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
DEMO.png
Geometry.hoc
GUI.hoc
mosinit.hoc
Recording.hoc
Run.hoc
screenshot.png
Simulation.hoc
Templates.hoc
                            
//
// Simulation.hoc - Main simulation file
//
// The simulation mode will generate some activity traces.
// Warning: this will take a LOT of time on a regular computer and more than 1GB of disk space.
// (see Recording.hoc for a description of the recording file format)
//
// Thalamocortical convergence studies
// Sébastien Béhuret, UNIC/CNRS Paris, 2009
//

// NEURON parameters
steps_per_ms = 10
dt = 0.1	
tstart = 0
tstop = 10000
runStopAt = tstop
v_init = -70
celsius = 34.5

// Retinothalamic topology
RetinalDivergenceCount = 30
RelayConvergenceCount = 1
RetinalCellCount = 1
RelayCellCount = 30

// TC cells
TC_Grest = 8.34 // nS
TC_Vrest = -74.31 // mV
TC_Gexc = 0 // excitatory conductance mean amplitude
TC_Ginh = 0 // inhibitory conductance mean amplitude
TC_Sexc = 0 // excitatory conductance variation ratio
TC_Sinh = 0 // inhibitory conductance variation ratio

RelayAMPAWeight = 12.5 // nS
FeedbackNoiseEnabled = 1
FeedbackNoiseInterCorrelation = 0

// Cortical cell (no synaptic noise)
C_Grest = 33.4 // nS
C_Vrest = -70.57 // mV
C_Gexc = 0
C_Ginh = 0
C_Sexc = 0
C_Sinh = 0

CorticalNoiseEnabled = 0
CorticalAMPAWeight = 7 * 30 / RelayCellCount // optimal rule: 7 nS for 30 TC cells and 2.33 nS for 90 TC cells

// Simulation variables and function
strdef RecordingFileName
BatchCount = 0

proc RunSimulation() {
	BatchCount = BatchCount + 1
	sprint(RecordingFileName, "Activity_traces-TC_Gexc=%.02f-TC_Ginh=%.02f-TC_Sexc=%.02f-TC_Sinh=%.02f-Cnoise=%.02f.dat", TC_Gexc, TC_Ginh, TC_Sexc, TC_Sinh, FeedbackNoiseInterCorrelation)
	printf("run(%d): %s\n", BatchCount, RecordingFileName)

	FeedbackNoiseGeMean = TC_Grest * TC_Gexc
	FeedbackNoiseGeSigma = FeedbackNoiseGeMean * TC_Sexc
	FeedbackNoiseGiMean = TC_Grest * TC_Ginh
	FeedbackNoiseGiSigma = FeedbackNoiseGiMean * TC_Sinh
	CorticalNoiseGeMean = C_Grest * C_Gexc
	CorticalNoiseGeSigma = CorticalNoiseGeMean * C_Sexc
	CorticalNoiseGiMean = C_Grest * C_Ginh
	CorticalNoiseGiSigma = CorticalNoiseGiMean * C_Sinh

	CreateGeometry()
	UpdateParameters()
	StartRecording(RecordingFileName)
	run()	
	StopRecording()
	DestroyGeometry()
	UpdateParameters()
}

// Explore synaptic noise mean amplitude
TC_Sexc = 0.2
TC_Sinh = 0.2
FeedbackNoiseInterCorrelation = 0

for (TC_Gexc = 0; TC_Gexc <= 3; TC_Gexc = TC_Gexc + 0.3) {
	for (TC_Ginh = 0; TC_Ginh <= 3; TC_Ginh = TC_Ginh + 0.3) {
		RunSimulation()
	}
}

// Explore synaptic noise variation ratio
TC_Gexc = 1.5
TC_Ginh = 1.0
FeedbackNoiseInterCorrelation = 0

for (TC_Sexc = 0; TC_Sexc <= 1; TC_Sexc = TC_Sexc + 0.1) {
	for (TC_Sinh = 0; TC_Sinh <= 1; TC_Sinh = TC_Sinh + 0.1) {
		RunSimulation()
	}
}

// Explore synaptic noise correlation across TC cells
TC_Gexc = 1.5
TC_Ginh = 1.0
TC_Sexc = 0.2
TC_Sinh = 0.2

for (FeedbackNoiseInterCorrelation = 0; FeedbackNoiseInterCorrelation <= 1; FeedbackNoiseInterCorrelation = FeedbackNoiseInterCorrelation + 0.1) {
	RunSimulation()
}