Thalamocortical augmenting response (Bazhenov et al 1998)

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Accession:37819
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
Reference:
1 . Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Computational models of thalamocortical augmenting responses. J Neurosci 18:6444-65 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell; Neocortex V1 pyramidal corticothalamic L6 cell;
Channel(s): I Na,t; I T low threshold; I A; I K,Ca;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synchronization; Synaptic Integration;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu];
Search NeuronDB for information about:  Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell; Neocortex V1 pyramidal corticothalamic L6 cell; GabaA; GabaB; AMPA; I Na,t; I T low threshold; I A; I K,Ca; Gaba; Glutamate;
: $Id: IT2.mod,v 1.9 2004/06/08 00:46:04 billl Exp $
TITLE Low threshold calcium current
:
:   Ca++ current responsible for low threshold spikes (LTS)
:   RETICULAR THALAMUS
:   Differential equations
:
:   Model of Huguenard & McCormick, J Neurophysiol 68: 1373-1383, 1992.
:   The kinetics is described by standard equations (NOT GHK)
:   using a m2h format, according to the voltage-clamp data
:   (whole cell patch clamp) of Huguenard & Prince, J Neurosci.
:   12: 3804-3817, 1992.
:
:    - Kinetics adapted to fit the T-channel of reticular neuron
:    - Time constant tau_h refitted from experimental data
:    - shift parameter for screening charge
:
:   Model described in detail in:   
:     Destexhe, A., Contreras, D., Steriade, M., Sejnowski, T.J. and
:     Huguenard, J.R.  In vivo, in vitro and computational analysis of
:     dendritic calcium currents in thalamic reticular neurons.
:     Journal of Neuroscience 16: 169-185, 1996.
:
:
:   Written by Alain Destexhe, Salk Institute, Sept 18, 1992
:

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

NEURON {
	SUFFIX itre
	USEION ca READ cai, cao WRITE ica
	RANGE gmax, m_inf, tau_m, h_inf, tau_h, carev, shift, i
        GLOBAL exptemp, q10m, q10h
}

UNITS {
	(molar) = (1/liter)
	(mV) =	(millivolt)
	(mA) =	(milliamp)
	(mM) =	(millimolar)

	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
}

PARAMETER {
	v		(mV)
	gmax	= .003	(mho/cm2)
	shift	= 2 	(mV)
	q10m	= 2.5
	q10h	= 2.5
        exptemp = 24
        cao
        cai

}

STATE {
	m h
}

ASSIGNED {
	i	(mA/cm2)  
	ica	(mA/cm2)
	carev	(mV)
	m_inf
	tau_m	(ms)
	h_inf
	tau_h	(ms)
	phim
        phih
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	carev = (1e3) * (R*(celsius+273.15))/(2*FARADAY) * log (cao/cai)
	i = gmax * m*m*h * (v-carev)
        ica=i
}

DERIVATIVE states {
	mh(v)

	m' = (m_inf - m) / tau_m
	h' = (h_inf - h) / tau_h
}

UNITSOFF
INITIAL {
:
:   Activation functions and kinetics were obtained from
:   Huguenard & Prince, and were at 23-25 deg.
:   Transformation to 36 deg using Q10
:
	phim = q10m ^ ((celsius-exptemp)/10)
	phih = q10h ^ ((celsius-exptemp)/10)

	mh(v)
	m = m_inf
	h = h_inf
}

PROCEDURE mh(v(mV)) { 
:
:   Time constants were obtained from J. Huguenard
:

	m_inf = 1.0 / ( 1 + exp(-(v+shift+50)/7.4) )
	h_inf = 1.0 / ( 1 + exp((v+shift+78)/5.0) )

	tau_m = ( 1 + 0.33 / ( exp((v+shift+25)/10) + exp(-(v+shift+100)/15) ) ) / phim
:	tau_h = ( 22.7 + 0.27 / ( exp((v+shift+46)/4) + exp(-(v+shift+405)/50) ) ) / phih
:	tau_h = ( 56.75 + 0.675 / ( exp((v+shift+46)/4) + exp(-(v+shift+405)/50) ) ) / phih
	tau_h = ( 85 + 1.0 / ( exp((v+shift+46)/4) + exp(-(v+shift+405)/50) ) ) / phih
}
UNITSON

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