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 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 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 I-h channel from Magee 1998 for distal dendrites

NEURON {
	SUFFIX h
	NONSPECIFIC_CURRENT i
    RANGE ghbar, vhalfl, eh, i
    RANGE linf,taul
}

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

PARAMETER {
    eh=-30.		    (mV)
	ghbar=.0001 	(mho/cm2)
    vhalfl=-90   	(mV)
    vhalft=-75   	(mV)
    a0t=0.011      	(/ms)
    zetal=4    	    (1)
    zetat=2.2    	(1)
    gmt=.4   	    (1)
	q10=4.5
	qtl=1
}

STATE {
    l
}

ASSIGNED {
	v 	    (mV)
    i       (mA/cm2)
	ih      (mA/cm2)
    linf
    taul
    g
    celsius (degC)
}

INITIAL {
	rate(v)
	l=linf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	g = ghbar*l
	i = g*(v-eh)
	: ih = i
}

DERIVATIVE states {
    rate(v)
    l' =  (linf - l)/taul
}

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

FUNCTION alpt(v(mV)) {
  alpt = exp(1e-3*zetat*(v-vhalft)*9.648e4/(8.315*(273.16+celsius)))
}

FUNCTION bett(v(mV)) {
  bett = exp(1e-3*zetat*gmt*(v-vhalft)*9.648e4/(8.315*(273.16+celsius)))
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-33)/10)
        a = alpt(v)
        linf = 1/(1+ alpl(v))
        taul = bett(v)/(qtl*qt*a0t*(1+a))
}