## 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 [$^2$](#note2) ` 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 [$^2$](#note2) ` 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 [$^1$](#note1) ` 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 [$^1$](#note1) ` 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 [$^2$](#note2) ` 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 [$^2$](#note2) ` 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 [$^1$](#note1) ` 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 [$^1$](#note1) ` 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 [$^2$](#note2) ` 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 [$^2$](#note2) ` 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 [$^2$](#note2) ` 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 [$^2$](#note2) ` 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.