Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)

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Accession:53451
A heuristic for compartmentalization based on the space constant at 100 Hz is proposed. The paper also discusses spatio/temporal accuracy and the use of CVODE.
Reference:
1 . Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123-35 [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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Tutorial/Teaching; Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[6]
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 = -70
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 = 1000
xvalue("t","t", 2 )
tstop = 1000
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 5.6778
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 = 19.03
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(75,85)
}
{
save_window_ = new Graph(0)
save_window_.size(0,1000,-80,20)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 1000, 100, 610, 2, 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)
}

//Begin VariableTimeStep
{
ocbox_ = NumericalMethodPanel[0]
}
{object_push(ocbox_)}
{
atol_ = 0.0025  CVode[0].atol(atol_)
restore(301, 1)
 atoltool_ = new AtolTool()
    ats("cai", 1e-04)
    ats("Vector", -1)
 atoltool_.scales()
}
{object_pop()}
{
ocbox_.map("VariableTimeStep", 74, 484, 272.64, 113.28)
}
objref ocbox_
//End VariableTimeStep

{
save_window_ = new PlotShape(0)
save_window_.size(-1025.81,291.82,-340.765,974.763)
save_window_.variable("v")
scene_vector_[3] = save_window_
{save_window_.view(-1025.81, -340.765, 1317.63, 1315.53, 379, 0, 200.64, 200.32)}
fast_flush_list.append(save_window_)
save_window_.save_name("fast_flush_list.")
}
{
save_window_ = new Graph(0)
save_window_.size(0,1000,-3,1)
scene_vector_[4] = save_window_
{save_window_.view(0, -3, 1000, 4, 610, 270, 300.48, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("log10(dt+1e-10)", 1, 1, 0.352716, 0.952714, 2)
}
{
save_window_ = new Graph(0)
save_window_.size(0,1000,0,5)
scene_vector_[5] = save_window_
{save_window_.view(0, 0, 1000, 5, 609, 550, 300.48, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addexpr("cvode.order()", 1, 1, 0.384665, 0.928755, 2)
}
objectvar scene_vector_[1]
{doNotify()}

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