LGMD impedance (Dewell & Gabbiani 2019)

 Download zip file 
Help downloading and running models
Accession:256024
"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration."
Reference:
1 . Dewell RB, Gabbiani F (2019) Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony. J Neurophysiol [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Locust Lobula Giant Movement Detector (LGMD) neuron;
Channel(s): I h; I M;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration; Membrane Properties;
Implementer(s): Dewell, Richard Burkett [dewell at bcm.edu];
Search NeuronDB for information about:  I M; I h; 
TITLE na3
: Na current 
: modified from Jeff Magee. M.Migliore may97
: added sh to account for higher threshold M.Migliore, Apr.2002

NEURON {
    THREADSAFE

	SUFFIX na3
	USEION na READ ena WRITE ina
	RANGE  gbar, ar, sh
	GLOBAL qinf, thinf
}

PARAMETER {
	sh   = 8	(mV)
	gbar = 0.010   	(mho/cm2)	
								
	tha  =  -30	(mV)		: v 1/2 for act	
	qa   = 7.2	(mV)		: act slope (4.5)		
	Ra   = 0.4	(/ms)		: open (v)		
	Rb   = 0.124 	(/ms)		: close (v)		

	thi1  = -45	(mV)		: v 1/2 for inact 	
	thi2  = -45 	(mV)		: v 1/2 for inact 	
	qd   = 1.5	(mV)	        : inact tau slope
	qg   = 1.5      (mV)
	mmin=0.02	(ms)
	hmin=0.5	(ms)
	q10=2		(1)
	Rg   = 0.01 	(/ms)		: inact recov (v) 	
	Rd   = .03 	(/ms)		: inact (v)	
	qq   = 10        (mV)
	tq   = -55      (mV)

	thinf  = -50 	(mV)		: inact inf slope	
	qinf  = 4 	(mV)		: inact inf slope 

	vhalfs=-60	(mV)		: slow inact.
	a0s=0.0003	(/ms)		: a0s=b0s
	zetas=12	(degC/mV)
	gms=0.2		(1)
	smax=10		(ms)
	vvh=-58		(mV) 
	vvs=2		(mV)
	ar=1		(1)		: 1=no inact., 0=max inact.
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

ASSIGNED {
	ena		(mV)            : must be explicitly def. in hoc
	celsius	(degC)
	v 		(mV)
	ina 		(mA/cm2)
	thegna		(mho/cm2)
	minf 		hinf 		
	mtau (ms)	htau (ms) 	
	sinf (1)	taus (ms)
}
 

STATE { m h s}

BREAKPOINT {
        SOLVE states METHOD cnexp
        thegna = gbar*m*m*m*h*s
	ina = thegna * (v - ena)
} 

INITIAL {
	trates(v,ar,sh)
	m=minf  
	h=hinf
	s=sinf
}


FUNCTION alpv(v(mV)) {
         alpv = 1/(1+exp((v-vvh-sh)/vvs))
}
        
FUNCTION alps(v(mV)) {  
  alps = exp(1.e-3*zetas*(v-vhalfs-sh)*9.648e4/(8.315*(273.16 (degC)+celsius)))
}

FUNCTION bets(v(mV)) {
  bets = exp(1.e-3*zetas*gms*(v-vhalfs-sh)*9.648e4/(8.315*(273.16(degC)+celsius)))
}

LOCAL mexp, hexp, sexp

DERIVATIVE states {   
        trates(v,ar,sh)      
        m' = (minf-m)/mtau
        h' = (hinf-h)/htau
        s' = (sinf - s)/taus
}

PROCEDURE trates(vm(mV),a2,sh2(mV)) {  
        LOCAL  a, b, c, qt
	qt=q10^((celsius-24(degC))/10(degC))
	a = trap0(vm,tha+sh2,Ra,qa)
	b = trap0(-vm,-tha-sh2,Rb,qa)
	mtau = 1(mV)/(a+b)/qt
        if (mtau<mmin) {mtau=mmin}
	minf = a/(a+b)

	a = trap0(vm,thi1+sh2,Rd,qd)
	b = trap0(-vm,-thi2-sh2,Rg,qg)
	htau =  1(mV)/(a+b)/qt
        if (htau<hmin) {htau=hmin}
	hinf = 1/(1+exp((vm-thinf-sh2)/qinf))
	c=alpv(vm)
        sinf = c+a2*(1-c)
        taus = bets(vm)/(a0s*(1+alps(vm)))
        if (taus<smax) {taus=smax}
}

FUNCTION trap0(v(mV),th(mV),a(/ms),q(mV)) (mV/ms){
	if (fabs((v-th)/1(mV)) > 1e-6) {
	        trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
	} else {
	        trap0 = a * q
 	}
}

Loading data, please wait...