ModelDB is moving. Check out our new site at https://modeldb.science. The corresponding page is https://modeldb.science/147867.

Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)

 Download zip file 
Help downloading and running models
Accession:147867
These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied neuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.
Reference:
1 . Narayanan R, Chattarji S (2010) Computational analysis of the impact of chronic stress on intrinsic and synaptic excitability in the hippocampus. J Neurophysiol 103:3070-83 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse; Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s): I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Conductance distributions;
Implementer(s): Narayanan, Rishikesh [rishi at iisc.ac.in];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; AMPA; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP; Glutamate;
/
CA3Atrophy
Input
README.html
ampa.mod
borgkm.mod *
cadiv.mod *
cagk.mod *
cal2.mod *
can2.mod *
cat.mod *
h.mod
kad.mod
kahp.mod *
kap.mod
kdr.mod *
nahh.mod *
0.png
25.png
35.png
75.png
Fig1D.hoc
Fig2D-E.hoc
Fig2F-G.hoc
Menu.png
mosinit.hoc
neuron.que
Neurons.inp
                            
TITLE K-A Proximal channel 
: M.Migliore et al. Jun 1997

NEURON {
	SUFFIX kap
	USEION k WRITE ik
	RANGE gbar,gka,ik
	GLOBAL ninf,linf,taul,taun,lmin
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	v (mV)
	celsius = 24	(degC)
	gbar=.008 (mho/cm2)
	vhalfn=11   (mV)
	vhalfl=-56   (mV)
	a0l=0.05      (/ms)
	a0n=0.05    (/ms)
	zetan=-1.5    (1)
	zetal=3    (1)
	gmn=0.55   (1)
	gml=1   (1)
	lmin=2  (mS)
	nmin=0.1  (mS)
	pw=-1    (1)
	tq=-40
	qq=5
	q10=5
	qtl=1
}

STATE {
	n
	l
}

ASSIGNED {
	ik (mA/cm2)
	ninf
	linf      
	taul
	taun
	gka
}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gka = gbar*n*l
	ik = gka*(v+90.0)
}


FUNCTION alpn(v(mV)) {
LOCAL zeta
	zeta=zetan+pw/(1+exp((v-tq)/qq))
	alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
LOCAL zeta
	zeta=zetan+pw/(1+exp((v-tq)/qq))
	betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION alpl(v(mV)) {
	alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

DERIVATIVE states {
	rates(v)
	n' = (ninf - n)/taun
	l' = (linf - l)/taul
}

PROCEDURE rates(v (mV)) {
LOCAL a,qt
	qt=q10^((celsius-24)/10)
	a = alpn(v)
	ninf = 1/(1 + a)
	taun = betn(v)/(qt*a0n*(1+a))
	if (taun<nmin) {
		taun=nmin
	}
	a = alpl(v)
	linf = 1/(1+ a)
	taul = 0.26*(v+50)/qtl
	if (taul<lmin/qtl) {
		taul=lmin/qtl
	}
}

Loading data, please wait...