Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010)

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Accession:140789
Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. ... To quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. Both specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. Furthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. ... Further computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. These properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.
Reference:
1 . Nörenberg A, Hu H, Vida I, Bartos M, Jonas P (2010) Distinct nonuniform cable properties optimize rapid and efficient activation of fast-spiking GABAergic interneurons. Proc Natl Acad Sci U S A 107:894-9 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite;
Brain Region(s)/Organism: Hippocampus; Dentate gyrus;
Cell Type(s): Dentate gyrus basket cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Detailed Neuronal Models;
Implementer(s): Matthia (Norenberg), Anja [anja.matthiae at charite.de];
// specifiy-BC6.hoc

ax=2079 // number of axonal sections
de=205  // number of dend sections (basal)
ap=432  // number of apic sections (apical)

objref all, somadend, axonal, apical, basal
proc secdef() {
	all = new SectionList()
		soma all.append()
		for i=0, ax-1 axon[i] all.append()
		for i=0, de-1 dend[i] all.append()
		for i=0, ap-1 apic[i] all.append()

	somadend = new SectionList()
		soma somadend.append()
		for i=0, de-1 dend[i] somadend.append()
		for i=0, ap-1 apic[i] somadend.append()

	axonal = new SectionList()
	for i=0, ax-1 axon[i] axonal.append()

	apical = new SectionList()
	for i=0, ap-1 apic[i] apical.append()

	basal = new SectionList()
	for i=0, de-1 dend[i] basal.append()
}

topol()
secdef()

// Segmentation ----------------------------------------------------------
// The number of segments per section (nseg) was set according to the "d-lambda rule" (see Carnevale and Hines 2006, The NEURON Book, Cambridge Univ Press ). The alternating current length constant at 1 kHz ,lambda(1kHz), was calculated for each section, and nseg was increased until the length of all individual segments was <10% of lambda(1kHz), assuming Rm = 10 kOhm cm2, Cm = 1 µF cm-2, and Ri = 100 Ohm cm. Nseg was constrained to odd numbers;
strdef NsegFile
NsegFile="NsegOut-BC6.txt"
objref AllNseg
AllNseg=new Vector(1+ax+de+ap)
ropen(NsegFile)

for(i=0;i<AllNseg.size;i=i+1) {
	AllNseg.x[i]=fscan()
}

proc geom_nseg() {
   soma.nseg = AllNseg.x[0]
   axon.nseg = AllNseg.x[1]
   for i=1,ax-1 axon[i].nseg = AllNseg.x[i+1]
   dend.nseg = AllNseg.x[ax+1]
   for i=1,de-1 dend[i].nseg = AllNseg.x[i+ax+1]
   apic.nseg = AllNseg.x[ax+de+1]
   for i=1,ap-1 apic[i].nseg = AllNseg.x[i+ax+de+1]
   print "Segmentation defined."
}

geom_nseg()

//biophysics --------------------------------------------------------
proc biophys() {
	forsec all {
		Ra = $2
		cm = $3
		g_pas = 1/$1
	}
}

forsec all {
	insert pas
	e_pas = v_init
}

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