CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)

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Accession:71312
The model shows how different input patterns (irregular & asynchronous, irregular & synchronous, regular & asynchronous, regular & synchronous) affect the neuron's output rate when 1000 synapses are distributed in the proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron.
References:
1 . Li X, Ascoli GA (2006) Computational simulation of the input-output relationship in hippocampal pyramidal cells J of Comput Neurosci 21(2):191-209 [PubMed]
2 . Li X, Ascoli GA (2008) Effects of synaptic synchrony on the neuronal input-output relationship. Neural Comput 20:1717-31 [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:
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Synaptic Integration;
Implementer(s): Li, Xiaoshen [xsli2 at yahoo.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; AMPA; NMDA; I Na,t; I A; I K; I h;
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xiaoshenli
readme.txt
distr.mod *
h.mod *
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
c20466.hoc
createNewSyn.hoc
createNewSyn2.hoc
exec.hoc
fig4A.hoc
fixnseg.hoc *
ModelTypeC.hoc
ModelTypeI.hoc
mosinit.hoc
NregNsyn.hoc
Nregsyn.hoc
regNsyn.hoc *
                            
/************************************************************************************
Flag MODELTYPE to decide if Type I or Type C model to be loaded and createNewSyn.hoc
& createNewSyn2.hoc set syn wt correspondly;
0: Type I Model
1: Type C Model
2: Passive Model
*************************************************************************************/
printf("regular & asynchronous input. frequency = %d \n", freq)

objref r
r = new Random()

for i=0, totSyn-1 {  //totSyn is the total # of synapses
	Ens[i].start=0 //Ens: the reference to the NetStim
	Ens[i].number=1000000000000000000000000000000
	Ens[i].noise=0
}

intv = 1000/freq
for i=0, totSyn-1 {
	Ens[i].interval = intv //set each syn. wt
	Ens[i].start = r.uniform(0, intv)
}
run()

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