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;
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TCR
influence_ih_iT
cells
cadecay.mod *
hh2.mod *
ic.mod *
Ih_des93.mod *
il.mod *
ITGHK.mod *
VClamp.mod *
El.oc *
loc3.oc *
stim_long.dat *
tc3_cc_ihiT.hoc
                            
TITLE Hippocampal HH channels
:
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:

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

NEURON {
	SUFFIX hh2
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub
	RANGE m_inf, h_inf, n_inf
	RANGE tau_m, tau_h, tau_n
	RANGE m_exp, h_exp, n_exp
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	gnabar	= .003 	(mho/cm2)
	gkbar	= .005 	(mho/cm2)

	ena	= 50	(mV)
	ek	= -90	(mV)
	celsius = 36    (degC)
	dt              (ms)
	v               (mV)
	vtraub	= -63	(mV)
}

STATE {
	m h n
}

ASSIGNED {
	ina	(mA/cm2)
	ik	(mA/cm2)
	il	(mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}


BREAKPOINT {
	SOLVE states
	ina = gnabar * m*m*m*h * (v - ena)
	ik  = gkbar * n*n*n*n * (v - ek)
}


:DERIVATIVE states {   : exact Hodgkin-Huxley equations
:	evaluate_fct(v)
:	m' = (m_inf - m) / tau_m
:	h' = (h_inf - h) / tau_h
:	n' = (n_inf - n) / tau_n
:}

PROCEDURE states() {	: exact when v held constant
	evaluate_fct(v)
	m = m + m_exp * (m_inf - m)
	h = h + h_exp * (h_inf - h)
	n = n + n_exp * (n_inf - n)
	VERBATIM
	return 0;
	ENDVERBATIM
}

UNITSOFF
INITIAL {
	m = 0
	h = 0
	n = 0
:
:  Q10 was assumed to be 3 for both currents
:
: original measurements at roomtemperature?

	tadj = 3.0 ^ ((celsius-36)/ 10 )
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2

	v2 = v - vtraub : convert to traub convention

	a = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1)
	b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	a = 0.128 * exp((17-v2)/18)
	b = 4 / ( 1 + exp((40-v2)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)

	a = 0.032 * (15-v2) / ( exp((15-v2)/5) - 1)
	b = 0.5 * exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)

	m_exp = 1 - exp(-dt/tau_m)
	h_exp = 1 - exp(-dt/tau_h)
	n_exp = 1 - exp(-dt/tau_n)
}

UNITSON