Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018)

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Accession:232876
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium- activated potassium current (Huguenard & McCormick 1994, accession number 3808). The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. Finally, the model was used to in the more realistic “high-conductance state” (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.
Reference:
1 . Zeldenrust F, Chameau P, Wadman WJ (2018) Spike and burst coding in thalamocortical relay cells. PLoS Comput Biol 14:e1005960 [PubMed]
2 . Destexhe A, Bal T, McCormick DA, Sejnowski TJ (1996) Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. J Neurophysiol 76:2049-70 [PubMed]
3 . Huguenard JP, Mccormick DA (1994) Electrophysiology of the Neuron: An Interactive Tutorial
4 . Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ (2001) Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107:13-24 [PubMed]
5 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349-98 [PubMed]
6 . Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786-95 [PubMed]
7 . Destexhe A, Neubig M, Ulrich D, Huguenard J (1998) Dendritic low-threshold calcium currents in thalamic relay cells. J Neurosci 18:3574-88 [PubMed]
8 . Destexhe A, Babloyantz A, Sejnowski TJ (1993) Ionic mechanisms for intrinsic slow oscillations in thalamic relay neurons. Biophys J 65:1538-52 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell;
Channel(s): I L high threshold; I K,Ca; I h; I T low threshold;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Bursting; Information transfer; Rebound firing; Sensory coding;
Implementer(s): Zeldenrust, Fleur [fleurzeldenrust at gmail.com];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; I L high threshold; I T low threshold; I h; I K,Ca; 
/*----------------------------------------------------------------

  Geometry file for thalamocortical neuron

  - 3 compartments
  - 1 soma, 1 proximal dendrite, 1 distal dendrite

  Obtained from merging the cell from rat vb, stained by Huguenard & Prince, 
  Stanford.  The cell was traced in the Salk Institute by A. Destexhe.
  Starting from a 200 compartment cell, the geometry was reduced into an 
  equivalent geometry of 3 compartments, by a method developed by Mike Neubig
  and inspired from Bush & Sejnowski theme for axial resistance conservation
  (J Neurosci Methods 46: 159-166, 1993).  

  Structure of the reduced compartmental model:


	1--0 1--0   1--0
         (dend1)   (soma)

  Length and diameters:
  ---------------------

	Soma :		L=38.42		diam=26		S=2624.6
	dend1[0] : 	L=12.49		diam=10.28	S=403.37
	dend1[1] : 	L=84.67		diam=8.5	S=2260.99

	=> total dendritic surface = 5288.96 (2664.36 for dendrites)
	   Reconstructed cell was of 23980.547 um2 (21355.8 for dendrites)

	=> avg reduction factor for dendrites is CorrD = 8.02
	   SimFit of experimental voltage-clamp trace gives CorrD = 7.954


The construction of this cell from dendritic morphology of the TC cells, the
evaluation of its passive parameters, its cable properties and how this neuron
matches the physiology of thalamic relay cells are described in:

      Destexhe A, Neubig M, Ulrich D and Huguenard JR.  Dendritic
      low-threshold calcium currents in thalamic relay cells.  
      J. Neurosci. 18: 3574-3588, 1998.
      http://cns.fmed.ulaval.ca

Please cite this reference if you use this model

Alain Destexhe and Mike Neubig, Laval University, 1997

----------------------------------------------------------------*/

print "----------------------------------------------"
print "   Simplified TC cell with 3 compartments"
print "----------------------------------------------"



/* create compartments (sections) */

create soma[1], \
       dend1[2]

/* connect compartments */

soma connect dend1[0](0),1
dend1[0] connect dend1[1](0),1



/* setup 3d geometry : */

soma {			/* define soma first */
  nseg = 1
  pt3dclear()
  pt3dadd(0, 0, 0, 26)	 	/* x,y,z,diam */
  pt3dadd(0, 38.42, 0, 26)
}



dend1[0] {		/* dendrites */
  nseg = 1
  pt3dclear()
  pt3dadd(0, 38.42, 0, 10.28)
  pt3dadd(0, 50.91, 0, 10.28)
}

dend1[1] {
  nseg = 1
  pt3dclear()
  pt3dadd(0, 50.91, 0, 8.5)
  pt3dadd(0, 135.58, 0, 8.5)
}