Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)

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Accession:147757
The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive. Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease.
Reference:
1 . Romani A, Marchetti C, Bianchi D, Leinekugel X, Poirazi P, Migliore M, Marie H (2013) Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses. Front Comput Neurosci 7:1 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I M; I h; I Calcium; I_AHP;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Facilitation; Depression; Synaptic Integration; Aging/Alzheimer`s;
Implementer(s): Bianchi, Daniela [danielabianchi12 -at- gmail.com]; Romani, Armando [romani.armando -at- gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; I Na,t; I A; I K; I M; I h; I Calcium; I_AHP; Glutamate;
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RomaniEtAl2013
experiment
cad.mod *
cagk.mod *
cal.mod *
calH.mod *
car.mod *
cat.mod *
d3.mod *
h.mod *
kadist.mod *
kaprox.mod *
kca.mod *
kdr.mod *
km.mod *
na3.mod *
na3dend.mod *
na3notrunk.mod *
nap.mod *
nax.mod *
netstimmm.mod *
somacar.mod *
tmgsyn.mod
vecevent.mod
cell-setup.hoc
createNewSyn4.hoc
loadBasicModel.hoc
mosinit.hoc
session.ses
simulation.hoc
                            
TITLE T-calcium channel



UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)

	FARADAY = 96520 (coul)
	R = 8.3134 (joule/degC)
	KTOMV = .0853 (mV/degC)
}



NEURON {
	SUFFIX cat
	USEION ca READ cai,cao 
        USEION Ca WRITE iCa VALENCE 2
        : The T-current does not activate calcium-dependent currents.
        : The construction with dummy ion Ca prevents the updating of the 
        : internal calcium concentration. 
        RANGE gcatbar, hinf, minf, taum, tauh, iCa
}

PARAMETER {
	v (mV)
	
      tBase = 23.5  (degC)
	celsius = 22  (degC)
	gcatbar = 0   (mho/cm2)  : initialized conductance
	ki = 0.001    (mM)
	cai = 5.e-5   (mM)       : initial internal Ca++ concentration
	cao = 2       (mM)       : initial external Ca++ concentration
       tfa = 1                  : activation time constant scaling factor
       : tfi = 0.68 
        tfi = 0.68               : inactivation time constant scaling factor
        eca = 140                : Ca++ reversal potential

}


STATE {
	m h 
}

ASSIGNED {
      iCa (mA/cm2)
      gcat (mho/cm2)
	hinf
	tauh
	minf
	taum
}

INITIAL {
	rates(v)
	m = minf
	h = hinf
     gcat = gcatbar*m*m*h*h2(cai)

}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gcat = gcatbar*m*m*h*h2(cai)
	iCa = gcat*ghk(v,cai,cao)

}

DERIVATIVE states {	: exact when v held constant
	rates(v)
	m' = (minf - m)/taum
	h' = (hinf - h)/tauh
}


UNITSOFF
FUNCTION h2(cai(mM)) {
	h2 = ki/(ki+cai)
}

FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
        LOCAL nu,f

        f = KTF(celsius)/2
        nu = v/f
        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
}

FUNCTION KTF(celsius (DegC)) (mV) {
        KTF = ((25./293.15)*(celsius + 273.15))
}


FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}


FUNCTION alph(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	alph = 1.6e-4*exp(-(v+57)/19)
}

FUNCTION beth(v(mV)) {
        TABLE FROM -150 TO 150 WITH 200
	:beth = 1/(exp((-v+15)/10)+1.0)
      beth = 1/(exp((-v+15)/10)+1.0)
}

FUNCTION alpm(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	alpm = 0.1967*(-1.0*v+19.88)/(exp((-1.0*v+19.88)/10.0)-1.0)
}

FUNCTION betm(v(mV)) {
	TABLE FROM -150 TO 150 WITH 200
	betm = 0.046*exp(-v/22.73)
}


PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a
        a = alpm(v)
        taum = 1/(tfa*(a + betm(v))) : estimation of activation tau
        minf =  a/(a+betm(v))        : estimation of activation steady state
        a = alph(v)
        tauh = 1/(tfi*(a + beth(v))) : estimation of inactivation tau
        hinf = a/(a+beth(v))         : estimation of inactivation steady state
        
}

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