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: intf.mod,v 1.40 2004/05/11 22:45:50 billl Exp $

NEURON {
  ARTIFICIAL_CELL INTF
  RANGE tau1, tau2, tau3, tau4, refrac, m1, m2, m3, m4, thresh, refractory, fflag
  RANGE adap,adapwt,tauadap
}

PARAMETER {
  tau1 = 10 (ms)
  tau2 = 10 (ms)
  tau3 = 10 (ms)
  tau4 = 10 (ms)
  adapwt = 0
  tauadap= 10 (ms)
  refrac = 5 (ms)
  thresh = 1
  fflag           = 1             : don't change
}

ASSIGNED {
  m1
  m2
  m3
  m4
  adap
  t0(ms)
  refractory
}

INITIAL {
  adap = 0
  m1 = 0
  m2 = 0
  m3 = 0
  m4 = 0
  t0 = t
  refractory = 0 : 0-integrates input, 1-refractory
}

NET_RECEIVE (w1,w2,w3,w4) {
  INITIAL { w2=w2 w3=w3 w4=w4 }
  : always update the state vars
  m1 = m1*exp(-(t - t0)/tau1)
  m2 = m2*exp(-(t - t0)/tau2)
  m3 = m3*exp(-(t - t0)/tau3)
  m4 = m4*exp(-(t - t0)/tau4)
  adap = adap*exp(-(t - t0)/tauadap)
  t0 = t
  if (flag==0) { : only add weights if an external excitation
    m1 = m1 + w1
    m2 = m2 + w2
    m3 = m3 + w3
    m4 = m4 + w4
   }
  if (flag==1) { refractory = 0 }
  if ((refractory==0 || flag==1) && (m1+m2+m3+m4>thresh)) {
    refractory = 1
    adap = adap + adapwt
    net_send(refrac+adap*adap, refractory)
    net_event(t)
  }
}

FUNCTION M1() { M1 = m1*exp(-(t - t0)/tau1) }
FUNCTION M2() { M2 = m2*exp(-(t - t0)/tau2) }
FUNCTION M3() { M3 = m3*exp(-(t - t0)/tau3) }
FUNCTION M4() { M4 = m4*exp(-(t - t0)/tau4) }
FUNCTION AD() { AD = adap*exp(-(t - t0)/tauadap) }
FUNCTION MT() { 
 MT = m1*exp(-(t - t0)/tau1)+m2*exp(-(t - t0)/tau2)+m3*exp(-(t - t0)/tau3)+m4*exp(-(t - t0)/tau4) 
}

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