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: na.mod,v 1.8 2004/07/27 18:41:01 billl Exp $

COMMENT
26 Ago 2002 Modification of original channel to allow variable time step and to
  correct an initialization error.
Done by Michael Hines(michael.hines@yale.e) and Ruggero
  Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational
  Neuroscience. Obidos, Portugal

na.mod

Sodium channel, Hodgkin-Huxley style kinetics.  

Kinetics were fit to data from Huguenard et al. (1988) and Hamill et
al. (1991)

qi is not well constrained by the data, since there are no points
between -80 and -55.  So this was fixed at 5 while the thi1,thi2,Rg,Rd
were optimized using a simplex least square proc

voltage dependencies are shifted approximately from the best
fit to give higher threshold

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

ENDCOMMENT

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

NEURON {
  SUFFIX naz
  USEION na READ ena WRITE ina
  RANGE m, h, gna, gmax, i
  GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf
  GLOBAL minf, hinf, mtau, htau
  GLOBAL Ra, Rb, Rd, Rg
  GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
  gmax = 1000   	(pS/um2)	: 0.12 mho/cm2
  vshift = -10	(mV)		: voltage shift (affects all)
  
  tha  = -35	(mV)		: v 1/2 for act		(-42)
  qa   = 9	(mV)		: act slope		
  Ra   = 0.182	(/ms)		: open (v)		
  Rb   = 0.124	(/ms)		: close (v)		

  thi1  = -50	(mV)		: v 1/2 for inact 	
  thi2  = -75	(mV)		: v 1/2 for inact 	
  qi   = 5	(mV)	        : inact tau slope
  thinf  = -65	(mV)		: inact inf slope	
  qinf  = 6.2	(mV)		: inact inf slope
  Rg   = 0.0091	(/ms)		: inact (v)	
  Rd   = 0.024	(/ms)		: inact recov (v) 

  temp = 23	(degC)		: original temp 
  q10  = 2.3			: temperature sensitivity

  v 		(mV)
  dt		(ms)
  celsius		(degC)
  vmin = -120	(mV)
  vmax = 100	(mV)
}


UNITS {
  (mA) = (milliamp)
  (mV) = (millivolt)
  (pS) = (picosiemens)
  (um) = (micron)
} 

ASSIGNED {
  ina 		(mA/cm2)
  i 		(mA/cm2)
  gna		(pS/um2)
  ena		(mV)
  minf 		hinf
  mtau (ms)	htau (ms)
  tadj
}


STATE { m h }

INITIAL { 
  tadj = q10^((celsius - temp)/10)
  rates(v+vshift)
  m = minf
  h = hinf
}

BREAKPOINT {
  SOLVE states METHOD cnexp
  gna = tadj*gmax*m*m*m*h
  i = (1e-4) * gna * (v - ena)
  ina = i
} 

LOCAL mexp, hexp 

DERIVATIVE states {   :Computes state variables m, h, and n 
  rates(v+vshift)      :             at the current v and dt.
  m' =  (minf-m)/mtau
  h' =  (hinf-h)/htau
}

PROCEDURE rates(vm) {  
  LOCAL  a, b

  a = trap0(vm,tha,Ra,qa)
  b = trap0(-vm,-tha,Rb,qa)

  mtau = 1/tadj/(a+b)
  minf = a/(a+b)

  :"h" inactivation 

  a = trap0(vm,thi1,Rd,qi)
  b = trap0(vm,thi2,-Rg,-qi)
  htau = 1/tadj/(a+b)
  hinf = 1/(1+exp((vm-thinf)/qinf))
}


FUNCTION trap0(v,th,a,q) {
  if (fabs(v-th) > 1e-6) {
    trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
  } else {
    trap0 = a * q
  }
}	

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