Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019)
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title></title> <meta name="generator" content="HTML::TextToHTML v2.51"/> <style> table { font-family: arial, sans-serif; border-70%; } td, th { border: 1px solid #dddddd; text-align: left; padding: 8px; } tr:nth-child(even) { background-color: #dddddd; } h4, h3 { font-family: arial, sans-serif; } </style> </head> <body> <p> <h4> <h3>This NEURON + Python scripts associated with paper:</h3><br/> Ruben A. Tikidji-Hamburyan, Conrad A. Leonik and Carmen C. Canavier <br/> Phase Response Theory Explains Cluster Formation in Sparsely but Strongly Connected Inhibitory Neural Networks and Effects of Jitter due to Sparse Connectivity <br/> <i>J Neurophysiology 2019 </i><br/> </h4> </p><hr/> <font style="font-family: arial, sans-serif;" > <p>To use this scripts you need python libraries: <ol> <li><a ref="http://www.numpy.org">numpy</a></li> <li><a ref="http://www.scipy.org">scipy</a></li> <li><a ref="http://matplotlib.org">matplotlib</a> and LaTeX for correct graphical interface</li> </ol> Under <u>Ubuntu</u> or any other <u>Debian</u> based Linux, use the following command for installation all required packages: <b>sudo apt-get install python-numpy python-scipy python-matplotlib texlive-full ncurses-dev dvipng</b></br> You can use <b>yum</b> or <b>zymm</b> under <i>RadHad(C)</i> or <i>SUSE(C)</i> based Linux distributions for installation or consult your distribution package-manager.</br> <b>NOTE: These scripts were <u>NOT</u> tested under <i>Windows(C)</i>, <i>MacOS(C)</i> or any other operating systems.</b></br> Please let me know if you you can run this code under <i>Windows(C)</i>, <i>MacOS(C)</i> (Ruben Tikidji-Hamburyan, ruben (dot) tikidji (dot) hamburyan (at) gmail (dot) com) </p><hr/> <p> To run simulations of pure ingibitory <b>I-network</b>: <ol> <li><b>cd PIR-Inetwork</b></li> <li><b>nrnivmodl</b></li> <li><b>nrngui -nogui -python network.py --help</b></li> </ol> Last command prints out a short HOWTO and the list of parameters on screen, but does <b>not</b> run simulation. You can use printed parameters to replicate Figures in the paper. </p> --- <p> To replicate activity for any parameter set (<u>X</u> synaptic conductance in uS and <u>Y</u> delay in milliseconds) in bifurcation diagram Figure 5 B run:<br/> <b> nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\(\'b\',0.133\) /synapse/weight=<u>X</u> /synapse/delay=<u>Y</u></b><br/> </p> <p style="margin-left: 50px;"> For example Figure 4 A1 - two clusters:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=299,299,40 /synapse/weight=0.006e-2 /synapse/delay=0.8</b><br/> For example Figure 4 A2 - synchrony:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=299,299,40 /synapse/weight=0.1e-2 /synapse/delay=3.</b><br/> For example Figure 4 B1 - two clusters:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=40 /synapse/weight=0.006e-2 /synapse/delay=0.8</b><br/> For example Figure 4 B2 - synchrony:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=40 /synapse/weight=0.1e-2 /synapse/delay=3.</b><br/> For example Figure 4 B1 - two clusters:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.1333 /synapse/weight=0.006e-2 /synapse/delay=0.8</b><br/> For example Figure 4 B2 - synchrony:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.1333 /synapse/weight=0.1e-2 /synapse/delay=3.</b><br/> For example Figure 5 D1 - transitional:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.133 /synapse/weight=0.0025e-2 /synapse/delay=3</b><br/> For example Figure 5 D2 - asynchrony:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.133 /synapse/weight=0.3e-2 /synapse/delay=0.8</b><br/> For example Figure 6 A - synaptic modulation for delay 2.2ms (simulation is very slow):<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.1333 /synapse/weight=0.006e-2 /synapse/delay=2.2 /wmod/scale=3.333 /wmod/time-points=\[600,1000\] /corefunc=1 /corefunc=1 /neuron/Istdev=0. /tv=0,1001</b><br/> For example Figure 6 B - synaptic modulation with noise (simulation is very slow):<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\'b\',0.1333 /synapse/weight=0.006e-2 /synapse/delay=2.2 /wmod/scale=3.333 /wmod/time-points=\[600,1000\] /corefunc=1 /corefunc=1 /neuron/Istdev=0.2e-2 /tv=0,1001</b><br/> </p> --- <p>To replicate activity for any parameter set in bifurcation diagram Figure 5A run:<br/> <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\(299,299,40\) /synapse/weight=<u>X</u> /synapse/delay=<u>X</u></b><br/> </p><p style="margin-left: 50px;"> For example<br/> for 2 clusters: <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\(299,299,40\) /synapse/weight=0.006e-2 /synapse/delay=0.8</b><br/> for synchrony: <b>nrngui -nogui -python network.py /gui=True /preview=True /git=False /ncon=\(299,299,40\) /synapse/weight=0.1e-2 /synapse/delay=3.</b><br/> </p> <hr/> <p> To run simulations excitatory/inhibitory <b>E/I-network</b>:: <ol> <li><b>cd PIR-EInetwork</b></li> <li><b>nrnivmodl</b></li> <li><b>nrngui -nogui -python pirping-network.py --help</b></li> </ol> Again last one will just print out HOWTO. </p> --- <p> To run simulation for any point in Figure 7B with synaptic conductance = <u>X</u> uS run :<br/> <b>nrngui -nogui -python pirping-network.py -f=PIR-PING-2.2ms-rnd.conf /Connections/II/gmax-mean=<u>X</u></b><br/> </p><p style="margin-left: 50px;"> To replicate exact Figure 7A1 run:<br/> <b>nrngui -nogui -python pirping-network.py -f=example-asyn.conf</b><br/> To regenerate network with parameters as in Figure 7A1 but with random set of connections and Poisson's processes spikes (regenerated stochasticity) run:<br/> <b>nrngui -nogui -python pirping-network.py -f=PIR-PING-2.2ms-rnd.conf /Connections/II/gmax-mean=1.333521e-5</b><br/> </p><p style="margin-left: 50px;"> To replicate exact Figure 7A2 run:<br/> <b>nrngui -nogui -python pirping-network.py -f=example-ping.conf</b><br/> or with regenerated stochasticity :<br/> <b>nrngui -nogui -python pirping-network.py -f=PIR-PING-2.2ms-rnd.conf /Connections/II/gmax-mean=1.0e-06</b><br/> </p><p style="margin-left: 50px;"> To replicate exact Figure 7A3 run:<br/> <b>nrngui -nogui -python pirping-network.py -f=example-2clt.conf</b><br/> or with regenerated stochasticity :<br/> <b>nrngui -nogui -python pirping-network.py -f=PIR-PING-2.2ms-rnd.conf /Connections/II/gmax-mean=7.498942e-05</b><br/> </p><p style="margin-left: 50px;"> To replicate exact Figure 7A4 run:<br/> <b>nrngui -nogui -python pirping-network.py -f=example-sync.conf</b><br/> or with regenerated stochasticity :<br/> <b>nrngui -nogui -python pirping-network.py -f=PIR-PING-2.2ms-rnd.conf /Connections/II/gmax-mean=1.0e-02</b><br/> </p> <hr /> Files in this record:<br/> </font> <table style="width:70%"> <tr> <th>Directory/File</th><th>Description</th> </tr><tr> <td>PIR-Inetwork/</td><td>the I-network model</td> </tr><tr> <td>PIR-Inetwork/network.py</td><td>the main model script</td> </tr><tr> <td>PIR-Inetwork/*.mod</td><td>NEURON mechanisms for the main model</td> </tr><tr> <td>PIR-EInetwork</td><td>the model of EI-network</td> </tr><tr> <td>PIR-EInetwork/pirping-network.py</td><td>the main model script</td> </tr><tr> <td>PIR-EInetwork/PIR-PING-2.2ms-rnd.conf </td><td>the main parameter set</td> </tr><tr> <td>PIR-EInetwork/example-ping.conf </td><td>example of PING mode</td> </tr><tr> <td>PIR-EInetwork/example-2clt.conf </td><td>example of 2 cluster mode</td> </tr><tr> <td>PIR-EInetwork/example-asyn.conf </td><td>example of asychrony mode</td> </tr><tr> <td>PIR-EInetwork/example-sync.conf </td><td>example of synchrony mode</td> </tr><tr> <td>PIR-EInetwork/ECellOlufsen.py </td><td> python module for Olufsen et al 2003 module</td> </tr><tr> <td>PIR-EInetwork/ECellOlufsen.mod </td><td> required NEURON mechanisms</td> </tr><tr> <td>PIR-EInetwork/HHinh.py </td><td>python module for Hodgkin-Huxley model</td> </tr><tr> <td>PIR-EInetwork/SGen.py </td><td>artificial spike generator</td> </tr><tr> <td>PIR-EInetwork/vecevent.mod</td><td>NEURON module for the spike generator</td> </tr><tr> <td>PIR-EInetwork/simtools</td><td>python network generator</td> <tr> </table> </p> <p> <b>Changelog:</b><br> 2022-05: Updated MOD files to contain valid C++ and be compatible with the upcoming versions 8.2 and 9.0 of NEURON. </p> </body> </html>
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Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019)
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