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
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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 *
                            
TITLE C channel
: Ca-dependent K channel.
: By Ojvind Bernander 92-01-21.
: (db) added RANGE section to allow access to parameters from mech. browser
: (db) 4.1.98 modifications for CVode

NEURON {
	SUFFIX icnew
	USEION k READ ek WRITE ik
	USEION ca READ ica
	GLOBAL inf
	RANGE gkbar,ek,tau_diff,taum,fact, ik, ca_beta
}
UNITSON
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
PARAMETER {
	v (mV)
	dt (ms)
	gkbar=.045 (mho/cm2)
	ek = -95 (mV)
	:ca_beta  = 20.0 (1/ms)   : Ca decay (inverse, 50 msec)
        tau_diff = 0.05 (ms)
	taum = 2.0 (1/ms)
	fact = 1e-3

}

CONSTANT {
	ca_beta  = 20.0 (1/ms)   : Ca decay (inverse, 50 msec)
	ca_alpha = 100.0 (mM/ms/mA)
}

STATE { m cai}
ASSIGNED {
	ik (mA/cm2)
	ica (mA/cm2)
	inf[1]
	tau[1]
}

INITIAL {
	cai = 0.0 (mM)
  	:ca_beta=1/(tau_diff+1e-10)
	:printf("ca_beta=%g\n",ca_beta)
}

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

DERIVATIVE states {	: exact when ca held constant
	mhn(cai)
	m' = (inf[0] - m)/tau[0]
	cai' = (ca_alpha*(-ica) - ca_beta*cai)*fact
}

FUNCTION varss(ca) {
	varss = ca / (ca + 0.040) :K activation
	:printf("ca_beta=%g\n",ca_beta)
}

FUNCTION vartau() {
	vartau = taum  :2.0  K activation tau
}	


PROCEDURE mhn(ca) { :no dependence on voltage, only on ca concentration (appears in states)
:	TABLE inf,tau 
:	DEPEND celsius, dt
:	FROM -100 TO 100 WITH 2000 : .1 mV steps
	tau[0] = vartau()
	inf[0] = varss(ca)
}

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