Classic model of the Tritonia Swim CPG (Getting, 1989)

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Accession:93326
Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible.
Reference:
1 . Getting PA (1989) Reconstruction of small neural networks. Methods in Neuronal Modeling: From Synapses to Networks., Koch C:Segev I, ed. pp.171
2 . Getting PA (1983) Mechanisms of pattern generation underlying swimming in Tritonia. II. Network reconstruction. J Neurophysiol 49:1017-35 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Tritonia;
Cell Type(s): Tritonia swim interneuron dorsal; Tritonia cerebral cell; Tritonia swim interneuron ventral;
Channel(s): I A;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Bursting; Oscillations; Invertebrate;
Implementer(s): Calin-Jageman, Robert [rcalinjageman at gsu dot edu];
Search NeuronDB for information about:  I A;
objectvar save_window_, rvp_
objectvar scene_vector_[6]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}

//Begin I/V Clamp Electrode
{
load_file("electrod.hoc")
}
{
ocbox_=new Electrode(0)
execute("can_locate=1 sec=\"DSIType[0].soma\" xloc=0.5 locate(0)", ocbox_)
execute("vc.dur[0]=0.1 vc.amp[0]=-65", ocbox_)
execute("vc.dur[1]=2.5 vc.amp[1]=10", ocbox_)
execute("vc.dur[2]=100 vc.amp[2]=-65", ocbox_)
execute("stim.del=0 stim.dur=600000 stim.amp=-0.2", ocbox_)
execute("vcsteps=5", ocbox_)
execute("samp=stim.amp  store_vclamp() glyph()", ocbox_)
ocbox_ = ocbox_.v1
ocbox_.map("I/V Clamp Electrode", 8, 757, 251.1, 576.9)
}
objref ocbox_
//End I/V Clamp Electrode

{WindowMenu[0].ses_gid(1, 0, 1, "unnamed Window Group")}
{
xpanel("RunControl", 0)
v_init = -47.5
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 60000
xvalue("t","t", 2 )
tstop = 60000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.5
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 2
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xcheckbox("Quiet",&stdrun_quiet,"")
realtime = 9.52
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(-5,309)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
{
save_window_ = new Graph(0)
save_window_.size(0,60000,-80,20)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 60000, 100, 382, 2, 451.8, 254.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("DSIhash.spikehash+DSI.soma.v(0.5)", 1, 1, 0.8, 0.9, 2)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
{
save_window_ = new Graph(0)
save_window_.size(0,60000,-110,20)
scene_vector_[4] = save_window_
{save_window_.view(0, -110, 60000, 130, 375, 806, 448.2, 238.6)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("VSIhash.spikehash+VSI.soma.v(0.5)", 1, 1, 0.8, 0.9, 2)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
{
save_window_ = new Graph(0)
save_window_.size(0,61000,-100,20)
scene_vector_[5] = save_window_
{save_window_.view(0, -100, 61000, 120, 378, 406, 449.1, 253.9)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("C2hash.spikehash+C2.soma.v(0.5)", 1, 1, 0.8, 0.9, 2)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
objectvar scene_vector_[1]
{doNotify()}