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: km.mod,v 1.5 2004/06/08 21:07:12 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

km.mod

Potassium channel, Hodgkin-Huxley style kinetics
Based on I-M (muscarinic K channel)
Slow, noninactivating

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

ENDCOMMENT

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

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

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

PARAMETER {
  gmax = 10   	(pS/um2)	: 0.03 mho/cm2
  v 		(mV)
  
  tha  = -30	(mV)		: v 1/2 for inf
  qa   = 9	(mV)		: inf slope		
  
  Ra   = 0.001	(/ms)		: max act rate  (slow)
  Rb   = 0.001	(/ms)		: max deact rate  (slow)

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

  vmin = -120	(mV)
  vmax = 100	(mV)
} 


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
} 

LOCAL nexp

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 = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
  b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))

  ntau = 1/tadj/(a+b)
  ninf = a/(a+b)
}


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