Midbrain dopamine neuron: firing patterns (Canavier 1999)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:6763
Sodium dynamics drives the generation of slow oscillations postulated to underly NMDA-evoked bursting activity.
Reference:
1 . Canavier CC (1999) Sodium dynamics underlying burst firing and putative mechanisms for the regulation of the firing pattern in midbrain dopamine neurons: a computational approach. J Comput Neurosci 6:49-69 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Electrogenic pump;
Brain Region(s)/Organism:
Cell Type(s): Substantia nigra pars compacta DA cell;
Channel(s): Na/K pump;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Oscillations; Sodium pump;
Implementer(s): Canavier, CC;
Search NeuronDB for information about:  Substantia nigra pars compacta DA cell; NMDA; Na/K pump;
load_file("nrngui.hoc")
objectvar save_window_, rvp_
objectvar scene_vector_[7]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
save_window_ = new Graph(0)
save_window_.size(0,100000,-100,-21)
scene_vector_[2] = save_window_
{save_window_.view(0, -100, 100000, 79, 391, 24, 438.72, 188.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 2, 1, 0.729461, 0.291608, 2)
save_window_.addexpr("dend[0].v(0.99)", 1, 1, 0.513693, 1.05734, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100000,3.6,10)
scene_vector_[3] = save_window_
{save_window_.view(0, 3.6, 100000, 6.4, 387, 541, 437.76, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("dend[0].nai(0.99)", 1, 1, 0.392105, 0.9, 2)
save_window_.addexpr("soma.nai(0.5)", 2, 1, 0.383333, 0.38722, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100000,-0.017,0.007)
scene_vector_[4] = save_window_
{save_window_.view(0, -0.017, 100000, 0.024, 389, 281, 438.72, 192.64)}
graphList[1].append(save_window_)
save_window_.save_name("graphList[1].")
save_window_.addexpr("dend[0].inapump_pump( 0.99 )", 3, 1, 0.413948, 0.972084, 2)
save_window_.addexpr("dend[0].ina_nmda(0.99)", 1, 1, 0.43583, 0.858627, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,500,0,30)
scene_vector_[5] = save_window_
{save_window_.view(0, 0, 500, 30, 862, 296, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")
objectvar rvp_
rvp_ = new RangeVarPlot("nai")
prox[1] rvp_.begin(0)
dend[1] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.8, 0.9)
}
{
save_window_ = new Graph(0)
save_window_.size(0,510,-80,40)
scene_vector_[6] = save_window_
{save_window_.view(0, -80, 510, 120, 862, 26, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")
objectvar rvp_
rvp_ = new RangeVarPlot("v")
prox[1] rvp_.begin(0)
dend[1] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.8, 0.9)
}
{
xpanel("RunControl", 0)
v_init = -60
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 = 100000
xvalue("t","t", 2 )
tstop = 100000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 178.627
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xcheckbox("Quiet",&stdrun_quiet,"")
realtime = 23
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(89,114)
}
objectvar scene_vector_[1]
{doNotify()}