Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)

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Accession:84612
"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details.
Reference:
1 . Canavier CC, Landry RS (2006) An increase in AMPA and a decrease in SK conductance increase burst firing by different mechanisms in a model of a dopamine neuron in vivo. J Neurophysiol 96:2549-63 [PubMed]
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): I L high threshold; I N; I T low threshold; I A; I K; I K,Ca; I Sodium; I Calcium; Na/K pump;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Bursting; Detailed Neuronal Models; Intrinsic plasticity; Calcium dynamics; Sodium pump;
Implementer(s): Kuznetsova, Anna [anna.kuznetsova at utsa.edu];
Search NeuronDB for information about:  Substantia nigra pars compacta DA cell; AMPA; NMDA; Gaba; I L high threshold; I N; I T low threshold; I A; I K; I K,Ca; I Sodium; I Calcium; Na/K pump;
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CanavierLandry2006
in_vitro
README
ampasyn.mod *
cabalan.mod *
cachan.mod *
capump.mod *
hh3.mod *
kca.mod *
leak.mod *
nabalan.mod *
nmdasyn.mod *
pump.mod *
stim.mod *
fig10a.hoc
fig10a.ses
fig10AMPA.dat
fig10b.hoc
fig10b.ses
fig10c.hoc
fig10c.ses
fig10NMDA.dat
fig11b1.hoc
fig11b1.ses
fig11b2.hoc
fig11b2.ses
fig4b1.hoc
fig4b1.ses
fig4b2.hoc
fig4b2.ses
fig4b3.hoc
fig4b3.ses
fig4bAMPA.dat
fig4bNMDA.dat
fig5a.hoc
fig5a.ses
fig5AMPA.dat
fig5b.hoc
fig5b.ses
fig5NMDA.dat
fig9a1.hoc
fig9a1.ses
fig9a2.hoc
fig9a2.ses
fig9a3.hoc
fig9a3.ses
fig9aAMPA.dat
fig9aNMDA.dat
fig9b1.hoc
fig9b1.ses
fig9b2.hoc
fig9b2.ses
fig9b3.hoc
fig9b3.ses
fig9bAMPA.dat
fig9bNMDA.dat
mosinit.hoc
Receptor.cpp
                            
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[3]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
xpanel("RunControl", 0)
v_init = -66.7454
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5000
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 = 163.768
xvalue("t","t", 2 )
tstop = 5000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.0228612
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
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 = 1.86
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(822,59)
}
{
save_window_ = new Graph(0)
save_window_.size(0,5000,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 5000, 120, 744, 493, 300.48, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 1, 1, 0.8, 0.9, 2)
}
objectvar scene_vector_[1]
{doNotify()}

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