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: IT.mod,v 1.12 2004/06/08 19:32:19 billl Exp $
TITLE Low threshold calcium current
:
:   Ca++ current responsible for low threshold spikes (LTS)
:   THALAMOCORTICAL CELLS
:   Differential equations
:
:   Model based on the data of Huguenard & McCormick, J Neurophysiol
:   68: 1373-1383, 1992 and Huguenard & Prince, J Neurosci.
:   12: 3804-3817, 1992.
:
:   Features:
:
:	- kinetics described by Nernst equations using a m2h format
:	- activation considered at steady-state
:	- inactivation fit to Huguenard's data using a bi-exp function
:	- shift for screening charge, q10 of inactivation of 3
:
:   Described in:
:    Destexhe, A., Bal, T., McCormick, D.A. and Sejnowski, T.J.  Ionic 
:    mechanisms underlying synchronized oscillations and propagating waves
:    in a model of ferret thalamic slices. Journal of Neurophysiology 76:
:    2049-2070, 1996.  (see http://www.cnl.salk.edu/~alain)
:
:
:   Alain Destexhe, Salk Institute and Laval University, 1995
:

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

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

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

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

PARAMETER {
	v		(mV)
	gmax	= 0.0022 (mho/cm2)
	q10m	= 3			: Q10 of activation
	q10h	= 3			: Q10 of inactivation
        exptemp = 24    (degC)
	shift	= 2 	(mV)		: corresponds to 2mM ext Ca++
	cai	= 2.4e-4 (mM)		: adjusted for eca=120 mV
	cao	= 2	(mM)
}

STATE {
  m h
}

ASSIGNED {
	g	(mho/cm2)
	i	(mA/cm2)
	ica	(mA/cm2)
	carev	(mV)
	m_inf
	tau_m	(ms)			: dummy variable for compatibility
	h_inf
	tau_h	(ms)
	phi_m
	phi_h
        celsius
}

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

DERIVATIVE states {
	mh(v)

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


UNITSOFF
INITIAL {
:
:   Transformation to 36 deg assuming Q10 of 3 for h
:   (as in Coulter et al., J Physiol 414: 587, 1989)

	phi_m = q10m ^ ((celsius-exptemp)/10)
	phi_h = q10h ^ ((celsius-exptemp)/10)

	mh(v)
	h = h_inf
	m = m_inf
}

PROCEDURE mh (v(mV)) { LOCAL Vm

	Vm = v + shift

	m_inf = 1.0 / ( 1 + exp(-(Vm+57)/6.2) )
	h_inf = 1.0 / ( 1 + exp((Vm+81)/4.0) )

:       tau_m = (0.822/(exp(-(Vm+130  )/16.7) + exp((Vm+14.8)/18.2) ) + 0.480)/phi_m
        tau_m = (1  /  (exp(-(Vm+129.6)/16.7) + exp((Vm+14.8)/18.2) ) + 0.612)/phi_m
:	tau_h = ( 8.2+(56.6+0.27*exp((Vm+113.2)/5))/(1+exp((Vm+84)/3.2)))/phi_h
        tau_h = (30.8+(211.4  +  exp((Vm+113.2)/5))/(1+exp((Vm+84)/3.2)))/phi_h
}

UNITSON

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