Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)

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Accession:116096
"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..."
Reference:
1 . Badoual M, Zou Q, Davison AP, Rudolph M, Bal T, Fregnac Y, Destexhe A (2006) Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int J Neural Syst 16:79-97 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Na,t; I K; I M; I Calcium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Synaptic Plasticity; Long-term Synaptic Plasticity; STDP;
Implementer(s):
Search NeuronDB for information about:  AMPA; NMDA; I Na,t; I K; I M; I Calcium;
/*

Creates a four-panel figure

*/

objref graphs[4], tmp, fig2, menu

proc add_panel() {
    xpanel($s2)
    xlabel($s2)
    xpanel(0,0)
    graphs[$1] = new Graph()
    graphList[0].append(graphs[$1])
}

proc add_row() {
    tmp = new HBox()
    tmp.intercept(1)
    add_panel($1, $s2)
    add_panel($3, $s4)
    tmp.intercept(0)
    tmp.map()
}

proc make_figure() {
    $o1 = new VBox()
    $o1.intercept(1)
    add_row(0, "a", 1, "b")
    add_row(2, "c", 3, "d")
    $o1.intercept(0)
    $o1.map($s2, 0, 250, 800, 500)
}

proc make_menu() {
    $o1 = new VBox()
    $o1.intercept(1)
    xpanel("Parameters")
    xlabel("This script reproduces Figures 2 and 3")
    xlabel("from Badoual et al (2006) Int J. Neural Syst. 16: 79-97") 
    xpvalue("t_pre", &ampasyn.onset)
    xpvalue("t_post - t_pre", &ampasyn.delta)
    xlabel("Click on one of the buttons below to run the simulation.")
    xpanel()
    xpanel("Buttons", 1)
    xbutton("Fig 2A", "ampasyn.delta = -100 set_axis_limits(0.0014, 0.007, 0.7, 0.014) run()")
    xbutton("Fig 2B", "ampasyn.delta = -10 set_axis_limits(0.0014, 0.007, 0.7, 0.35) run()")
    xbutton("Fig 3A", "ampasyn.delta = 10 set_axis_limits(0.0045, 0.5, 0.9, 0.014) run()")
    xbutton("Fig 3B", "ampasyn.delta = 200 ampasyn.onset = 10 set_axis_limits(0.0016, 0.016, 0.7, 0.014) run()")
    xpanel()
    $o1.intercept(0)
    $o1.map($s2, 400, 0, -1, -1)
}

proc set_axis_limits() {
    graphs[0].size(0, 500, 0, $1)
    graphs[1].size(0, 500, 0, $2)
    graphs[2].size(0, 500, 0, $3)
    graphs[3].size(0, 500, 0, $4)
}

make_figure(fig2, "Figure 2/Figure 3")
make_menu(menu, "Click to run")

graphs[0].addvar("Calcium signal (mM)", "spine.cai(0.5)", 3, 1)
graphs[1].addvar("% of K*", "kin.n", 3, 1)
graphs[1].addvar("Maximum", "maxltp", 2, 6)
graphs[2].addvar("% of m*", "kin.m", 3, 1)
graphs[2].addvar("% of h*", "kin.h", 4, 1)
graphs[3].addvar("% of Ph*", "ltd", 3, 1)
graphs[3].addvar("Maximum", "maxltd", 2, 6)

set_axis_limits(0.0014, 0.007, 0.7, 0.014)

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