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.
1 . Li X, Ascoli GA (2006) Computational simulation of the input-output relationship in hippocampal pyramidal cells. J Comput Neurosci 21:191-209 [PubMed]
2 . Li X, Ascoli GA (2008) Effects of synaptic synchrony on the neuronal input-output relationship. Neural Comput 20:1717-31 [PubMed]
Channel(s): I Na,t; I A; I K; I h;
Model Concept(s): Detailed Neuronal Models; Synaptic Integration;
Implementer(s): Li, Xiaoshen [xsli2 at yahoo.com];
Search NeuronDB for information about: Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I Na,t; I A; I K; I h;
xiaoshenli
distr.mod *
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CA1 pyramidal neuron: integration of subthreshold inputs from PP and SC (Migliore 2003)
Estimation and Production of Time Intervals (Migliore et al 2001)
Excitability of PFC Basal Dendrites (Acker and Antic 2009)
Hippocampal Mossy Fiber bouton: presynaptic KV7 channel function (Martinello et al 2019)
Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)
Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)
Vomeronasal sensory neuron (Shimazaki et al 2006)
naxn.mod *
Other models using naxn.mod:
c20466.hoc
createNewSyn.hoc
createNewSyn2.hoc
exec.hoc
fig4A.hoc
fixnseg.hoc *
Other models using fixnseg.hoc:
ModelTypeC.hoc
ModelTypeI.hoc
NregNsyn.hoc
Nregsyn.hoc
regNsyn.hoc *
Other models using 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()