Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)

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Accession:239145
This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020. It allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs. The model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011). The model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5.
Reference:
1 . Egger R, Narayanan RT, Guest JM, Bast A, Udvary D, Messore LF, Das S, de Kock CP, Oberlaender M (2020) Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers Neuron
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Calcium; I h; I M; I K; I Na,t; I Na,p; I K,Ca;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Sensory processing; Stimulus selectivity; Synaptic Integration;
Implementer(s): Egger, Robert [robert.egger at nyumc.org];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I K; I M; I h; I K,Ca; I Calcium; Gaba; Glutamate;
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model_publication
mechanisms
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COMMENT
//****************************//
// Created by Alon Polsky 	//
//    apmega@yahoo.com		//
//		2010			//
//****************************//
Modified 2015 by Robert Egger
to include facilitation variable
as modeled by Varela et al. 1997
ENDCOMMENT

TITLE NMDA synapse with depression


NEURON {
	POINT_PROCESS glutamate_syn
	NONSPECIFIC_CURRENT inmda,iampa
	RANGE gampamax,gnmdamax,inmda,iampa
	RANGE decayampa,dampa,taudampa
    RANGE decaynmda,dnmda,taudnmda
    RANGE facilampa,fampa,taufampa
    RANGE facilnmda,fnmda,taufnmda
	RANGE gnmda,gampa
	RANGE e,tau1,tau2,tau3,tau4
}

UNITS {
	(nA) 	= (nanoamp)
	(mV)	= (millivolt)
	(nS) 	= (nanomho)
	(mM)    = (milli/liter)
 	(mA) = (milliamp)
	(um) = (micron)
}

PARAMETER {
	gnmdamax=1	(nS)
	gampamax=1	(nS)
	e= 0.0	(mV)
	tau1=50	(ms)	: NMDA inactivation
	tau2=2	(ms)	: NMDA activation
	tau3=2	(ms)	: AMPA inactivation
	tau4=0.1	(ms)	: AMPA activation
	tau_ampa=2	(ms)	
	n=0.25 	(/mM)	    : Schiller and Larkum
	gama=0.08 	(/mV)   : Schiller and Larkum
	:n=0.28      (/mM)   : Jahr and Stevens
	:gama=0.062  (/mV)   : Jahr and Stevens
	dt 		(ms)
	v		(mV)
	decayampa=.5
	decaynmda=.5
	taudampa=200	(ms):tau decay
	taudnmda=200	(ms):tau decay
    taufampa=200    (ms)
    facilampa=0.0
    taufnmda=200    (ms)
    facilnmda=0.0
}

ASSIGNED { 
	inmda		(nA)  
	iampa		(nA)  
	gnmda		(nS)
	gampa		(nS)
	factor1		: NMDA normalization factor
	factor2		: AMPA normalization factor

}
STATE {
	A 		(nS)
	B 		(nS)
	C 		(nS)
	D 		(nS)
	dampa
	dnmda
    fampa
    fnmda
}


INITIAL {
	LOCAL tp1, tp2
    gnmda=0 
    gampa=0 
	A=0
	B=0
	C=0
	D=0
	dampa=1
	dnmda=1
    fampa=1
    fnmda=1
	
	tp1 = (tau2*tau1)/(tau1 - tau2) * log(tau1/tau2)
	factor1 = -exp(-tp1/tau2) + exp(-tp1/tau1)
	factor1 = 1/factor1
	
	tp2 = (tau4*tau3)/(tau3 - tau4) * log(tau3/tau4)
	factor2 = -exp(-tp2/tau4) + exp(-tp2/tau3)
	factor2 = 1/factor2
}    

BREAKPOINT {  
    
	LOCAL count
	SOLVE state METHOD cnexp
	gnmda=(A-B)/(1+n*exp(-gama*v) )
	gampa=(C-D)
	inmda =(1e-3)*gnmda*(v-e)
	iampa= (1e-3)*gampa*(v- e)

}
NET_RECEIVE(weight_ampa, weight_nmda) {
 
	INITIAL {
	  gampamax = weight_ampa
	  gnmdamax = weight_nmda
	}
	gampamax = weight_ampa
	gnmdamax = weight_nmda
	
	A = A+ factor1*gnmdamax*dnmda*fnmda
	B = B+ factor1*gnmdamax*dnmda*fnmda
	C = C+ factor2*gampamax*dampa*fampa
	D = D+ factor2*gampamax*dampa*fampa
	:gampa = gampa+ gampamax*dampa
	dampa = dampa* decayampa
	dnmda = dnmda* decaynmda
    fampa = fampa + facilampa
    fnmda = fnmda + facilnmda
	
:	VERBATIM
:	/*
:	printf("***********\n");
:	printf("A = %.2f\n", A);
:	printf("B = %.2f\n", B);
:	printf("C = %.2f\n", C);
:	printf("D = %.2f\n", D);
:	*/
:	ENDVERBATIM
}
DERIVATIVE state {
	A'=-A/tau1
	B'=-B/tau2
	C'=-C/tau3
	D'=-D/tau4
	dampa'=(1-dampa)/taudampa
	dnmda'=(1-dnmda)/taudnmda
    fampa'=(1-fampa)/taufampa
    fnmda'=(1-fnmda)/taufnmda
}






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