Striatal Output Neuron (Mahon, Deniau, Charpier, Delord 2000)

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Accession:150621
Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (1 sec)increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input–output relationship as a function of their past activation.
Reference:
1 . Mahon S, Deniau JM, Charpier S, Delord B (2000) Role of a striatal slowly inactivating potassium current in short-term facilitation of corticostriatal inputs: a computer simulation study. Learn Mem 7:357-62 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell; Abstract Wang-Buzsaki neuron;
Channel(s): I Na,p; I Na,t; I K; I_Ks; I Krp;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Ion Channel Kinetics; Short-term Synaptic Plasticity;
Implementer(s): Biddell, Kevin [kevin.biddell at gmail.com];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; I Na,p; I Na,t; I K; I_Ks; I Krp;
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MahonEtAl2000
README.html
KAfm.mod *
KAsm.mod *
Kirm.mod
Km.mod *
Krpm.mod *
Leakm.mod
Nam.mod *
NaPm.mod *
NaSm.mod *
figure2a.ses
figure3a.ses
Figures2B3B.xls
init.hoc
kmb.mahon.1.hoc
mosinit.hoc *
screenshot2A.png
screenshot3Aa.png
screenshot3Ab.png
                            
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[4]
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(0,800,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 800, 120, 565, 308, 300.6, 200.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("v(.5)", 1, 1, 0.8, 0.9, 2)
}
{
xpanel("IClamp[0] at soma(0.5)", 0)
xlabel("IClamp[0] at soma(0.5)")
stim1.del = 1400
xvalue("del","stim1.del", 1,"", 0, 1 )
stim1.dur = 200
xvalue("dur","stim1.dur", 1,"", 0, 1 )
stim1.amp = 0.00167
xvalue("amp","stim1.amp", 1,"", 0, 1 )
stim1.i = 0
xvalue("i","stim1.i", 0,"", 0, 1 )
xpanel(296,642)
}
{
xpanel("IClamp[1] at soma(0.5)", 0)
xlabel("IClamp[1] at soma(0.5)")
stim2.del = 1400
xvalue("del","stim2.del", 1,"", 0, 1 )
stim2.dur = 200
xvalue("dur","stim2.dur", 1,"", 0, 1 )
stim2.amp = 0.00167
xvalue("amp","stim2.amp", 1,"", 0, 1 )
stim2.i = 0
xvalue("i","stim2.i", 0,"", 0, 1 )
xpanel(978,-1)
}
{
xpanel("AlphaSynapse[0] at soma(0.166667)", 0)
xlabel("AlphaSynapse[0] at soma(0.166667)")
Asynm.onset = 500
xvalue("onset","Asynm.onset", 1,"", 0, 1 )
Asynm.tau = 15
xvalue("tau","Asynm.tau", 1,"", 0, 1 )
Asynm.gmax = 3.45e-05
xvalue("gmax","Asynm.gmax", 1,"", 0, 1 )
Asynm.e = 0
xvalue("e","Asynm.e", 1,"", 0, 1 )
Asynm.i = -0
xvalue("i","Asynm.i", 0,"", 0, 1 )
xpanel(978,212)
}
{
xpanel("ExpSyn[0] at soma(0)", 0)
xlabel("ExpSyn[0] at soma(0)")
syn.tau = 15
xvalue("tau","syn.tau", 1,"", 0, 1 )
syn.e = 0
xvalue("e","syn.e", 1,"", 0, 1 )
syn.i = -9.07236e-12
xvalue("i","syn.i", 0,"", 0, 1 )
syn.g = 1.1705e-13
xvalue("g","syn.g", 0,"", 0, 1 )
xpanel(979,667)
}
{
xpanel("NetStim[0] at soma(0.166667)", 0)
xlabel("NetStim[0] at soma(0.166667)")
mnoise.interval = 7
xvalue("interval","mnoise.interval", 1,"", 0, 1 )
mnoise.number = 6
xvalue("number","mnoise.number", 1,"", 0, 1 )
mnoise.start = 500
xvalue("start","mnoise.start", 1,"", 0, 1 )
mnoise.noise = 1
xvalue("noise","mnoise.noise", 1,"", 0, 1 )
xpanel(980,456)
}
{
xpanel("RunControl", 0)
v_init = -77.4
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 = 800
xvalue("t","t", 2 )
tstop = 800
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.025
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 = 2.98
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(105,139)
}
{
xpanel("soma(0 - 1) (Parameters)", 0)
xlabel("soma(0 - 1) (Parameters)")
xlabel("nseg = 3")
soma.L = 5.6419
xvalue("L","soma.L", 1,"", 0, 0 )
soma.cm = 1
xvalue("cm","soma.cm", 1,"", 0, 0 )
soma.diam = 5.6419
xvalue("diam","soma.diam", 1,"", 0, 0 )
soma.gnabar_Nam = 0.035
xvalue("gnabar_Nam","soma.gnabar_Nam", 1,"", 0, 0 )
soma.gkmbar_Km = 0.006
xvalue("gkmbar_Km","soma.gkmbar_Km", 1,"", 0, 0 )
soma.gl_Leakm = 7.5e-05
xvalue("gl_Leakm","soma.gl_Leakm", 1,"", 0, 0 )
soma.el_Leakm = -75
xvalue("el_Leakm","soma.el_Leakm", 1,"", 0, 0 )
soma.gkirmbar_Kirm = 0.00015
xvalue("gkirmbar_Kirm","soma.gkirmbar_Kirm", 1,"", 0, 0 )
soma.gkafmbar_KAfm = 9e-05
xvalue("gkafmbar_KAfm","soma.gkafmbar_KAfm", 1,"", 0, 0 )
soma.gkasmbar_KAsm = 0.00032
xvalue("gkasmbar_KAsm","soma.gkasmbar_KAsm", 1,"", 0, 0 )
soma.gkrpmbar_Krpm = 0.00042
xvalue("gkrpmbar_Krpm","soma.gkrpmbar_Krpm", 1,"", 0, 0 )
soma.gnapmbar_NaPm = 2e-05
xvalue("gnapmbar_NaPm","soma.gnapmbar_NaPm", 1,"", 0, 0 )
soma.gnasmbar_NaSm = 0.00011
xvalue("gnasmbar_NaSm","soma.gnasmbar_NaSm", 1,"", 0, 0 )
xpanel(247,90)
}
{
save_window_ = new Graph(0)
save_window_.size(0,800,-80,40)
scene_vector_[3] = save_window_
{save_window_.view(0, -80, 800, 120, 564, -2, 300.6, 200.8)}
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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