Voltage imaging calibration in tuft dendrites of mitral cells (Djurisic et al 2004)

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Accession:253991
A detailed morphology of a tuft is provided in a reconstruction of a mitral cell that was used to place simulated estimates on for the calibration of EPSPs as recorded in voltage imaging in the real cells (estimated to be within +12% to -18% of the actual amplitude).
Reference:
1 . Djurisic M, Antic S, Chen WR, Zecevic D (2004) Voltage imaging from dendrites of mitral cells: EPSP attenuation and spike trigger zones. J Neurosci 24:6703-14 [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: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
Model Concept(s): Methods;
Implementer(s):
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell;
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DjurisicEtAl2004
readme.html
EPSPClamp.mod *
avgdiams1.txt *
avgdiams2.txt *
avgdiams3.txt *
avgdiams4.txt *
avgdiams5.txt *
basediams1.txt *
basediams2.txt *
basediams3.txt *
basediams4.txt *
basediams5.txt *
epsp.txt *
epsp_soma.txt *
lengths1.txt *
lengths2.txt *
lengths3.txt *
lengths4.txt *
lengths5.txt *
membrane.hoc
morphology.hoc
mosinit.hoc
nltemplate.hoc
order1.txt *
order2.txt *
order3.txt *
order4.txt *
order5.txt *
parameters.hoc *
screenshot1.png
screenshot2.png
test.ses
xyangles1.txt *
xyangles2.txt *
xyangles3.txt *
xyangles4.txt *
xyangles5.txt *
xyz1.txt *
xyz2.txt *
xyz3.txt *
xyz4.txt *
xyz5.txt *
zangles1.txt *
zangles2.txt *
zangles3.txt *
zangles4.txt *
zangles5.txt *
                            
load_file("nrngui.hoc")
objectvar save_window_, rvp_
objectvar scene_vector_[10]
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(0,100,-55,-41)
scene_vector_[4] = save_window_
{save_window_.view(0, -55, 100, 14, 96, -1, 300.6, 163.9)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[44].v(0.75)", 2, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-55,-46.3)
scene_vector_[5] = save_window_
{save_window_.view(0, -55, 100, 8.7, 94, 278, 300.6, 160.3)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[46].v(0.75)", 3, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-55,-46.1)
scene_vector_[6] = save_window_
{save_window_.view(0, -55, 100, 8.9, 99, 558, 297.9, 159.4)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[35].v(0.5)", 4, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-55,-49.2)
scene_vector_[7] = save_window_
{save_window_.view(0, -55, 100, 5.8, 514, 560, 300.6, 166.6)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[28].v(0.625)", 7, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-55,-49.2)
scene_vector_[8] = save_window_
{save_window_.view(0, -55, 100, 5.8, 513, 278, 300.6, 164.8)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[20].v(0.75)", 6, 1, 0.8, 0.9, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,100,-55,-49.2)
scene_vector_[9] = save_window_
{save_window_.view(0, -55, 100, 5.8, 513, 1, 300.6, 161.2)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("tuft_nl.sect[13].v(0.75)", 5, 1, 0.8, 0.9, 2)
}
objectvar scene_vector_[1]
{doNotify()}

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