Mathematical model for windup (Aguiar et al. 2010)

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Accession:128559
"Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..."
Reference:
1 . Aguiar P, Sousa M, Lima D (2010) NMDA channels together with L-type calcium currents and calcium-activated nonspecific cationic currents are sufficient to generate windup in WDR neurons. J Neurophysiol 104:1155-66 [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): Wide dynamic range neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I N; I K; I K,Ca;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Activity Patterns; Action Potentials;
Implementer(s):
Search NeuronDB for information about:  GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I N; I K; I K,Ca;
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WDR-Model
readme.html
AMPA_DynSyn.mod
CaIntraCellDyn.mod
GABAa_DynSyn.mod *
GABAb_DynSyn.mod *
HH2.mod *
iCaAN.mod *
iCaL.mod *
iKCa.mod *
iNaP.mod *
mGluR_DynSyn.mod
NK1_DynSyn.mod *
NMDA_DynSyn.mod *
herreroscatter.m
interneuron.hoc *
loadsynapticcurrents.m
mosinit.hoc
screenshot.jpg
WDR.hoc
wdr_spike_times.dat *
wdr-complete-model.hoc
wdr-complete-model.ses
wdr-complete-model-exportsyns.hoc
                            
TITLE GABA_A receptor with pre-synaptic short-term plasticity 


COMMENT
GABA_A receptor conductance using a dual-exponential profile
Pre-synaptic short-term plasticity based on Fuhrmann et al, 2002

Written by Paulo Aguiar and Mafalda Sousa, IBMC, May 2008
pauloaguiar@fc.up.pt ; mafsousa@ibmc.up.pt
ENDCOMMENT



NEURON {
	POINT_PROCESS GABAa_DynSyn	
	RANGE tau_rise, tau_decay
	RANGE U1, tau_rec, tau_fac
	RANGE i, g, e
	NONSPECIFIC_CURRENT i
}

PARAMETER {
	tau_rise  = 1.0   (ms)  : dual-exponential conductance profile
	tau_decay = 20.0  (ms)  : IMPORTANT: tau_rise < tau_decay
	U1        = 1.0   (1)   : The parameter U1, tau_rec and tau_fac define _
	tau_rec   = 0.1   (ms)  : the pre-synaptic short-term plasticity _
	tau_fac   = 0.1   (ms)  : mechanism (see Fuhrmann et al, 2002)
	e         = -80.0 (mV)  : GABAa synapse reversal potential
}
     

ASSIGNED {
	v (mV)
	i (nA)
	g (umho)
	factor
}

STATE {
	A	: state variable to construct the dual-exponential profile
	B	: 
}

INITIAL{
	LOCAL tp
	A = 0
	B = 0
	tp = (tau_rise*tau_decay)/(tau_decay-tau_rise)*log(tau_decay/tau_rise)
	factor = -exp(-tp/tau_rise)+exp(-tp/tau_decay)
	factor = 1/factor
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	g = B-A
	i = g*(v-e)
}

DERIVATIVE state{
	A' = -A/tau_rise
	B' = -B/tau_decay
}

NET_RECEIVE (weight, Pv, P, Use, t0 (ms)){
	INITIAL{
		P=1
		Use=0
		t0=t
	    }	
	Use = Use * exp(-(t-t0)/tau_fac)
	Use = Use + U1*(1-Use) 
	P   = 1-(1- P) * exp(-(t-t0)/tau_rec)
	Pv  = Use * P
	P   = P - Use * P
	
	t0 = t
	
	A = A + weight*factor*Pv
	B = B + weight*factor*Pv	
}

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