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
FullModel
data
morphologies
README.md
ampa_kin.mod *
exp2EPSC.mod
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 CA1 KM channel from Mala Shah
: M. Migliore June 2006
: option to have faster activation than inactivation kinetics based on Chen & Johnston, 2004.

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

PARAMETER {
	gkmbar 	= .0001 	(mho/cm2)
    vhalfl 	= -40   	(mV)
	kl 		= -7
    vhalft 	= -42   	(mV)
    a0t_f 	= 0.009     (/ms)
	a0t_s 	= 0.036
    zetat 	= 7    		(1)
    gmt 	= .4   		(1)
	q10 	= 5
	t0_f 	= 15
	t0_s 	= 60
	st 		= 1
}

NEURON {
	SUFFIX km2
	USEION k READ ek WRITE ik
    RANGE  gkmbar,ik,gk,inf,tau
}

STATE {
    m
}

ASSIGNED {
    v 	    (mV)
	ek
	celsius (degC)
	ik      (mA/cm2)
    gk 		(S/cm2)
    inf
	tau
    tau_f
	tau_s
}

INITIAL {
	rate(v)
	m=inf
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	gk = gkmbar*m^st
	ik = gk*(v-ek)
}

FUNCTION alpt(v(mV)) {
    alpt = exp(0.0378*zetat*(v-vhalft))
}

FUNCTION bett(v(mV)) {
    bett = exp(0.0378*zetat*gmt*(v-vhalft))
}

DERIVATIVE state {
    rate(v)
	if (m<inf) {tau=tau_s} else {tau=tau_f}
    :if (m<inf) {tau=tau_f} else {tau=tau_s}
	:tau=tau_s
	:tau=tau_f
	m' = (inf - m)/tau
}

PROCEDURE rate(v (mV)) { :callable from hoc
    LOCAL a,qt
    qt=q10^((celsius-35)/10)
    inf = (1/(1 + exp((v-vhalfl)/kl)))
    a = alpt(v)
    tau_f = t0_f + bett(v)/(a0t_f*(1+a))
    tau_s = t0_s + bett(v)/(a0t_s*(1+a))
}