A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)

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Accession:150240
“… Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. … The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. …”
Reference:
1 . Behuret S, Deleuze C, Gomez L, Fregnac Y and Bal T (2013) Cortically-controlled population stochastic facilitation as a plausible substrate for guiding sensory transfer across the thalamic gateway PLoS Computational Biology 9(12):e1003401
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex; Thalamus; Retina;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Retina ganglion GLU cell; Thalamus lateral geniculate nucleus interneuron;
Channel(s): I Na,t; I T low threshold; I K; I M;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Synaptic Convergence;
Implementer(s): Behuret, Sebastien [behuret at unic.cnrs-gif.fr];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Retina ganglion GLU cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell; GabaA; AMPA; I Na,t; I T low threshold; I K; I M;
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TCconvergenceModel
README.html
cadecay.mod *
ConductancePattern.mod
ConstantCurrent.mod
hh2.mod *
IM.mod
IT.mod
ITGHK.mod
RandomGenerator.mod
RetinalInput.mod
SineWaveCurrent.mod
SynapticNoise.mod
Demo.hoc
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Geometry.hoc
GUI.hoc
mosinit.hoc
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Simulation.hoc
Templates.hoc
                            
TITLE Cortical M current
:
:   M-current, responsible for the adaptation of firing rate and the 
:   afterhyperpolarization (AHP) of cortical pyramidal cells
:
:   First-order model described by hodgkin-Hyxley like equations.
:   K+ current, activated by depolarization, noninactivating.
:
:   Model taken from Yamada, W.M., Koch, C. and Adams, P.R.  Multiple 
:   channels and calcium dynamics.  In: Methods in Neuronal Modeling, 
:   edited by C. Koch and I. Segev, MIT press, 1989, p 97-134.
:
:   See also: McCormick, D.A., Wang, Z. and Huguenard, J. Neurotransmitter 
:   control of neocortical neuronal activity and excitability. 
:   Cerebral Cortex 3: 387-398, 1993.
:
:   Written by Alain Destexhe, Laval University, 1995
:

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

NEURON {
	SUFFIX im
	USEION k READ ek WRITE ik
        RANGE gkbar, m_inf, tau_m
	GLOBAL taumax

}

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


PARAMETER {
	v		(mV)
	celsius = 36    (degC)
	ek		(mV)
	gkbar	= 1e-6	(mho/cm2)
	taumax	= 1000	(ms)		: peak value of tau
}



STATE {
	m
}

ASSIGNED {
	ik	(mA/cm2)
	m_inf
	tau_m	(ms)
	tau_peak	(ms)
	tadj
}

BREAKPOINT {
	SOLVE states METHOD euler
	ik = gkbar * m * (v - ek)
}

DERIVATIVE states { 
	evaluate_fct(v)

	m' = (m_inf - m) / tau_m
}

UNITSOFF
INITIAL {
	evaluate_fct(v)
	m = 0
:
:  The Q10 value is assumed to be 2.3
:
        tadj = 2.3 ^ ((celsius-36)/10)
	tau_peak = taumax / tadj
}

PROCEDURE evaluate_fct(v(mV)) {

	m_inf = 1 / ( 1 + exptable(-(v+35)/10) )
	tau_m = tau_peak / ( 3.3 * exptable((v+35)/20) + exptable(-(v+35)/20) )
}
UNITSON


FUNCTION exptable(x) { 
	TABLE  FROM -25 TO 25 WITH 10000

	if ((x > -25) && (x < 25)) {
		exptable = exp(x)
	} else {
		exptable = 0.
	}
}