CA1 pyramidal neuron: Persistent Na current mediates steep synaptic amplification (Hsu et al 2018)

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Accession:240960
This paper shows that persistent sodium current critically contributes to the subthreshold nonlinear dynamics of CA1 pyramidal neurons and promotes rapidly reversible conversion between place-cell and silent-cell in the hippocampus. A simple model built with realistic axo-somatic voltage-gated sodium channels in CA1 (Carter et al., 2012; Neuron 75, 1081–1093) demonstrates that the biophysics of persistent sodium current is sufficient to explain the synaptic amplification effects. A full model built previously (Grienberger et al., 2017; Nature Neuroscience, 20(3): 417–426) with detailed morphology, ion channel types and biophysical properties of CA1 place cells naturally reproduces the steep voltage dependence of synaptic responses.
Reference:
1 . Hsu CL, Zhao X, Milstein AD, Spruston N (2018) Persistent sodium current mediates the steep voltage dependence of spatial coding in hippocampal pyramidal neurons Neuron 99:1-16
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Channel/Receptor; Neuron or other electrically excitable cell; Axon; Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Abstract single compartment conductance based cell;
Channel(s): I Sodium; I A; I M; I h; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Ion Channel Kinetics; Membrane Properties; Synaptic Integration; Synaptic Amplification; Place cell/field; Active Dendrites; Conductance distributions; Detailed Neuronal Models; Electrotonus; Markov-type model;
Implementer(s): Hsu, Ching-Lung [hsuc at janelia.hhmi.org]; Milstein, Aaron D. [aaronmil at stanford.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I A; I K; I M; I h; I Sodium; Glutamate;
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HsuEtAl2018
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gaba_a_kin.mod *
h.mod
kad.mod *
kap.mod *
kdr.mod *
km2.mod
nas.mod
nax.mod
nmda_kin5.mod *
pr.mod *
vecevent.mod *
batch_nap_EPSC_amplification.sh
batch_nap_EPSP_amplification.sh
batch_nap_EPSP_amplification_IO.sh
function_lib.py
install notes.txt
plot_nap_EPSC_amplification.py
plot_nap_EPSP_amplification.py
plot_nap_EPSP_amplification_IO.py
plot_results.py
simulate_nap_EPSC_amplification.py
simulate_nap_EPSP_amplification.py
simulate_nap_EPSP_amplification_IO.py
specify_cells.py
visualize_ion_channel_gating_parameters.py
                            
TITLE nas
: Na current 
: modified from Jeff Magee. M.Migliore may97
: added sh to account for higher threshold M.Migliore, Apr.2002
: Aaron Milstein modified October 2015, added additional sha that applies only to activation threshold

NEURON {
	THREADSAFE
    SUFFIX nas
	USEION na READ ena WRITE ina
	RANGE gbar, ar, sh, sha
	RANGE minf, hinf, mtau, htau, sinf, taus, ina
    GLOBAL qinf, thinf
}

PARAMETER {
	sh   = 0	    (mV)
    sha  = 0        (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	
	hmin=0.5			
	q10=2
	Rg   = 0.01 	(/ms)		: inact recov (v) 	
	Rd   = .03 	    (/ms)		: inact (v)

	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	    (1)
    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)
    celsius     (degC)
	v 		    (mV)
    ina 		(mA/cm2)
	thegna		(mho/cm2)
	minf
    hinf
	mtau        (ms)
    htau        (ms)
	sinf
    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,sha)
	m=minf  
	h=hinf
	s=sinf
    thegna = gbar*m*m*m*h*s
	ina = thegna * (v - ena)
}


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

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

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

PROCEDURE trates(vm,a2,sh2,sha2) {
    LOCAL  a, b, c, qt
    qt=q10^((celsius-24)/10)
	a = trap0(vm,tha+sh2+sha2,Ra,qa)
	b = trap0(-vm,-tha-sh2-sha2,Rb,qa)
	mtau = 1/(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)
    :a = trap0(vm,thi1,Rd,qd)
	:b = trap0(-vm,-thi2,Rg,qg)
	htau =  1/(a+b)/qt
    if (htau<hmin) {htau=hmin}
	hinf = 1/(1+exp((vm-thinf-sh2)/qinf))
    :hinf = 1/(1+exp((vm-thinf)/qinf))
	c=alpv(vm)
    sinf = c+a2*(1-c)
    taus = bets(vm)/(a0s*(1+alps(vm)))
    if (taus<smax) {taus=smax}
}

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
 	}
}