Effects of spinal cord stimulation on WDR dorsal horn network (Zhang et al 2014)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:168414
" ... To study the mechanisms underlying SCS (Spinal cord stimulation), we constructed a biophysically-based network model of the dorsal horn circuit consisting of interconnected dorsal horn interneurons and a wide dynamic range (WDR) projection neuron and representations of both local and surround receptive field inhibition. We validated the network model by reproducing cellular and network responses relevant to pain processing including wind-up, A-fiber mediated inhibition, and surround receptive field inhibition. ..." See paper for more.
Reference:
1 . Zhang TC, Janik JJ, Grill WM (2014) Modeling effects of spinal cord stimulation on wide-dynamic range dorsal horn neurons: influence of stimulation frequency and GABAergic inhibition. J Neurophysiol 112:552-67 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Wide dynamic range neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Glutamate; Glycine;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Zhang, Tianhe [tz5@duke.edu];
Search NeuronDB for information about:  GabaA; AMPA; NMDA; Glutamate; Glycine;
/
ZhangEtAl2014
Critical Mod Files
AMPA_DynSyn.mod
B_A.mod
B_DR.mod
B_NA.mod
CaIntraCellDyn.mod *
GABAa_DynSyn.mod *
GABAb_DynSyn.mod *
Glycine_DynSyn.mod
HH2.mod *
HH2new.mod *
iCaAN.mod *
iCaL.mod
iKCa.mod *
iNaP.mod *
KDR.mod
KDRI.mod
NK1_DynSyn.mod *
NMDA_DynSyn.mod *
SS.mod
vsource.mod *
                            
TITLE Hippocampal HH channels
:
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:
: Modifications by Arthur Houweling for use in MyFirstNEURON
: Modifications by Paulo Aguiar: vh changed from 5 to 6 - NOT ANYMORE: vh=5 as originally set

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

NEURON {
	SUFFIX HH2
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub
	RANGE m_inf, h_inf, n_inf
	RANGE tau_m, tau_h, tau_n
	RANGE m_exp, h_exp, n_exp
	RANGE ik, ina 
}


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

PARAMETER {
	gnabar	= .1 	(mho/cm2)
	gkbar	= .06 	(mho/cm2)

	ena		(mV)
	ek		(mV)
	celsius		(degC)
	dt              (ms)
	v               (mV)
	vtraub	= -55	(mV)	: adjusts threshold
}

STATE {
	m h n
}

ASSIGNED {
	ina	(mA/cm2)
	ik	(mA/cm2)
	il	(mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}


BREAKPOINT {
	SOLVE states
	ina = gnabar * m*m*m*h * (v - ena)
	ik  = gkbar * n*n*n*n * (v - ek)
}


:DERIVATIVE states {   : use this for exact Hodgkin-Huxley equations
:	evaluate_fct(v)
:	m' = (m_inf - m) / tau_m
:	h' = (h_inf - h) / tau_h
:	n' = (n_inf - n) / tau_n
:}

PROCEDURE states() {	: this discretized form is more stable
	evaluate_fct(v)
	m = m + m_exp * (m_inf - m)
	h = h + h_exp * (h_inf - h)
	n = n + n_exp * (n_inf - n)
	VERBATIM
	return 0;
	ENDVERBATIM
}

UNITSOFF
INITIAL {
:
:  Q10 was assumed to be 3 for both currents
:
	tadj = 3.0 ^ ((celsius-36)/ 10 )
	evaluate_fct(v)
	m= m_inf
	h= h_inf
	n= n_inf
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2,vh

	v2 = v - vtraub : convert to traub convention
	vh = 5
	
	a = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1)
	b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	:a = 0.128 * exp((17-v2)/18)
	:b = 4 / ( 1 + exp((40-v2)/5) )
	:tau_h = 1 / (a + b) / tadj
	:h_inf = a / (a + b)
	
	a = 0.128 * exp((17-v2-vh)/18)
	b = 4 / ( 1 + exp((40-v2-vh)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)	
	
	
	a = 0.032 * (15-v2) / ( exp((15-v2)/5) - 1)
	b = 0.5 * exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)

	m_exp = 1 - exp(-dt/tau_m)
	h_exp = 1 - exp(-dt/tau_h)
	n_exp = 1 - exp(-dt/tau_n)
}

UNITSON

Loading data, please wait...