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-cell = $Revision: 1.4 $, ",pkgversions)

load_file("bpap-data.hoc")
load_file("Segment.hoc")
load_file("ReferenceAxis.hoc")

//
// Cell model
// 

// Proc to set Ra in each section from global variable cell_Ra
proc cell_set_cell_pars() {
    forall Ra = cell_Ra
}

// Proc to turn on cvode and record this in cvode_on so it can be recorded
proc cell_set_cvode_active() {
    cvode_active($1)
    cvode_on = $1
}

// Proc to set cvode atol and save it in cvode_atol so it can be recorded
proc cell_set_cvode_atol() {
    cvode_atol = $1
    cvode.atol(cvode_atol)
}

// Proc to load the cell model
strdef cmd
proc cell_load_cell_model() {
    // Load Neuron file    
    sprint(cmd, "load_file(\"%s.hoc\")",$s1)
    execute1(cmd)
}

strdef cell_model
objref base, apex
//create apical_dendrite[93] // COMMENTED OUT BY MR GROEN, double defined
objref measure[2]

// Procedure to load the cell model and determine some statistics of
// the cell
proc cell_setup_cell () {
    ra_adjustment = 0
    cell_set_cvode_active(1)
    cell_load_cell_model(cell_model)    
    cell_set_cell_pars()
    
    // This should give a maxium effective L of leas than 0.2 during synaptic stimulation without a spike
    // and an effective L of less than 0.5 when there is a spike
    // Verify this with:
    // forsec "apical" print L/nseg/length_constant(Ra, 1/get_max_g_in_sim(), diam) 
    
    forsec "apical" cell_set_max_length_by_length_constant(1) 
    cell_statistics()
    cell_find_erest()
    cell_measure_impedance()
}

// Find resting potential
erest=0
proc cell_find_erest() { 
    run()
    erest=v(0.5)
}

// Procedure to set up some useful lists and do some statistics on
// the cell 

objref sl
objref ra
objref diams, distances, perp_distances
objref segreflist, parents
objref trunk_segreflist, trunk_sri
objref apical_trunk_list

proc cell_statistics() { local aptf, i
    print "Calculating cell statistics"
    
    // Set default section as origin for measuring distances from
    distance()
    
    // Set up the reference axis for measuring perpendicular distance 
    // and distributing synapses
    ra = new ReferenceAxis()
    ra.setup_with_segrefs(base, apex, ra_adjustment)
    apexdist = sqrt(ra.axis.sumsq())+ra.adjustment
    datalist.appendSetDouble("apexdist ", apexdist)
    
    // Make a section list of the apical dendrites
    sl = new SectionList() 
    forsec "apical" sl.append()	
    
    // Make a list of the segments from the section list
    segreflist = new SegmentRefList()
    // Append only *internal* segments as point processes that use
    // ca_ion cannot by placed on 0 or 1
    
    segreflist.append_segments_from_section(0.5)
    segreflist.append_internal_segments_from_seclist(sl)
    segreflist.parents(parents)
    
    // Make a list of inds of segrefs that are on the trunk
    trunk_segreflist = new SegmentRefList()
    trunk_segreflist.append_internal_segments_from_seclist(apical_trunk_list)
    trunk_sri = new Vector()
    for i=0, trunk_segreflist.count()-1 {
        trunk_sri.append(segreflist.find_ind_of_segment(trunk_segreflist.srl.o(i)))
    }
    
    // Section diameters and distances
    diams = new Vector()
    distances = new Vector()
    perp_distances = new Vector()
    
    // Set number of segments
    nsegs = segreflist.srl.count()
    // Go through each segment in turn
    for i = 0, nsegs-1 {
        segreflist.srl.object(i).secref.sec {
            // Write diameters
            diams.append(diam(segreflist.srl.object(i).x))
        
            // Write distances
            distances.append(distance(segreflist.srl.object(i).x))
        }
        
        perp_distances.append(ra.apply_expr_to_segref(segreflist.srl.object(i),"H*l")/ra.apply_expr_to_segref(segreflist.srl.object(i),"l"))
    }
    
    // Write to list
    datalist.appendVector("diams")
    datalist.appendVector("distances")
    datalist.appendVector("perp_distances")
    datalist.appendVector("parents")
    datalist.appendVector("trunk_sri")
}    

// Find the theoretical maxium conductance in each section
func cell_get_max_gbar() { local gbar
    gbar = 0 
    if (ismembrane("hha2")) { gbar += gkbar_hha2 }
    if (ismembrane("hha2")) { gbar += gnabar_hha2 }
    if (ismembrane("hha2")) { gbar += gl_hha2 }
    if (ismembrane("hha_old")) { gbar += gkbar_hha_old }
    if (ismembrane("hha_old")) { gbar += gnabar_hha_old }
    if (ismembrane("hha_old")) { gbar += gl_hha_old }
    if (ismembrane("pas"))  { gbar += g_pas }
    if (ismembrane("h"))    { gbar += gbar_h }
    if (ismembrane("kap"))  { gbar += gkabar_kap }
    if (ismembrane("kad"))  { gbar += gkabar_kad }
    if (ismembrane("km"))   { gbar += gbar_km * 1E-4 }
    if (ismembrane("cal"))   { gbar += gcalbar_cal }
    if (ismembrane("cat"))   { gbar += gcatbar_cat }
    if (ismembrane("somacar"))   { gbar += gcabar_somacar }
    if (ismembrane("kca"))   { gbar += gbar_kca }
    if (ismembrane("mykca"))   { gbar += gkbar_mykca }
    if (ismembrane("calH"))   { gbar += gcalbar_calH }
    return(gbar)
}

// Find the maximum possible value of g in each channel after a run
// This will be an overestimate, since not all channels will have been 
// at their most open at the same time
func cell_get_max_g_in_run() { local gmax
    gmax = 0 
    if (ismembrane("hha2")) { gmax += gmax_hha2 }
    if (ismembrane("hha_old")) { gmax += gmax_hha_old }
    if (ismembrane("pas"))  { gmax += g_pas }
    if (ismembrane("h"))    { gmax += gmax_h }
    if (ismembrane("kap"))  { gmax += gmax_kap }
    if (ismembrane("kad"))  { gmax += gmax_kad }
    if (ismembrane("km"))   { gmax += gmax_km }
    if (ismembrane("cal"))   { gmax += gmax_cal }
    if (ismembrane("cat"))   { gmax += gmax_cat }
    if (ismembrane("somacar"))   { gmax += gmax_somacar }
    if (ismembrane("kca"))   { gmax += gmax_kca }
    if (ismembrane("mykca"))   { gmax += gmax_mykca }
    if (ismembrane("calH"))   { gmax += gmax_calH }
    return(gmax)
}

//
// length_constant(Ri, Rm, d) 
// Compute the length constant in um in a section where the units of 
// Ri are Ohm-cm
// Rm is  Ohm-cm2
// diam is um
func cell_length_constant() { local Ri, Rm , d
    Ri = $1 * 1E4                       // Convert Ohm-cm to Ohm-um
    Rm = $2 * 1E8                       // Convert Ohm-cm2 to Ohm-um2
    d  = $3 
    return(sqrt(Rm*d/(4*Ri)))
}

func cell_get_length_by_length_constant() {
    return(L/nseg/cell_length_constant(Ra, 1/cell_get_max_gbar(), diam))
}

// Set the maxumum length as a fraction of the length constant by increaseing the number of segments
proc cell_set_max_length_by_length_constant() { local lmax, l, n
    lmax = $1                           // Maximum allowed length in terms of length constant
    l = cell_get_length_by_length_constant() // Actual length of each segment
    if (l > lmax) {
        nseg = nseg * l / lmax          // Increase nseg 
        if (nseg % 2) { nseg += 1 }     // Ensure nseg is odd
    }
}

// The business
objref imp
objref transimp, inputimp
transimp = new Vector()
inputimp = new Vector()

proc cell_measure_impedance() { local i, maxntestsegs
    // We seem to need to initialise the cell first
    init()
    
    maxntestsegs = segreflist.srl.count()
    
    // New impedance object
    imp = new Impedance()
    
    // Locate voltage electrode in soma
    imp.loc(0)
    
    // Compute transfer impdeance at 0Hz between this point and all other points 
    imp.compute(0)
    
    // Now get the transfer impedance for each segment in turn
    transimp.resize(maxntestsegs)
    inputimp.resize(maxntestsegs)
    for i= 0, maxntestsegs - 1 {        
        segreflist.srl.object(i).secref.sec {
            transimp.x(i) = imp.transfer(segreflist.srl.object(i).x)
            inputimp.x(i) = imp.input(segreflist.srl.object(i).x)
        }
    }
    datalist.appendVector("transimp")
    datalist.appendVector("inputimp")
}

proc cell_print_trunk_distances() { local i
    for i=0, trunk_sri.size()-1 {
        segreflist.srl.o(trunk_sri.x(i)).print_distance()
    }
}

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