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")

xpanel("Figures")
xradiobutton("Fig 1", "restart(\"fig1\")")
xradiobutton("Fig 3,4", "restart(\"fig34\")")
xradiobutton("Fig 5", "restart(\"fig5\")")
xradiobutton("Fig 7", "restart(\"fig7\")")
xradiobutton("Fig 8", "restart(\"fig8\")")
xradiobutton("Fig 9", "restart(\"fig9\")")
xpanel(5, 100)

pwmcnt = PWManager[0].count

restarting_=0
proc restart() { local i
	if (restarting_) { return }
	restarting_ = 1
	objref graphItem, save_window_
	for i=0, n_graph_lists-1 {
		graphList[i].remove_all()
	}
	flush_list.remove_all()
	fast_flush_list.remove_all()
	doNotify()
	for (i= PWManager[0].count-1; i >= pwmcnt; i -= 1) {
		PWManager[0].close(i)
		doNotify()
	}
	forall delete_section()
	stoprun = 0
	cvode_active(0)
	secondorder=0
	dt = .025
	steps_per_ms = 40
	tstop = 5
	sprint(tstr, "%s.hoc", $s1)
	load_file(1, tstr)
	restarting_ = 0
}


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