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: gabab.mod,v 1.9 2004/06/17 16:04:05 billl Exp $

COMMENT
-----------------------------------------------------------------------------

	Kinetic model of GABA-B receptors
	=================================

  MODEL OF SECOND-ORDER G-PROTEIN TRANSDUCTION AND FAST K+ OPENING
  WITH COOPERATIVITY OF G-PROTEIN BINDING TO K+ CHANNEL

  PULSE OF TRANSMITTER

  SIMPLE KINETICS WITH NO DESENSITIZATION

	Features:

  	  - peak at 100 ms; time course fit to Tom Otis' PSC
	  - SUMMATION (psc is much stronger with bursts)


	Approximations:

	  - single binding site on receptor	
	  - model of alpha G-protein activation (direct) of K+ channel
	  - G-protein dynamics is second-order; simplified as follows:
		- saturating receptor
		- no desensitization
		- Michaelis-Menten of receptor for G-protein production
		- "resting" G-protein is in excess
		- Quasi-stat of intermediate enzymatic forms
	  - binding on K+ channel is fast


	Kinetic Equations:

	  dR/dt = K1 * T * (1-R-D) - K2 * R

	  dG/dt = K3 * R - K4 * G

	  R : activated receptor
	  T : transmitter
	  G : activated G-protein
	  K1,K2,K3,K4 = kinetic rate cst

  n activated G-protein bind to a K+ channel:

	n G + C <-> O		(Alpha,Beta)

  If the binding is fast, the fraction of open channels is given by:

	O = G^n / ( G^n + KD )

  where KD = Beta / Alpha is the dissociation constant

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  Parameters estimated from patch clamp recordings of GABAB PSP's in
  rat hippocampal slices (Otis et al, J. Physiol. 463: 391-407, 1993).

-----------------------------------------------------------------------------

  PULSE MECHANISM

  Kinetic synapse with release mechanism as a pulse.  

  Warning: for this mechanism to be equivalent to the model with diffusion 
  of transmitter, small pulses must be used...

  For a detailed model of GABAB:

  Destexhe, A. and Sejnowski, T.J.  G-protein activation kinetics and
  spill-over of GABA may account for differences between inhibitory responses
  in the hippocampus and thalamus.  Proc. Natl. Acad. Sci. USA  92:
  9515-9519, 1995.

  For a review of models of synaptic currents:

  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  Kinetic models of 
  synaptic transmission.  In: Methods in Neuronal Modeling (2nd edition; 
  edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996.

  This simplified model was introduced 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 also http://www.cnl.salk.edu/~alain



  Alain Destexhe, Salk Institute and Laval University, 1995

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ENDCOMMENT



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

NEURON {
	POINT_PROCESS GABAB
	RANGE R, G, g
	NONSPECIFIC_CURRENT i
	GLOBAL Cmax, Cdur
	GLOBAL K1, K2, K3, K4, KD, Erev
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {

	Cmax	= 0.5	(mM)		: max transmitter concentration
	Cdur	= 0.3	(ms)		: transmitter duration (rising phase)
:
:	From Kfit with long pulse (5ms 0.5mM)
:
	K1	= 0.52	(/ms mM)	: forward binding rate to receptor
	K2	= 0.0013 (/ms)		: backward (unbinding) rate of receptor
	K3	= 0.098 (/ms)		: rate of G-protein production
	K4	= 0.033 (/ms)		: rate of G-protein decay
	KD	= 100			: dissociation constant of K+ channel
	n	= 4			: nb of binding sites of G-protein on K+
	Erev	= -95	(mV)		: reversal potential (E_K)
}


ASSIGNED {
	v		(mV)		: postsynaptic voltage
	i 		(nA)		: current = g*(v - Erev)
	g 		(umho)		: conductance
	Gn
	R				: fraction of activated receptor
	edc
	synon
	Rinf
	Rtau (ms)
	Beta (/ms)
}


STATE {
	Ron Roff
	G				: fraction of activated G-protein
}


INITIAL {
	R = 0
	G = 0
	synon = 0
	Rinf = K1*Cmax/(K1*Cmax + K2)
	Rtau = 1/(K1*Cmax + K2)
	Beta = K2
}

BREAKPOINT {
	SOLVE bindkin METHOD cnexp
	Gn = G*G*G*G : ^n = 4
	g = Gn / (Gn+KD)
	i = g*(v - Erev)
}


DERIVATIVE bindkin {
	Ron' = synon*K1*Cmax - (K1*Cmax + K2)*Ron
	Roff' = -K2*Roff
	R = Ron + Roff
	G' = K3 * R - K4 * G
}

: following supports both saturation from single input and
: summation from multiple inputs
: Note: automatic initialization of all reference args to 0 except first

NET_RECEIVE(weight,  r0, t0 (ms)) {
	if (flag == 1) { : at end of Cdur pulse so turn off
		r0 = weight*(Rinf + (r0 - Rinf)*exp(-(t - t0)/Rtau))
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
        }else{ : at beginning of Cdur pulse so turn on
		r0 = weight*r0*exp(-Beta*(t - t0))
		t0 = t
		synon = synon + weight
		state_discontinuity(Ron, Ron + r0)
		state_discontinuity(Roff, Roff - r0)
		:come again in Cdur
		net_send(Cdur, 1)
        }
}

Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Computational models of thalamocortical augmenting responses. J Neurosci 18:6444-65[PubMed]

References and models cited by this paper

References and models that cite this paper

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