68 /gui=ON /git=ON /preview=ON /tv=0,500 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/weight=1e-3 /singmod/tstart=-50 /singmod/tstop=methods['tv'][1] /singmod/gmax=2e-6 /singmod/per=100 /tracetail=p2eLFP /ttFFT=False /p2eLFP_max=250. /sortbysk=T

Figure 8 B2

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Iapp=2.85e-2 /neuron/Istdev=3.e-2 /neuron/Vinit=-68 /gui=ON /git=ON /preview=ON /tv=0,500 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /synapse/weight=1e-3 /singmod/tstart=-50 /singmod/tstop=methods['tv'][1] /singmod/gmax=2e-6 /singmod/per=100 /tracetail=p2eLFP /ttFFT=False /p2eLFP_max=250. /sortbysk=T

Figure 9 A

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-65.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-65.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=8.e-06 /singmod/per=200.0

Figure 9 B

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=ON /preview=ON /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-65.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /sortbysk=I /singmod/E=-65.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tv=0.,2000.0 /singmod/gmax=8.e-06 /singmod/per=200.0

Any point on heatmap Figure 9 C1 $^2$

nrngui -nogui -python network.py /neuron/Type=1 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-65.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /singmod/E=-65.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tstop=2000.0 /singmod/gmax=XXXX*1.e-02 /singmod/per=1000./YYYY

where XXXX is modulation conductance in $\mu A/cm^2$ and YYYY is modulation frequency in Hz

Any point on heatmap Figure 9 C2 $^2$

nrngui -nogui -python network.py /neuron/Type=2 /neuron/Vinit=-51.0,20.0 /neuron/Istdev=0.03 /neuron/Iapp=\'u\',0.02,0.037 /git=False /gui=False /preview=False /ncell=300 /ncon=\'b\',0.133 /synapse/weight=0.001 /synapse/Esyn=-65.0 /synapse/delay=0.7,3.5 /delay-dist=UNIFORM /tracetail=p2eLFP /p2eLFP_max=250 /ttFFT=False /PAC-VS=True /singmod/E=-65.0 /singmod/tstart=0 /singmod/tstop=2000.0 /tstop=2000.0 /singmod/gmax=XXXX*1.e-02 /singmod/per=1000./YYYY

where XXXX is modulation conductance in $\mu A/cm^2$ and YYYY is modulation frequency in Hz

For Figures 10

Use parameters for Figures 3C, 3D, 5C, 5D, 9C1, or 9C2 as above and add /neuron/distribution/F=\'n\',1.04,0.4 to the end of command line.

Notes

1. For Figures 4A, 4B, 6A, and 6B, if you click on phase-plot window, you can explore evolution of population dynamics using page-up/page-down keys.
2. Simulations for Figures 3C, 3D, 5C, 5D, 7C, and 9C will not show anything on the screen. All results are saved in the network.simdb file. simdb has a very simple format: each simulation is a line. Column : separates recorded fields. Each field is a couple key=value with the equal symbol as a separator. An example of fields in a simulation record shows R2-index of network synchronization, spike-per-cycle, and neurons firing rate to network frequency ratio: /R2-results/R2=0.80846372802:/R2-results/spc=88.1:/R2-results/stdr_Fr/Fnet=0.264655449759

Files in this directory

File	Description
network.py	main script
norm_translation.py	subroutine for synapses amplitude normalization (wasn't used in the paper)
type21v02.mod	NEURON module for membrane currents of a single neuron
innp.mod	noise current generator, writen by Ted Carnevale
sinGstim.mod	module for sinusoidal conductance modulation
sinIstim.mod	module for sinusoidal current modulation (wasn't used in the paper)

Changelog

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