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];
// both functions load_curr() and load_exp():
// first parameter is the file to import
// second parameter is the time until the file should be read (tstop usually)
obfunc load_curr() {local i, ii localobj stimVec
	ropen($s1)
	for(ii=0;ii<1+3600;ii+=1) {fscan()}
	stimVec=new Vector($2/Dt_exp)
	for i=0, $2/Dt_exp-1 {
		stimVec.x[i]=fscan()/1000
	}
	ropen()
	return stimVec
}

obfunc load_exp() {local i,j,jj localobj yData
	ropen($s1)
	for(jj=0;jj<1+3600;jj+=1) {fscan()}
	yData = new Vector($2/Dt_exp)
	for i=0, $2/Dt_exp-1 {
		yData.x[i]=fscan()
	}
	ropen()	// closes the input file
	return yData
}

objref ExpData, shiftExpData
ExpData = new List()
shiftExpData = new List()

// load experimental data
objref xData_sh, xData_lo
xData_sh = new Vector(tstop_sh/Dt_exp)
xData_lo = new Vector(tstop_lo/Dt_exp)
xData_sh.indgen(Dt_exp)
xData_lo.indgen(Dt_exp)

shiftExpData.append(load_exp("ExpTrace-SomaShort.txt",tstop_sh))
shiftExpData.append(load_exp("ExpTrace-DendShort.txt",tstop_sh))
shiftExpData.append(load_exp("ExpTrace-SomaLong.txt",tstop_lo))
shiftExpData.append(load_exp("ExpTrace-DendLong.txt",tstop_lo))

// load of current data
objref CurrData //, timeVec
CurrData = new List()
CurrData.append(load_curr("InpTrace-SomaShort.txt",tstop_sh))
CurrData.append(load_curr("InpTrace-SomaLong.txt",tstop_lo))

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