MEG of Somatosensory Neocortex (Jones et al. 2007)

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Accession:113732
"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
Reference:
1 . Jones SR, Pritchett DL, Stufflebeam SM, Hämäläinen M, Moore CI (2007) Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study. J Neurosci 27:10751-64 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I T low threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I R;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Magnetoencephalography; Touch;
Implementer(s): Sikora, Michael [Sikora at umn.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; GabaB; AMPA; NMDA; I T low threshold; I K; I M; I K,Ca; I Sodium; I Calcium; I R; Gaba; Glutamate;
objectvar save_window_, rvp_
objectvar scene_vector_[8]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(5,28,1)}
{
xpanel("RunControl", 0)
v_init = -65
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 200
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 = 160
xvalue("t","t", 2 )
tstop = 160
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 = 8.34
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(43,209)
}
{
save_window_ = new Graph(0)
save_window_.size(0,170,-21000,29000)
scene_vector_[2] = save_window_
{save_window_.view(0, -21000, 170, 50000, 779, 54, 297.6, 161.92)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("L2_dipole()", 1, 1, 0.229685, 0.216161, 2)
save_window_.addexpr("L5_dipole()", 2, 1, 0.601753, 0.295671, 2)
save_window_.label(0.34107, 0.896827, "MEG (fAm)", 2, 1, 0, 0, 1)
save_window_.label(0.301158, 0.79531, "Layer contribution", 2, 1, 0, 0, 1)
}
{
save_window_ = new Graph(0)
save_window_.size(0,170,-2200,2900)
scene_vector_[3] = save_window_
{save_window_.view(0, -2200, 170, 5100, 442, 290, 310.08, 178.24)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("dset(PL5[0].all)", 2, 1, 0.203158, 0.188936, 2)
save_window_.addexpr("dset(PL5[0].apical)", 3, 1, 0.197484, 0.359881, 2)
save_window_.addexpr("dset(PL5[0].basal)", 4, 1, 0.19704, 0.520599, 2)
save_window_.label(0.220447, 0.924658, "Single Layer 5  cell Diplole (fAm)", 2, 1, 0, 0, 1)
}
{
save_window_ = new Graph(0)
save_window_.size(0,170,-19000,30000)
scene_vector_[4] = save_window_
{save_window_.view(0, -19000, 170, 49000, 441, 59, 309.12, 160)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("L2_dipole() + L5_dipole()", 1, 1, 0.187523, 0.124056, 2)
save_window_.label(0.33519, 0.84715, "MEG (fAm)", 2, 1, 0, 0, 1)
}
{
xpanel("NetStimG[0] at acell_home_(0.5)", 0)
xlabel("NetStimG[0] at acell_home_(0.5)")
FeedX[0].pp.MeanInterval = 1
xvalue("MeanInterval","FeedX[0].pp.MeanInterval", 1,"", 0, 1 )
FeedX[0].pp.SD = 0.5
xvalue("SD","FeedX[0].pp.SD", 1,"", 0, 1 )
FeedX[0].pp.MeanStart = 25
xvalue("MeanStart","FeedX[0].pp.MeanStart", 1,"", 0, 1 )
FeedX[0].pp.StartSD = 2.5
xvalue("StartSD","FeedX[0].pp.StartSD", 1,"", 0, 1 )
FeedX[0].pp.number = 1
xvalue("number","FeedX[0].pp.number", 1,"", 0, 1 )
FeedX[0].pp.noise = 1
xvalue("noise","FeedX[0].pp.noise", 1,"", 0, 1 )
FeedX[0].pp.interval = 1.74866
xvalue("interval","FeedX[0].pp.interval", 0,"", 0, 1 )
xpanel(26,793)
}
{
xpanel("NetStimG[1] at acell_home_(0.5)", 0)
xlabel("NetStimG[1] at acell_home_(0.5)")
FeedX[1].pp.MeanInterval = 1
xvalue("MeanInterval","FeedX[1].pp.MeanInterval", 1,"", 0, 1 )
FeedX[1].pp.SD = 1
xvalue("SD","FeedX[1].pp.SD", 1,"", 0, 1 )
FeedX[1].pp.MeanStart = 70
xvalue("MeanStart","FeedX[1].pp.MeanStart", 1,"", 0, 1 )
FeedX[1].pp.StartSD = 6
xvalue("StartSD","FeedX[1].pp.StartSD", 1,"", 0, 1 )
FeedX[1].pp.number = 1
xvalue("number","FeedX[1].pp.number", 1,"", 0, 1 )
FeedX[1].pp.noise = 1
xvalue("noise","FeedX[1].pp.noise", 1,"", 0, 1 )
FeedX[1].pp.interval = 0.79655
xvalue("interval","FeedX[1].pp.interval", 0,"", 0, 1 )
xpanel(304,792)
}
{
xpanel("NetStimG[2] at acell_home_(0.5)", 0)
xlabel("NetStimG[2] at acell_home_(0.5)")
FeedX[2].pp.MeanInterval = 1
xvalue("MeanInterval","FeedX[2].pp.MeanInterval", 1,"", 0, 1 )
FeedX[2].pp.SD = 1
xvalue("SD","FeedX[2].pp.SD", 1,"", 0, 1 )
FeedX[2].pp.MeanStart = 135
xvalue("MeanStart","FeedX[2].pp.MeanStart", 1,"", 0, 1 )
FeedX[2].pp.StartSD = 7
xvalue("StartSD","FeedX[2].pp.StartSD", 1,"", 0, 1 )
FeedX[2].pp.number = 1
xvalue("number","FeedX[2].pp.number", 1,"", 0, 1 )
FeedX[2].pp.noise = 1
xvalue("noise","FeedX[2].pp.noise", 1,"", 0, 1 )
FeedX[2].pp.interval = 1.70261
xvalue("interval","FeedX[2].pp.interval", 0,"", 0, 1 )
xpanel(575,793)
}
{
save_window_ = new Graph(0)
save_window_.size(0,170,-800,900)
scene_vector_[5] = save_window_
{save_window_.view(0, -800, 170, 1700, 784, 287, 297.6, 175.36)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("dset(PL2[0].apical)", 3, 1, 0.250479, 0.272205, 2)
save_window_.addexpr("dset(PL2[0].basal)", 4, 1, 0.253674, 0.435144, 2)
save_window_.addexpr("dset(PL2[0].all)", 2, 1, 0.253674, 0.344089, 2)
save_window_.label(0.246006, 0.915335, "Single Layer 2/3 cell Dipole (fAm)", 2, 1, 0, 0, 1)
}
{
save_window_ = new Graph(0)
save_window_.size(0,160,-100,60)
scene_vector_[6] = save_window_
{save_window_.view(0, -100, 160, 160, 443, 540, 306.24, 172.48)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("PL5[0].soma.v( 0.5 )", 2, 1, 0.8, 0.9, 2)
save_window_.addexpr("PL2[0].soma.v( 0.5 )", 1, 1, 0.8, 0.9, 2)
save_window_.label(0.249021, 0.909147, "Somatic Potential (mV)", 2, 1, 0, 0, 1)
save_window_.label(0.265296, 0.0712626, "Single Layer 2/3 & 5 cells", 2, 1, 0, 0, 1)
}
objectvar scene_vector_[1]
{doNotify()}

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