Thalamocortical augmenting response (Bazhenov et al 1998)

 Download zip file 
Help downloading and running models
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 GLU cell; Thalamus reticular nucleus GABA cell; Neocortex L5/6 pyramidal GLU 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 [bill.lytton at downstate.edu];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex L5/6 pyramidal GLU cell; GabaA; GabaB; AMPA; I Na,t; I T low threshold; I A; I K,Ca; Gaba; Glutamate;
: $Id: kv.mod,v 1.9 2004/07/28 21:25:39 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

kv.mod

Potassium channel, Hodgkin-Huxley style kinetics
Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu

ENDCOMMENT

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

NEURON {
  SUFFIX kv
  USEION k READ ek WRITE ik
  RANGE  n, i, gk, gmax
  GLOBAL ninf, ntau
  GLOBAL Ra, Rb
  GLOBAL q10, temp, tadj
}

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

PARAMETER {
  gmax = 5   	(pS/um2)	: 0.03 mho/cm2
  v 		(mV)
  
  tha  = 25	(mV)		: v 1/2 for inf
  qa   = 9	(mV)		: inf slope		
  
  Ra   = 0.02	(/ms)		: max act rate
  Rb   = 0.002	(/ms)		: max deact rate	

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

} 


ASSIGNED {
  a		(/ms)
  b		(/ms)
  i 		(mA/cm2)
  ik 		(mA/cm2)
  gk		(pS/um2)
  ek		(mV)
  ninf
  ntau (ms)	
  tadj
}


STATE { n }

INITIAL { 
  tadj = q10^((celsius - temp)/10)
  rates(v)
  n = ninf
}

BREAKPOINT {
  SOLVE states METHOD cnexp
  gk = tadj*gmax*n
  i = (1e-4) * gk * (v - ek)
  ik = i
} 



DERIVATIVE  states {   :Computes state variable n 
  rates(v)      :             at the current v and dt.
  n' =  (ninf-n)/ntau
}

PROCEDURE rates(v) {  :Computes rate and other constants at current v.
  :Call once from HOC to initialize inf at resting v.

  a = trap0(v,tha,Ra,qa)
  b = trap0(v,tha,-Rb,-qa)
  ntau = 1/tadj/(a+b)
  ninf = a/(a+b)
}

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
  }
}	

Loading data, please wait...