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 *
                            
COMMENT
26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
    Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal


na.mod

Sodium channel, Hodgkin-Huxley style kinetics.

Kinetics were fit to data from Huguenard et al. (1988) and Hamill et
al. (1991)

qi is not well constrained by the data, since there are no points
between -80 and -55.  So this was fixed at 5 while the thi1,thi2,Rg,Rd
were optimized using a simplex least square proc

voltage dependencies are shifted approximately from the best
fit to give higher threshold

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

tadj, the temperature adjustment was removed from instantaneous conductance term
in BREAKPOINT

steady-state inactivation was changed to more usual form: a/(a+b) and
inactivation time constant was significantly reduced to reflect recent data
from Kole, .... Stuart '08 Nat Neurosci.

Corey Acker, July 2008, Neuroscience, UConn Health Center

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX na
	USEION na READ ena WRITE ina
	RANGE m, h, gna, gbar
	GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf
	RANGE minf, hinf, mtau, htau
	GLOBAL Ra, Rb, Rd, Rg
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
	gbar = 1000   	(pS/um2)	: 0.12 mho/cm2
:	vshift = -10	(mV)		: voltage shift (affects all)
	vshift = 0	(mV)		: voltage shift (affects all)

	tha  = -38 :-40 :-35.5 : -35	(mV)	: v 1/2 for act		(-42)
	qa   = 9	(mV)		: act slope
	Ra   = 0.182	(/ms)		: open (v)
	Rb   = 0.124	(/ms)		: close (v)

:	thi1  = -50	(mV)		: v 1/2 for inact
      thi1 = -65 (mV)
:	thi2  = -75	(mV)		: v 1/2 for inact
      thi2 = -65 (mV)
	qi   = 6	(mV)	        : inact tau slope
:	thinf  = -65	(mV)		: inact inf slope
	thinf  = -65	(mV)		: inact inf slope
	qinf  = 6.2	(mV)		: inact inf slope
:	Rg   = 0.0091	(/ms)		: inact (v)
	Rg   = 0.02	(/ms)		: inact (v)
	Rd   = 0.024	(/ms)		: inact recov (v)

	temp = 23	(degC)		: original temp
	q10 = 2.3			: temperature sensitivity

	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)
}


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

ASSIGNED {
	ina 		(mA/cm2)
	gna		(pS/um2)
	ena		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}


STATE { m h }

INITIAL {
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
:        gna = tadj*gbar*m*m*m*h : originally included tadj
        gna = gbar*m*m*m*h
	ina = (1e-4) * gna * (v - ena)
}

LOCAL mexp, hexp

DERIVATIVE states {   :Computes state variables m, h, and n
        trates(v+vshift)      :             at the current v and dt.
        m' =  (minf-m)/mtau
        h' =  (hinf-h)/htau
}

PROCEDURE trates(v) {


        TABLE minf,  hinf, mtau, htau
	DEPEND  celsius, temp, Ra, Rb, Rd, Rg, tha, thi1, thi2, qa, qi, qinf

	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable == 1

:        tinc = -dt * tadj

:        mexp = 1 - exp(tinc/mtau)
:        hexp = 1 - exp(tinc/htau)
}


PROCEDURE rates(vm) {
        LOCAL  a, b

	a = trap0(vm,tha,Ra,qa)
	b = trap0(-vm,-tha,Rb,qa)

        tadj = q10^((celsius - temp)/10)

	mtau = 1/tadj/(a+b)
	minf = a/(a+b)

		:"h" inactivation

	a = trap0(-vm,-thi1,Rd,qi)
	b = trap0(vm,thi2,Rg,qi)
	htau = 1/tadj/(a+b)
:	hinf = 1/(1+exp((vm-thinf)/qinf))
      hinf = a/(a+b)
}


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

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