Glutamate mediated dendritic and somatic plateau potentials in cortical L5 pyr cells (Gao et al '20)

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Accession:249705
Our model was built on a reconstructed Layer 5 pyramidal neuron of the rat medial prefrontal cortex, and constrained by 4 sets of experimental data: (i) voltage waveforms obtained at the site of the glutamatergic input in distal basal dendrite, including initial sodium spikelet, fast rise, plateau phase and abrupt collapse of the plateau; (ii) a family of voltage traces describing dendritic membrane responses to gradually increasing intensity of glutamatergic stimulation; (iii) voltage waveforms of backpropagating action potentials in basal dendrites (Antic, 2003); and (iv) the change of backpropagating action potential amplitude in response to drugs that block Na+ or K+ channels (Acker and Antic, 2009). Both, synaptic AMPA/NMDA and extrasynaptic NMDA inputs were placed on basal dendrites to model the induction of local regenerative potentials termed "glutamate-mediated dendritic plateau potentials". The active properties of the cell were tuned to match the voltage waveform, amplitude and duration of experimentally observed plateau potentials. The effects of input location, receptor conductance, channel properties and membrane time constant during plateau were explored. The new model predicted that during dendritic plateau potential the somatic membrane time constant is reduced. This and other model predictions were then tested in real neurons. Overall, the results support our theoretical framework that dendritic plateau potentials bring neuronal cell body into a depolarized state ("UP state"), which lasts 200 - 500 ms, or more. Plateau potentials profoundly change neuronal state -- a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to action potential firing triggered by other afferent inputs. Plateau potentials may allow cortical pyramidal neurons to tune into ongoing network activity and potentially enable synchronized firing, to form active neural ensembles.
Reference:
1 . Gao PP, Graham JW, Zhou WL, Jang J, Angulo SL, Dura-Bernal S, Hines ML, Lytton W, Antic SD (2020) Local Glutamate-Mediated Dendritic Plateau Potentials Change the State of the Cortical Pyramidal Neuron. J Neurophysiol [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Prefrontal cortex (PFC); Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I A; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s): Glutamate; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Active Dendrites; Calcium dynamics; Axonal Action Potentials; Dendritic Bistability; Detailed Neuronal Models; Membrane Properties; Synaptic Integration;
Implementer(s): Antic, Srdjan [antic at neuron.uchc.edu]; Gao, Peng [peng at uchc.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; NMDA; Glutamate; I A; I K; I h; I K,Ca; Glutamate;
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plateau-potentials
mod
x86_64
ampa.mod *
ca.mod *
Ca_HVA.mod *
Ca_LVAst.mod *
Cad.mod *
CaDynamics_E2.mod *
CaT.mod *
epsp.mod *
gabaa.mod *
gabab.mod *
glutamate.mod *
h_kole.mod *
h_migliore.mod *
Ih.mod *
IL.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
kadist.mod *
kaprox.mod *
kBK.mod *
kv.mod *
na.mod
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
NMDA.mod *
NMDAeee.mod
NMDAmajor.mod
PlateauConductance.mod *
SK_E2.mod *
SKv3_1.mod *
vecstim.mod *
vmax.mod *
ghk.inc *
                            
TITLE K-A channel from Klee Ficker and Heinemann
: modified to account for Dax A Current --- M.Migliore Jun 1997
: modified to be used with cvode  M.Migliore 2001


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

}

PARAMETER {
	v (mV)
	celsius		(degC)
	gkabar=.008 (mho/cm2)
        vhalfn=11   (mV)
        vhalfl=-56   (mV)
        a0l=0.05      (/ms)
        a0n=0.05    (/ms)
        zetan=-1.5    (1)
        zetal=3    (1)
        gmn=0.55   (1)
        gml=1   (1)
	lmin=2  (mS)
	nmin=0.1  (mS)
	pw=-1    (1)
	tq=-40
	qq=5
	q10= 5: 2.3
	: 1 :5 The orginal value is 5, changed to 2 on Nov 6, 2017 by Penny
	: change to 2.3 on Dec 8, 2017 by Penny
	qtl=1
	ek
}


NEURON {
	SUFFIX kap
	USEION k READ ek WRITE ik
        RANGE gkabar,gka
        GLOBAL ninf,linf,taul,taun,lmin
}

STATE {
	n
        l
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        linf
        taul
        taun
        gka
}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	gka = gkabar*n*l
	ik = gka*(v-ek)

}


FUNCTION alpn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION betn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION alpl(v(mV)) {
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION betl(v(mV)) {
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius)))
}

DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rates(v)
        n' = (ninf - n)/taun
        l' =  (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-24)/10)
        a = alpn(v)
        ninf = 1/(1 + a)
        taun = betn(v)/(qt*a0n*(1+a))
	if (taun<nmin) {taun=nmin}
        a = alpl(v)
        linf = 1/(1+ a)
	taul = 0.26*(v+50)/qtl
	if (taul<lmin/qtl) {taul=lmin/qtl}
}

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