DRt neuron model (Sousa et al., 2014)

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Accession:151949
Despite the importance and significant clinical impact of understanding information processing in the nociceptive system, the functional properties of neurons in many parts of this system are still unknown. In this work we performed whole-cell patch-clamp recording in rat brainstem blocks to characterize the electrophysiological properties of neurons in the dorsal reticular nucleus (DRt), a region known to be involved in pronociceptive modulation. We also compared properties of DRt neurons with those in the adjacent parvicellular reticular nucleus (PCRt) and in neighboring regions outside the reticular formation. We found that neurons in the DRt and PCRt had similar electrophysiological properties and exhibited mostly tonic-like firing patterns, whereas neurons outside the reticular formation showed a larger diversity of firing-patterns. The dominance of tonic neurons in the DRt supports previous conclusions that these neurons encode stimulus intensity through their firing frequency.
Reference:
1 . Sousa M, Szucs P, Lima D, Aguiar P (2014) The pronociceptive dorsal reticular nucleus contains mostly tonic neurons and shows a high prevalence of spontaneous activity in block preparation. J Neurophysiol 111:1507-18 [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): Hodgkin-Huxley neuron;
Channel(s): I Na,t; I K; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns;
Implementer(s): Aguiar, Paulo [pauloaguiar at fc.up.pt];
Search NeuronDB for information about:  I Na,t; I K; I K,Ca; I Calcium;
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

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