CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)

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Accession:144490
This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value.
Reference:
1 . Sterratt DC, Groen MR, Meredith RM, van Ooyen A (2012) Spine calcium transients induced by synaptically-evoked action potentials can predict synapse location and establish synaptic democracy. PLoS Comput Biol 8:e1002545 [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): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I Mixed; I R; I_AHP;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Synaptic Plasticity;
Implementer(s): Sterratt, David ; Groen, Martine R [martine.groen at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I Mixed; I R; I_AHP;
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bpap
CA1_multi
datastore
pars
plots
poirazi-nmda-car
tests
validation-plots
README.txt
ampa_forti.mod
cacum.mod
cad.mod *
cagk.mod
cal.mod
calH.mod
car.mod
car_mag.mod
cat.mod
d3.mod *
h.mod
hha_old.mod
hha2.mod
kadist.mod
kaprox.mod
kca.mod
km.mod
nap.mod
nmda_andr.mod
somacar.mod
binaverages.m
bpap-cell.hoc
bpap-data.hoc
bpap-dendburst.hoc
bpap-graphics.hoc
bpap-gui.hoc
bpap-gui.ses
bpap-pars.hoc
bpap-record.hoc
bpap-run.hoc
bpap-scaling.hoc
bpap-sims.hoc
bpap-sims-cell1.hoc
bpap-sims-cell2.hoc
bpap-sims-scaling.hoc
bpap-somainj.hoc
bpap-spiketrain.hoc
ca1_mrg_cell1.hoc
ca1_mrg_cell2.hoc
ca1_poirazi.hoc
ChannelBlocker.hoc
CrossingFinder.hoc
epspsizes.hoc
figure-example.R
figures.R
figures-common.R
FileUtils.hoc
FormatFile.hoc
ghk.inc
GraphUtils.hoc
Integrator.hoc
Makefile
mosinit.hoc
NmdaAmpaSpineSynStim.hoc
NmdaAmpaSynStim.hoc
ObjectClass.hoc
plotscalingresults_pergroup1.m
plotscalingresults5.m
PointProcessDistributor.hoc
ReferenceAxis.hoc
removezeros.m
RPlot.hoc
scaling_plots.m
Segment.hoc
SimpleSpine.hoc
Spine.hoc
TreePlot.hoc
TreePlotArray.hoc
triexpsyn.inc
units.inc
utils.hoc
validate-bpap.hoc
VarList.hoc
VCaGraph.hoc
                            
if (name_declared("pkgversions") != 4 ) {  execute("strdef pkgversions") }
sprint(pkgversions,"%s, ", nrnversion(), pkgversions)
sprint(pkgversions,"%sbpap-gui = $Revision: 1.51 $, ",pkgversions)

load_file("nrngui.hoc")
load_file("bpap-dendburst.hoc")
load_file("bpap-somainj.hoc")
load_file("TreePlot.hoc")
load_file("ChannelBlocker.hoc")
load_file("VarList.hoc")
load_file("epspsizes.hoc")
load_file("TreePlotArray.hoc")
load_file("Integrator.hoc")
load_file("utils.hoc")
load_file("bpap-graphics.hoc")
load_file("bpap-spiketrain.hoc")
load_file("bpap-cell.hoc")
load_file("bpap-pars.hoc")
load_file("bpap-data.hoc")
load_file("bpap-run.hoc")

// Set up the parameter lists and the default parameters
pars_set_defaults()

// Need to set the cvode integrator tolerance here to speed up the  
// computation that occurs while loading the model
cell_set_cvode_atol(0.0001)

// Load the cell and extract some statistics from it
cell_setup_cell()

// Specify a SegmentRefList of synapse we always want to record from.
// segreflist.srl.o(screc_inputs_sri.x(0)).name     
screc_inputs_sri = new Vector()
objref seg 
apical_dendrite[5] seg = new SegmentRef(0.125)
screc_inputs_sri.append(segreflist.find_ind_of_segment(seg))
apical_dendrite[74] seg = new SegmentRef(0.5)
screc_inputs_sri.append(segreflist.find_ind_of_segment(seg))
apical_dendrite[51] seg = new SegmentRef(0.91666667 )
screc_inputs_sri.append(segreflist.find_ind_of_segment(seg))
apical_dendrite[28] seg = new SegmentRef(0.5 )
screc_inputs_sri.append(segreflist.find_ind_of_segment(seg))

parlist.appendVector("screc_inputs_sri")
screc_inputs_sr = new SegmentRefList()
for i=0, screc_inputs_sri.size() - 1 {
    screc_inputs_sr.append_segment(segreflist.srl.object(screc_inputs_sri.x(i)))
    //print screc_inputs_sri.x(i)
}

//create_and_distribute_screc_inputs()
create_and_distribute_inputs()

//
// Experimental setup 
// 

// Soma iclamp
setup_soma_iclamp()
tstop = 100

// Channel blockers
objref kapblocker, kadblocker
kapblocker = new ChannelBlocker("gkabar_kap",sl)
kadblocker = new ChannelBlocker("gkabar_kad",sl)

//
// User interface
// 

// Plots for the Soma EPSP amplidtude and transfer impedance
objref epsp_soma_amp_tp, transimp_tp
epsp_soma_amp_tp = new TreePlot(0)
epsp_soma_amp_tp.view(0, 0, 1300, 1,   375, 543, 250,150)
epsp_soma_amp_tp.auto_limits_on(1,1,1,1)
epsp_soma_amp_tp.set_xlab("path distance /\\mu m")
epsp_soma_amp_tp.set_ylab("EPSP amplitude at soma /mV")

transimp_tp = new TreePlot(0)
transimp_tp.view(0, 0, 1300, 100, 375, 750, 250,150)
transimp_tp.auto_limits_on(1,1,1,1)

// Plots for the Synapse EPSP amplidtude and transfer impedance
objref epsp_syn_amp_deck, inputimp_deck
objref epsp_syn_amp_tp[2], inputimp_tp[2]
epsp_syn_amp_deck = new Deck()
epsp_syn_amp_deck.intercept(1)
for i=0,1 {
    epsp_syn_amp_tp[i] = new TreePlot()
    epsp_syn_amp_tp[i].auto_limits_on(1,1,1,1)
    epsp_syn_amp_tp[i].set_ylab("EPSP amplitude at synapse /mV")
}
epsp_syn_amp_tp[0].set_xlab("Path distance /\\mu m")
epsp_syn_amp_tp[1].set_xlab("Effective amplitude /mV")

epsp_syn_amp_deck.intercept(0)
epsp_syn_amp_deck.map("Synapse EPSP amp vs distance or size", 650, 543, 250, 178)

inputimp_deck = new Deck()
inputimp_deck.intercept(1)
inputimp_tp[0] = new TreePlot()
inputimp_tp[0].auto_limits_on(1,1,1,1)
inputimp_tp[1] = new TreePlot()
inputimp_tp[1].auto_limits_on(1,1,1,1)
inputimp_deck.intercept(0)
inputimp_deck.map("Input impedance vs distance or size", 650, 750, 250, 178)

// Decks to put the plots in
objref deck
deck = new Deck()

// TreePlotArrays for plots vs distance and size
objref tpa[2]
deck.intercept(1)
rows = 2
cols = 3
for i=0,1 {
    tpa[i] = new TreePlotArray(rows,cols)
    tpa[i].rp.set_width(6)
    tpa[i].rp.set_height(8)
}

deck.intercept(0)
deck.map("Features vs distance or size", 375, 0  , 500, 465)
deck.flip_to(0)


// Plots for the  features of vtree versus distance
objref vtreemax_tp[2], vtreeint_tp[2], vsyndel_tp[2]
for i=0,1 {
    tpa[i].cellref(vtreemax_tp[i], 0, 0)
    vtreemax_tp[i].auto_limits_on(0,0,0,1)
    vtreemax_tp[i].set_ylab("Peak voltage /mV")
    // When in crosshair mode, pressing a key will call
    // tp_action()
    vtreemax_tp[i].graph.crosshair_action("tp_action",1)
    
    tpa[i].cellref(vtreeint_tp[i], 0, 1)
    vtreeint_tp[i].auto_limits_on(0,0,1,1)
    vtreeint_tp[i].set_ylab("Integral of voltage /\\mu Vs")
    
    tpa[i].cellref(vsyndel_tp[i], 0, 2)
    vsyndel_tp[i].auto_limits_on(0,0,1,1)
    vsyndel_tp[i].set_ylab("Delay to peak voltage /ms")
}

// Plots for the  features of casyn versus distance
objref casynmax_tp[2], casynint_tp[2], casyndel_tp[2] 
for i=0,1 {
    tpa[i].cellref(casynmax_tp[i], 1, 0)
    casynmax_tp[i].auto_limits_on(0,0,1,1)
    casynmax_tp[i].lines(0)
    casynmax_tp[i].marks(1)
    casynmax_tp[i].set_ylab("Peak Ca conc /mM")
    casynmax_tp[i].graph.crosshair_action("tp_action",1)

    tpa[i].cellref(casynint_tp[i], 1, 1)
    casynint_tp[i].auto_limits_on(0,0,1,1)
    casynint_tp[i].lines(0)
    casynint_tp[i].marks(1)
    casynint_tp[i].set_ylab("Integral of Ca conc /\\mu Ms")    
    tpa[i].cellref(casyndel_tp[i], 1, 2)
    casyndel_tp[i].auto_limits_on(0,0,1,1)
    casyndel_tp[i].lines(0)
    casyndel_tp[i].marks(1)
    casyndel_tp[i].set_ylab("Delay to peak Ca conc /ms")    
}

// This is the function called when a key is pressed in crosshair mode
// Args i, c, $o3, $o4
proc tp_action() {
    print $1, $2
    print segreflist.srl.object($1).name()
    segreflist.srl.object($1).secref.sec print distance(segreflist.srl.object($1).x)
    print "Perp_distance: ", perp_distances.x($1)
    segreflist.srl.object($1).secref.sec Shape[0].color($2 % 8)
}

// Function and panel to help flip the decks
proc _gui_decks_flip_to () { 
    deck.flip_to($1)
    epsp_syn_amp_deck.flip_to($1)
    inputimp_deck.flip_to($1)
}
_gui_decks_flip_to(0)

// Functions to make plotting voltage and calcium signals less painful
_gui_errorbars = 1   // Global specifying whether errobars are plotted

// _gui_plot(TreePlot tp, Vector measure, Vector quantity_mean, Vector quantity_stdev) 
// Procedure to plot quantity_mean versus meseaur in a TreePlot tp. The
// quantity_stdev argument is used to plot errorbars if the global variable
// _gui_errorbars is set to 1
proc _gui_plot() {
    $o1.errorbars(_gui_errorbars)
    $o1.plot($o2,$o3,parents,$o4)
    $o1.set_limits($o2.min(), $o2.max(), -70, 30)
    $o1.view_equal_plot()
}

// _gui_plot_inputs(TreePlot tp, Vector measure, Vector quantity_mean, Vector quantity_stdev) 
// Same as _gui_plot(), except only plot from segments which have inputs inserted 
proc _gui_plot_inputs() { 
    _gui_plot($o1, $o2.ind(scall_inputs_sri), $o3.ind(scall_inputs_sri), $o4.ind(scall_inputs_sri))
}

// The main panel
xpanel("Control panel") 
xlabel("Parameters")
parlist.xstatebutton("Dendburst","dendburst_state")
parlist.xstatebutton("Spikes from file","spiketime_state")
//xlabel(scstim_file)
//xbutton("Choose SC spike file","bpap_spiketrain_choose_file()")
parlist.xvalue("Ra", "cell_Ra")
parlist.xvalue("nsyn", "nsyn")
parlist.xvalue("AMPA gbar","gsbar_ampa")
parlist.xvalue("NMDA gbar","gsbar_nmda")
parlist.xstatebutton("AMPA synapses scaled","ampa_scaled")
parlist.xvalue("spine head diameter","headdiam")
parlist.xvalue("spine head length","headL")
parlist.xvalue("gmax_car_spine","gmax_car_spine")
parlist.xstatebutton("Current clamp","soma_iclamp_state")
parlist.xvalue("amp","soma_iclamp_amp")
parlist.xvalue("start time","soma_iclamp_del")
parlist.xstatebutton("Block KA channels","ka_blocked_state")
parlist.xvalue("Min KA inactivation tau","taulmin_ka")
parlist.xradiobuttons("Jitter type","jitter_type","Uniform","Normal")
parlist.xvalue("Jitter","jitter")
parlist.xvalue("Number of runs","nruns")
parlist.xvalue("EPSP start time","epsp_start")
xlabel("Run")
xbutton("BPAP Run ","gui_run_bpap()")
xbutton("Find EPSP amplitudes","gui_epsprun()")
xlabel("File")
xbutton("Save Parameters","parlist.save(\"\",\"pars\")")
xbutton("Load Parameters","parlist.load(\"\",\"pars\")")
xbutton("Save Parameters and Data","save_parameters_and_data_gui()")
xlabel("Plot features vs")
xradiobutton("distance", "_gui_decks_flip_to(0)")
xradiobutton("size", "_gui_decks_flip_to(1)")
xpanel(0,100)                           // Just below the main menu

strdef dataname
proc save_parameters_and_data_gui() {
    data_get_dataname(dataname, "dendburst")
    string_dialog("Enter name of parameter set",dataname )
    data_save_parameters_and_data(dataname)
}

// 
//  The procedure to run the simulation -- also redraws graphs
// 

// Set up the plot
graphics_lineup_shapeplot()
graphics_mark_synapses()
graphics_add_synrecs()

// Define the measures for the arrays of plots
measure[0] = distances
measure[1] = transimp
epsps_measured = 0

// Proc to run the simulation and collect statistics
proc gui_run_bpap() {
    // Check what our measure of size is
    if (!epsps_measured) { 
        print "bpap-interactive: warning: using transfer impedance as measure of size" 
    }
    
    run_bpap()
    // Do plots
    gui_bpap_plot() 
}

// Proc to plot various statistics from the BPAP run
proc gui_bpap_plot() { local i
    // Plot maxima
    for  i=0,1 {                        // For plots vs distance and size
        _gui_plot(vtreemax_tp[i], measure[i], vtreemax_mean, vtreemax_stderr)
        _gui_plot_inputs(casynmax_tp[i], measure[i], casrimax_mean, casrimax_stderr)
    }
    
    // Plot integrals
    for i=0,1 {
        _gui_plot(vtreeint_tp[i],  measure[i], vtreeint_mean, vtreeint_stderr)
        _gui_plot_inputs(casynint_tp[i], measure[i], casriint_mean, casriint_stderr)
    }
    
    // Plot delays
    for i=0,1 {
        vsyndel_tp[i].marks(1)
        vsyndel_tp[i].lines(0)
        _gui_plot_inputs(vsyndel_tp[i], measure[i], vsridel_mean, vsridel_stderr)
        _gui_plot_inputs(casyndel_tp[i], measure[i], casridel_mean,casridel_stderr, 0, nsyn-1)
    }
    
    // Plot impedance
    transimp_tp.plot(distances,transimp,parents)
    for i=0,1 {
        inputimp_tp[i].plot(measure[i],inputimp,parents)
        inputimp_tp[i].view_equal_plot()
    }
}

// Proc to find EPSP amplitudes
proc gui_epsprun() {
    // Set name for data
    data_get_epsprun_dataname(dataname)
    string_dialog("Enter name of parameter set", dataname)
    run_epsp()
    
    // Plots
    epsp_soma_amp_tp.plot(distances, epsp_soma_amp, parents)
    epsp_soma_amp_tp.view_equal_plot()
    epsp_soma_amp_tp.rp.set_width(3)
    epsp_soma_amp_tp.rp.set_height(3)
    for i=0,1 {
        epsp_syn_amp_tp[i].plot(measure[i], epsp_syn_amp, parents)
        epsp_syn_amp_tp[i].view_equal_plot()
        epsp_syn_amp_tp[i].rp.set_width(3)
        epsp_syn_amp_tp[i].rp.set_height(3)
    }
    
    // Print plots
    sprint(filename,"plots/%s-soma.eps",dataname)
    epsp_soma_amp_tp.rprintfile(filename,1)
    sprint(filename,"plots/%s-syn-distance.eps",dataname)
    epsp_syn_amp_tp[0].rprintfile(filename,1)
    sprint(filename,"plots/%s-syn-size.eps",dataname)
    epsp_syn_amp_tp[1].rprintfile(filename,1)
    
    // Save to file
    data_save_parameters_and_data(dataname)
}


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