Estimation and Production of Time Intervals (Migliore et al 2001)

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Accession:3167
NEURON model files from the paper M. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, Quantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.
Reference:
1 . Migliore M, Messineo L, Cardaci M, Ayala GF (2001) Quantitative modeling of perception and production of time intervals. J Neurophysiol 86:2754-60 [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):
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Simplified Models;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  AMPA; I Na,t; I A; I K;
/
timing
README.txt
kaprox.mod *
kdrca1.mod *
na3.mod *
pnoise.mod
mosinit.hoc
timing_db.hoc
                            
load_file("nrngui.hoc")
cvode.active(1)

Rm = 10000
CmSoma= 1
RaSoma=150  

Vrest = -65
dt = 0.05
gna =  .015
gkdr = 0.002
celsius = 34.0  
ka = 0.025
gavre=1.
gdeve=0. 
gavri=0.15
gdevi=0.0
sd=0

tstop=1000

objref nc, nci, g, b, rsyn, rsyni, apc

create soma, apical
connect soma(1), apical(0)
access soma

apc = new APCount(0.5)
apc.thresh=0

apical {
	rsyn = new pnoise(0.1)
	rsyni = new pnoise(0.1)
	nc = new NetCon(rsyn,rsyn)
	nci = new NetCon(rsyni,rsyni)
}

b = new VBox()
b.intercept(1)
g = new Graph()
g.size(0,tstop,-70,30)
g.addvar("soma.v(0.5)",2,1, 2*tstop,0,2)
g.color(1)
g.label(0.3,0.9,"production of a 0.5 sec interval")
g.xaxis(1)
g.begin()
xpanel("")
xbutton("run", "runu()")
xvalue("inhibition","gavri")
xpanel()
b.intercept(0)
b.map()

meant=50
rsyn.e=0
rsyn.alp=2.4
rsyn.eps=3   
rsyn.bet=1    
rsyn.count=0  
rsyn.start=0
rsyn.gavr=gavre
rsyn.meant=meant
rsyn.type=-1
rsyn.sd=sd

meanti=25
rsyni.e=-80   
rsyni.alp=3.5
rsyni.eps=20
rsyni.bet=1
rsyni.count=0
rsyni.start=0
rsyni.meant=meanti
rsyni.rfreq=0
rsyni.type=-1
rsyni.sd=0

soma {        L=10 diam=10 nseg=1
              insert na3  gbar_na3=gna ar2_na3=1
              insert kdr gkdrbar_kdr=gkdr 
              insert kap gkabar_kap = ka
              insert pas e_pas=Vrest g_pas = 1/Rm Ra=RaSoma cm=CmSoma
}
apical {      L=400 diam=1 nseg=10
              insert na3  gbar_na3=gna ar2_na3=1
              insert kdr gkdrbar_kdr=gkdr 
              insert kap gkabar_kap = ka
              insert pas e_pas=Vrest g_pas = 1/Rm Ra=RaSoma cm=CmSoma
}

proc init() {
	t=0
        forall {
        v=Vrest
	ena=55
	ek=-90
	}
	finitialize(v)
        fcurrent()
        forall {e_pas=v+(ina+ik)/g_pas}
	nc.event(0)
	nci.event(0)
	cvode.re_init()
}

proc step() {
	fadvance()
	g.plot(t)
	g.flush()
	doNotify()
}

proc run() {
	g.begin()
	init()
	while(apc.n<5 && t<tstop) { step()}
}


proc runu() {
	print " production of a 0.5 sec interval"
	sum=0
	k=0
	for i=0,4 {
	t=0
	rsyn.rfreq=0
	rsyni.gavr=gavri
	run()
	if (t<tstop) {
		k=k+1
		print " subject n. ",i+1," produced ",t/1000, " sec"
		sum=sum+t/1000
		} else {
		print " subject n. ",i+1,": simulation time too short "
	}
	}
	print " average interval produced: ",sum/k, " sec"
}



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