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Contrast invariance by LGN synaptic depression (Banitt et al. 2007)

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Accession:114637
"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. ... The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated."
References:
1 . Banitt Y, Martin KA, Segev I (2007) A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression. J Neurosci 27:10230-9 [PubMed]
2 . Anderson JC, Douglas RJ, Martin KA, Nelson JC (1994) Map of the synapses formed with the dendrites of spiny stellate neurons of cat visual cortex. J Comp Neurol 341:25-38 [PubMed]
3 . Anderson JC, Douglas RJ, Martin KA, Nelson JC (1994) Synaptic output of physiologically identified spiny stellate neurons in cat visual cortex. J Comp Neurol 341:16-24 [PubMed]
4 . Banitt Y, Martin KA, Segev I (2005) Depressed responses of facilitatory synapses. J Neurophysiol 94:865-70 [PubMed]
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:
Cell Type(s): Neocortex spiny stellate cell;
Channel(s): I Na,t; I A; I K; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Parameter Fitting; Active Dendrites; Synaptic Integration; Vision;
Implementer(s):
Search NeuronDB for information about:  I Na,t; I A; I K; I K,Ca; I Calcium;
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SSC_model
ReadMe.html
ReadMe_orig
iA.mod
iap.mod
ic.mod
ical.mod
VectorSynNS.mod
axon.hoc
InitArrays.hoc
InitSSCArrays.hoc
InitSynapses.hoc
j3886d_sdt.hoc
MeasureDist.hoc
mosinit.hoc
ssc.hoc
sscDistCl1
sscDistCl2
sscDistCl3
sscDistCl4
sscProxCl1
sscProxCl2
sscProxCl3
sscProxCl4
sscSomaCl1 *
sscSomaCl2 *
sscSomaCl3 *
sscSomaCl4 *
                            
forsec "a*_" {nseg=10}
access soma
distance()

func Length_Of_Prox_Dend(){local i, legnth
	legnth = 0
	forsec "a*_" {
		if (distance(0)<ProxDef){
			i=0.0
   			while (distance(i)<ProxDef){
	  			i+=0.1
				if (i>1){break}
			}
   			i-=0.1			
			legnth+=distance(i)-distance(0)
		}
	}
	//print "legnth of proximal tree = ",legnth
	return legnth
}

func Length_Of_Dist_Dend(){local i, legnth
	legnth=0
	forsec "a*_" {
		if (distance(1)>ProxDef){
			i=1
			while (distance(i)>ProxDef){
				i=i-0.1
				if (i<0){break}
			}
   			i=i+0.1
			legnth+=distance(1)-distance(i)
		}
	}
	//print "legnth of dustal tree =",legnth
	return legnth
}

func Length_Of_Tree(){local length
	legnth=0
	forsec "a*_"{
		//nseg=1
		legnth+=L
	}
	//print "legnth of entire cell =" ,legnth
	return legnth
}

LENOFPROXTREE = Length_Of_Prox_Dend() 
LENOFDISTTREE = Length_Of_Tree()-LENOFPROXTREE

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