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

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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.
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]
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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;
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-]; Romani, Armando [romani.armando -at-];
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;
cad.mod *
cagk.mod *
cal.mod *
calH.mod *
car.mod *
cat.mod *
d3.mod *
h.mod *
kadist.mod *
kaprox.mod *
kca.mod *
kdr.mod *
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na3.mod *
na3dend.mod *
na3notrunk.mod *
nap.mod *
nax.mod *
netstimmm.mod *
somacar.mod *
for i = 0, nSyn-1 {


	while (!SELECTED) {
		//select section (sec) using apical_non_trunk_list
		//repick a random number
		sec=int(ransec.repick())	//select section
		dend[sec].sec {

			//select location (loc)
			ranseg.uniform(1, nseg+1)	//generate distribution for selecting segments
			tmpnseg = int(ranseg.repick())	//repick a random number
			loc = (2*tmpnseg - 1)/(2*nseg)

			//check if the distance is correct, otherwise start from the beginning
			xdist = find_vector_distance_precise(secname(),loc)
			if ((xdist > min_distance) && (xdist < max_distance)) {SELECTED=1}
		}	//exit from section
	}	//close while loop

	//locate synapses
	dend[sec].sec {
          syn[i] = new tmgsyn(loc)

          //insert NetStim
		nstim[i] = new NetStim(0.5)
		netcon[i] = new NetCon(nstim[i],syn[i])

		flagW = 0
		while (!flagW) {
			wei = 5+ranwei.repick()  //x0=5 for fitting data by Ito & Schuman
			if (wei > 0) {
				netcon[i].weight = 100*wei/(70*1000)
        //divide by 70 mV
        //divide by 1000 to change from nS to uS
        //multiply by a factor to simulate multiple sinapses

				flagW = 1

	}	//close locate synapses

}	//close main loop