Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)

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Accession:93325
Model 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.
Reference:
1 . Calin-Jageman RJ, Tunstall MJ, Mensh BD, Katz PS, Frost WN (2007) Parameter space analysis suggests multi-site plasticity contributes to motor pattern initiation in Tritonia. J Neurophysiol 98:2382-98 [PubMed]
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]; Mensh, Brett ; Frost, William N; Katz, Paul S; Tunstall, Mark ;
Search NeuronDB for information about:  I A;
objectvar save_window_, rvp_
objectvar scene_vector_[4]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}

//Begin RunFitter[0]
{
load_file("runfit.hoc")
}
{
ocbox_=new RunFitter()
runfit_master = ocbox_
}
{object_push(ocbox_)}
{
fitness_name = ""
spec_fit_var("TestCell.sthold.freq")
spec_parms("tcscale", 1, -1e+06, 1e+06, 1)
tolerance = 1e-05
spec_parms("synscale", 1, -1e+06, 1e+06, 1)
tolerance = 1e-05
init_weights(1)
quiet_ = 0
normweight = 1
w_boundary.x[0] = 0
w_weight.x[0] = 1
w_boundary.x[1] = 1e+09
w_weight.x[1] = 1
build_deck()
}
{object_pop()}
{
{
save_window_=ocbox_.g
save_window_.size(0,300,0,200)
scene_vector_[2] = save_window_
ocbox_.g = save_window_
save_window_.save_name("ocbox_.g")
}
ocbox_.map("RunFitter[0]", 960, 96, 406.8, 551.7)
}
objref ocbox_
//End RunFitter[0]

{WindowMenu[0].ses_gid(1, 0, 1, "unnamed Window Group")}
{
xpanel("RunControl", 0)
v_init = -65
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 = 105000
xvalue("t","t", 2 )
tstop = 105000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 1
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 1
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
screen_update_invl = 0.05
xvalue("Scrn update invl","screen_update_invl", 1,"", 0, 1 )
realtime = 2.66
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(36,138)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
{
save_window_ = new Graph(0)
save_window_.size(0,105000,1.19209e-07,9.7)
scene_vector_[3] = save_window_
{save_window_.view(0, 1.19209e-07, 105000, 9.7, 372, 338, 300.6, 200.8)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("TestCell.sthold.freq", 1, 1, 0.8, 0.9, 2)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
{
xpanel("IClamp[0] at VSIType[0].soma(0.5)", 0)
xlabel("IClamp[0] at VSIType[0].soma(0.5)")
etrode.del = 0
xvalue("del","etrode.del", 1,"", 0, 1 )
etrode.dur = 105000
xvalue("dur","etrode.dur", 1,"", 0, 1 )
etrode.amp = 2.8
xvalue("amp","etrode.amp", 1,"", 0, 1 )
etrode.i = 0.65
xvalue("i","etrode.i", 0,"", 0, 1 )
xpanel(410,74)
}
{WindowMenu[0].ses_gid(0, 0, 1, "unnamed Window Group")}
objectvar scene_vector_[1]
{doNotify()}

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