CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)

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Accession:151731
The paper discusses the effects induced by an electric field at power lines frequency.
Reference:
1 . Cavarretta F, Carnevale NT, Tegolo D, Migliore M (2014) Effects of low frequency electric fields on synaptic integration in hippocampal CA1 pyramidal neurons: implications for power line emissions. Front Cell Neurosci 8:310 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Extracellular;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Action Potentials; Synaptic Integration; Extracellular Fields;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu]; Cavarretta, Francesco [francescocavarretta at hotmail.it];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; I Na,t; I A; I K; I h; Glutamate;
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CavarrettaEtAl2014
readme.html
fzap.mod *
h.mod *
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
netstimm.mod *
xtrau.mod *
anatscale.hoc *
biophys.hoc
calcd.hoc
calcrxcu.hoc
efheader.hoc *
geo5038804.hoc *
init.hoc
interpxyzu.hoc *
mosinit.hoc *
prova.ses
rigc.ses
screenshot.png
setnseg.hoc *
setpointersu.hoc *
synapses.hoc
voltage.ses
zapstimu.hoc
                            
nsyn_dist = 50
nsyn_prox = 3 * nsyn_dist

cutdist = 350

nsyn = nsyn_dist + nsyn_prox

objref syn[nsyn], nc[nsyn], st[nsyn]

weight = 5e-5

for i = 0, nsyn - 1 {
	st[i] = new NetStimm(.5)
	st[i].interval = 1000 / 50
	st[i].number = 100000000000
	st[i].noise = 1
	st[i].start = 0
	st[i].seed(114)

	syn[i] = new Exp2Syn(.5)
	syn[i].e = 0
	syn[i].tau1 = 2
	syn[i].tau2 = 10

	nc[i] = new NetCon(st[i], syn[i], 0, 0, weight)
}

objref rcomp[2], rsec

use_mcell_ran4()
mcell_ran4_init()

rsec = new Random()
rsec.uniform(0, 1)

for i = 0, 1 rcomp[i] = new Random()
rcomp[0].uniform(0, numapical - 1)

/* distribuzione synapsi prossimali */
for i = 0, nsyn_prox - 1 {
	flag = 0
	while(flag == 0) {
		comp = int(rcomp[0].repick())
		tmp  = rsec.repick()
		apical_dendrite[comp] if(distance(tmp) < cutdist) flag = 1
	}
	apical_dendrite[comp] syn[i].loc(tmp)
}




objref seclst, rnd, secobj
seclst = new List()

forsec "user5" {
	secobj = new SectionRef()
	seclst.append(secobj)
}

forsec "apical" {
	secobj = new SectionRef()
	seclst.append(secobj)
}

rcomp[1].uniform(0, seclst.count() - 1)

for i = nsyn_prox, nsyn - 1 {
	flag = 0
	while(flag == 0) {
		comp = int(rcomp[1].repick())
		tmp  = rsec.repick()
		seclst.o(comp).sec if(distance(tmp) >= cutdist) flag = 1
	}
	seclst.o(comp).sec syn[i].loc(tmp)
}




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