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 neuron; Thalamus reticular nucleus cell; Neocortex U1 L5B pyramidal pyramidal tract cell; Retina ganglion 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 neuron; Thalamus reticular nucleus cell; Retina ganglion cell; Neocortex U1 L5B pyramidal pyramidal tract 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
DEMO.png
Geometry.hoc
GUI.hoc
mosinit.hoc
Recording.hoc
Run.hoc
screenshot.png
Simulation.hoc
Templates.hoc
                            
TITLE Fast mechanism for submembranal Ca++ concentration (cai)
:
: Takes into account:
:
:	- increase of cai due to calcium currents
:	- extrusion of calcium with a simple first order equation
:
: This mechanism is compatible with the calcium pump "cad" and has the 
: same name and parameters; however the parameters specific to the pump
: are dummy here.
:
: Parameters:
:
:	- depth: depth of the shell just beneath the membran (in um)
:	- cainf: equilibrium concentration of calcium (2e-4 mM)
:	- taur: time constant of calcium extrusion (must be fast)
:	- kt,kd: dummy parameters
:
: Written by Alain Destexhe, Salk Institute, 1995
:

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

NEURON {
	SUFFIX cad
	USEION ca READ ica, cai WRITE cai
	RANGE depth,kt,kd,cainf,taur
}

UNITS {
	(molar) = (1/liter)			: moles do not appear in units
	(mM)	= (millimolar)
	(um)	= (micron)
	(mA)	= (milliamp)
	(msM)	= (ms mM)
}

CONSTANT {
	FARADAY = 96489		(coul)		: moles do not appear in units
:	FARADAY = 96.489	(k-coul)	: moles do not appear in units
}

PARAMETER {
	depth	= .1	(um)		: depth of shell
	taur	= 5	(ms)		: rate of calcium removal
	cainf	= 2e-4	(mM)
	kt	= 0	(mM/ms)		: dummy
	kd	= 0	(mM)		: dummy
}

STATE {
	cai		(mM) 
}

INITIAL {
	cai = cainf
}

ASSIGNED {
	ica		(mA/cm2)
	drive_channel	(mM/ms)
}
	
BREAKPOINT {
	SOLVE state METHOD euler
}

DERIVATIVE state { 

	drive_channel =  - (10000) * ica / (2 * FARADAY * depth)

	if (drive_channel <= 0.) { drive_channel = 0. }	: cannot pump inward

	cai' = drive_channel + (cainf-cai)/taur
}