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
                            
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON 
{
	POINT_PROCESS AMPA
	RANGE C, g, gmax, lastrelease, TRise, tau
	NONSPECIFIC_CURRENT i
	RANGE Cmax, Cdur, Alpha, Beta, Erev, Deadtime
}

UNITS 
{
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER 
{
	TRise  	= 2 (ms)
	tau    	= 2(ms)
	Cmax	= 1	(mM)		: max transmitter concentration
	Erev	= 0	(mV)		: reversal potential
	Deadtime = 1	(ms)		: mimimum time between release events
	gmax	= 0		(umho)		: maximum conductance
}


ASSIGNED 
{
	Alpha	(/ms mM)	: forward (binding) rate
	Beta	(/ms)		: backward (unbinding) rate
	Cdur	(ms)		: transmitter duration (rising phase)
	v		(mV)		: postsynaptic voltage
	i 		(nA)		: current = g*(v - Erev)
	g 		(umho)		: conductance
	C		(mM)		: transmitter concentration
	lastrelease	(ms)		: time of last spike
}

STATE
{
	R				: fraction of open channels
}

INITIAL 
{
	R = 0
	C = 0
	lastrelease = -1000
	Cdur=TRise
	Beta=1/tau
	Alpha=1/Cdur - Beta
}

BREAKPOINT 
{
	SOLVE states METHOD cnexp
	g = (gmax * R * (Alpha+Beta)) / (Alpha*(1-1/exp(1)))
	i = g*(v - Erev)
}

DERIVATIVE states
{
	evaluateC() 	: Find out value of C
	R'=Alpha * C * (1-R) - Beta * R
}

PROCEDURE evaluateC()
{
	LOCAL q
	q = ((t - lastrelease) - Cdur)		: time since last release ended
	if (q >= 0 && q <= Deadtime && C == Cmax) {	: in dead time after release
		C = 0.
	}
}

NET_RECEIVE (weight (umho)) 
{ 
	LOCAL q
	q = ((t - lastrelease) - Cdur)		: time since last release ended

: Spike has arrived, ready for another release?

	if (q > Deadtime) {
		C = Cmax			: start new release
		lastrelease = t
	} 
}


Loading data, please wait...