Effects of electric fields on cognitive functions (Migliore et al 2016)

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Accession:190559
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
Reference:
1 . Migliore R, De Simone G, Leinekugel X, Migliore M (2017) The possible consequences for cognitive functions of external electric fields at power line frequency on hippocampal CA1 pyramidal neurons. Eur J Neurosci 45:1024-1031 [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: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal 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; Detailed Neuronal Models; Action Potentials; Synaptic Integration; Extracellular Fields; Gamma oscillations; Pattern Recognition; Spatio-temporal Activity Patterns;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu]; Migliore, Rosanna [rosanna.migliore at cnr.it];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; AMPA; I Na,t; I A; I K; I h; Glutamate;
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MiglioreEJN2016
readme.html
distr.mod *
distr2.mod
fzap.mod *
Gfluct.mod
h.mod *
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
netstimm.mod *
xtrau.mod *
anatscale.hoc *
biophys.hoc
biophysPLAST.hoc
calcd.hoc
calcrxcu.hoc
ef_fig.png
efheader.hoc *
fig1B.hoc
fixnseg.hoc *
freq50.xfm
geo5038804.hoc *
init.hoc
interpxyzu.hoc *
LTDran.hoc
media-st er.xfm
mosinit.hoc
Napical.txt
no_ef_fig.png
Plast.hoc
setnseg.hoc *
setpointersu.hoc *
soma.hoc
synapses.hoc
voltage.ses
zapstimu.hoc
                            
load_file("nrngui.hoc")
cvode_active(1)
load_file("geo5038804.hoc")


numaxon=1
numsoma=1
numbasal  = 52
numapical = 70
numuser5  = 49
objref stim0, stim1, stim2, stim3, ncstim, syn, nc0[100], nc[100], nc2[100], bsyn[100]
objref g, b,c, distrx, distry, cdistry, p, xvec, r, rn, vec, weight, rx, xloc, dati

dati = new File()

load_file("biophysLTD.hoc")
load_file("efheader.hoc")

xvec = new Vector(100)
weight= new Vector(100)
xloc = new Vector(20)

for jj = 0, 14{
		xvec.x[jj]=jj
		weight.x[jj]=jj 
		xloc.x[jj]=jj/15
	}

seed=9 
r = new Random()
r.MCellRan4(seed) 

rn = new Random()
rn.MCellRan4(seed) 

rx = new Random()
rx.MCellRan4(seed) 

access soma

freq=50
//geom_nseg()
tot=0
forall {tot=tot+nseg}
distance()

tstop=13500

b = new VBox()
b.intercept(1)
g = new Graph()
g.size(0,tstop,-70,30)
g.addvar("soma[0].v(0.5)",1,1,2*tstop,0,2)
g.xaxis(1)
xpanel("")
xpanel()
b.intercept(0)
b.map()

p = new PlotShape()
p.exec_menu("Shape Plot")
p.size(-194.658,304.758,-223.667,609.667)
p.variable("v")
p.show(0)

proc init() {

  t=0

  
  finitialize(Vrest)

	forall {
		v = Vrest
		if(ismembrane("nax") || ismembrane("na3")) {
			ena=55
		}
		if(ismembrane("kdr") || ismembrane("kap") || ismembrane("kad")) {
			ek=-90
		}
		if(ismembrane("hd")) {
			ehd_hd=-30
		}
	}

	finitialize(Vrest)
	fcurrent()

	forall {
		for(x) {
			if(ismembrane("na3") || ismembrane("nax")) {
				e_pas(x)=v(x)+(ina(x)+ik(x))/g_pas(x)
			}
			if(ismembrane("hd")) {
				e_pas(x)=e_pas(x)+i_hd(x)/g_pas(x)
			}
		}
	cvode.re_init()
	cvode.event(tstop)
	access soma
	g.begin()
	}

soma {

stim0= new NetStim(.5)
stim0.number=1
stim0.interval=10
stim0.start=10

stim1= new NetStim(.5)
stim1.number=10
stim1.interval=1000
stim1.start=500

stim2 = new NetStim(.5)
stim2.number =2
stim2.interval =10
stim2.start =500

ncstim = new NetCon(stim1, stim2, 0, 0, 0.001)

stim3= new NetStim(.5)
stim3.number =1
stim3.interval =10
stim3.start =13000
}

proc synloc(){
	for ii=0, 14 { 
		apical_dendrite[xvec.x[ii]]{
			bsyn[xvec.x[ii]]= new giada2TBS(xloc.x[ii])
			bsyn[ii].loc(xloc.x[ii])
		}
	}
} 

synloc()

proc select() { 
	xvec.resize(0)
	weight.resize(0)
	xloc.resize(0)
	for jj = 0, 14 {
		xx = r.discunif(0, 69)
		//xvec.append(xx)
		yy = rn.uniform(0.5, 0.9)
		//weight.append(yy) if (yy < 0 ) {weight.x[jj]=0}
		zz = rx.uniform(0,1)
		//xloc.append(zz)
		while(xvec.contains(xx)) xx = r.repick()
		xvec.append(xx)
		while(weight.contains(yy)) yy = rn.repick()
		weight.append(yy) if (yy < 0 ) {weight.x[jj]=0}
		while(xloc.contains(zz)) zz = rx.repick()
		xloc.append(zz)
		print jj, xvec.x[jj], weight.x[jj], xloc.x[jj]
		}
synloc()
}

select()

for ii = 0, 14 { 
		nc0[ii]= new NetCon(stim0, bsyn[xvec.x[ii]], 0, 0, weight.x[ii]*1e-3)
		nc[ii] = new NetCon(stim1, bsyn[xvec.x[ii]], 0, 0, weight.x[ii]*1e-3)
		nc2[ii]= new NetCon(stim2, bsyn[xvec.x[ii]], 0, 0, weight.x[ii]*1e-3)
		}

proc advance() {
	fadvance()
	g.plot(t)
	g.flush()
	p.flush()
	doNotify()
	}
dati.aopen("ltd.txt")
proc runp() {
//run()
	distrx=new Vector()
	distry=new Vector()
	forsec "apical_dendrite" {
		for (x) if (x>0 && x<1) {
			if (diam>=0.) {
			distrx.append(distance(x)) 
			distry.append(vmax_ds(x)-Vrest)
			}
		}
	}
	distry.mark(c,distrx,"O",3,3,2)

	distrx=new Vector()
	distry=new Vector()
	forsec "user5" {
		for (x) if (x>0 && x<1) {
			if (diam>=0) {
			distrx.append(distance(x)) 
			distry.append(vmax_ds(x)-Vrest)
			}
		}
	}
	distry.mark(c,distrx,"t",5,$1,1)
	c.flush()
	doNotify()

}

proc runm() {
runp(1)
}
tstop=40000
run()
for jj=0,14{
dati.printf("%g\t%g\t%g\t%g\t%g\t%g\t%g\t%g\t\n", seed, soma[0].vmax1_ds2(0.5)+65,  soma[0].vmax2_ds2(0.5)+65, xvec.x[jj] , xloc.x[jj], weight.x[jj], apical_dendrite[xvec.x[jj]].vmax1_ds2(xloc.x[jj])+65, apical_dendrite[xvec.x[jj]].vmax2_ds2(xloc.x[jj])+65)
}
dati.close("ltd.txt")
//load_file("TBSall.ses")

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