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,"%sbpap-dendburst = $Revision: 1.22 $, ",pkgversions)

load_file("bpap-cell.hoc")
load_file("bpap-record.hoc")
load_file("utils.hoc")
load_file("PointProcessDistributor.hoc")
load_file("NmdaAmpaSynStim.hoc")
load_file("NmdaAmpaSpineSynStim.hoc")
load_file("bpap-data.hoc")

//
// Synapses on the SC input to the apical dendrite.  This is a similar
// situation to Spruston et al. (1995).  We would like the inputs to 
// be distributed within 100 to 450 um measured perpendicular from the
// pyramidal cell layer (Megias et al. 2001).  To do this we have to use
// the routines of Poirazi, perhaps coupled with my PointProcessDistributor 
// object
// 

objref ppd
ppd = new PointProcessDistributor(1)
strdef tmpstr

objref screc_inputs_sr
// These objrefs will be lists that contain NmdaAmpaSpineSynStim (syn) objects 
objref screc_inputs                        // SC "Recorded"       
objref scthin_inputs                       // SC thin dendrites   
objref scthickprox_inputs                  // Proximal SC         
objref scthickmed_inputs                   // Medial SC           
objref scthickdist_inputs                  // Distal SC           
objref scall_inputs                        // All SC              

// These objrefs will be vectors that contain the indicies of the segrefs
// on which the NmdaAmpaSpineSynStim are located
objref screc_inputs_sri                    // SC "Recorded"       
objref scthin_inputs_sri                   // SC thin dendrites   
objref scthickprox_inputs_sri              // Proximal SC         
objref scthickmed_inputs_sri               // Medial SC           
objref scthickdist_inputs_sri              // Distal SC           
objref scall_inputs_sri                    // All SC              

//
// create_and_distribute_synapses(List synstim, int nsyn, Vector segrefs)
// 
// Function to create the NmdaAmpaSpineSynStim objects, set their parameters
// and distribute them using the ppd PointProcessDistributor object.
// The list of NmdaAmpaSpineSynStim objects is placed in SYNSTIM, NSYN is the number 
// of synapses to create, and the indicies of the segment references 
// in which the NmdaAmpaSpineSynStim objects are located are placed in SEGREFS.
// 
proc create_inputs() { local n, i
    n = int($2)
    // Create the synapses
    for i=$o1.count(), $o1.count() + n - 1 {
        $o1.append(new NmdaAmpaSpineSynStim(-1)) // -ve arg prevents location
        $o1.object(i).spine.head {
            insert cacum
            insert car_mag
        }
        set_input_pars($o1.object(i))
    }
}

proc destroy_inputs() { local nsyn, i
    nsyn = int($2)
    // Destroy the synapses
    for i=0, nsyn-1 {
        $o1.object($o1.count()-1).destroy() // Destructor method
        $o1.remove($o1.count()-1)  // Remove ref from list
    }
}


// 
// Functions to set parameters
// 
proc set_spine_pars() {
    $o1.spine.head tau_cacum = tauca
    $o1.spine.head cai0_cacum = cai0
    $o1.spine.head kappa_cacum = kappa
    // Assuming uniform concentration in the entire spine head is 
    // equivalent to a shell of depth diam/4. The 1E3 is to convert
    // into nm from um
    $o1.spine.head.diam = headdiam
    $o1.spine.head.L =    headL
    $o1.spine.neck.diam = neckdiam
    $o1.spine.neck.L    = neckL
    $o1.spine.head depth_cacum = ($o1.spine.head.diam/4) * 1E3
    $o1.spine.head gmax_car_mag = gmax_car_spine*1E-12/(area(0.5)*1E-8)
}

proc set_stim_pars() {
    $o1.netstim.noise = noise
    if (dendburst_state) {
        $o1.netstim.start = start
    } else {
        $o1.netstim.start = tstop + 1
    }
    $o1.netstim.interval = interval
}

proc set_syn_pars() {
    $o1.ampasyn.e = e_ampa
    $o1.netcon.weight = gsbar_ampa 
    $o1.nmdasyn.e = e_nmda
    $o1.nmdanetcon.weight = gsbar_nmda 
}

proc set_input_pars() {
    set_spine_pars($o1)
    set_stim_pars($o1)
    set_syn_pars($o1)
}

proc disable_synapses() {
    for i=0, scall_inputs.count()-1 {
        scall_inputs.object(i).netstim.start = tstop + 1
    }
}

// Enable first nsyn synapses 
proc enable_synapses() { local i 
    for i=0, scall_inputs.count()-1 {
        scall_inputs.object(i).netstim.number = tstop/interval * 100
    }
}

objref scalefac
datalist.appendVector("scalefac")
proc set_all_input_pars() { local i
    for i=0, scall_inputs.count()-1 {
        set_input_pars(scall_inputs.object(i))        // Schaffer collateral synapses
    }
    scalefac = new Vector(scall_inputs_sri.size())
    if (ampa_scaled) {
        cell_measure_impedance()             // Make sure the transimp exists
        scalefac.fill(transimp.ind(scall_inputs_sri).mean())
        scalefac.div(transimp.ind(scall_inputs_sri))
        for i=0, scall_inputs.count()-1 {
            scall_inputs.object(i).netcon.weight *= scalefac.x(i)
        }
    } else {
        scalefac.fill(1)
    }
}

// At one point (rev 1.18 and under), the idea was to create a number
// of synapses and then activate a certain number (nsynmax) of them to
// change the number of active synapses. However, now we want to
// create a different distribution of synapses over the dendritic tree
// for each simulation.  This means that we will create all the
// synapses required on each simulation run, and destroy them
// afterwards. This is not as efficient, but it removes the need to do
// the permuations which served to randomise the synapses in the rev
// 1.18 and under.

// Set up the probability distributions of synapses The dendritic tree
// is divided into four different zones: Thin branches, and thick
// proximal, medial and distal branches. The fraction of synapses in
// each zone are given by thinfrac, thickproxfrac, thickmedfrac, and
// thickdistfrac.

scall_inputs = new List()
objref nsyn_zone, ratio
objref screc_inputs, screc_inputs_sri
proc create_and_distribute_inputs() { local n, i
    // We may need to create inputs
    if (nsyn > scall_inputs.count()) {
        create_inputs(scall_inputs, nsyn - scall_inputs.count())
    }
    // Or delete excess inputs
    if (nsyn < scall_inputs.count()) {
        destroy_inputs(scall_inputs, scall_inputs.count() - nsyn)
    }
    
    // create_and_distribute_screc_inputs() 
    // screc_inputs_sr is a list of the SegmentRefs of the synapses which we wish to record from
    // it has been created  elsewhere (perhaps bpap-interactive.hoc) with commands like
    // objref screc_inputs_sr
    // screc_inputs_sr = new SegRefList()
    // apical_dendrite[57] screc_inputs_sr.append_segements_from_section(0.3)
    // apical_dendrite[62] screc_inputs_sr.append_segements_from_section(0.5)
    // Get index of screc_inputs_sr in segfreflist
    //    segreflist.find_ind(screc_inputs_sri, screc_inputs_sr)
    // Then locate them on the tree 
    n = screc_inputs_sr.count()
    for i=0, (n - 1) {
        screc_inputs_sr.srl.object(i).secref.sec scall_inputs.object(i).loc(screc_inputs_sr.srl.object(i).x)
    }
    utils.list_copy(screc_inputs, scall_inputs, 0, n - 1)
    
    // Find the number of synapses per zone, nsyn_per_zone, given the fraction of synapses in each zone and 
    // the total number of synapses 
    ratio = new Vector(4)
    ratio.x(0) = thinfrac
    ratio.x(1) = thickproxfrac
    ratio.x(2) = thickmedfrac
    ratio.x(3) = thickdistfrac
    utils.mul_ratio_round(nsyn_zone, nsyn - n, ratio)
    
    // Synapses on thin dendrites
    sprint(tmpstr, "((d<1.5) && (H<%f))*l" , apexdist)
    ppd.setup_probs_from_seglist_with_refaxis(segreflist, ra, tmpstr)
    //    create_inputs(scthin_inputs, nsyn_zone.x(0))
    utils.list_copy(scthin_inputs, scall_inputs, n, n  + nsyn_zone.x(0) - 1)
    ppd.distribute_pps_from_list(scthin_inputs, scthin_inputs_sri)
    n = n + nsyn_zone.x(0)
    
    // Synapses on thick proximal dendrites
    sprint(tmpstr, "((d>=1.5) && (H<%f))*l" , apexdist * 100/450)
    ppd.setup_probs_from_seglist_with_refaxis(segreflist, ra, tmpstr)
    utils.list_copy(scthickprox_inputs, scall_inputs, n, n + nsyn_zone.x(1) - 1)
    ppd.distribute_pps_from_list(scthickprox_inputs, scthickprox_inputs_sri)
    n = n + nsyn_zone.x(1)
    
    // Synapses on thick medial dendrites
    sprint(tmpstr, "((d>=1.5) && (H>=%f) && (H<%f))*l" , apexdist * 100/450, apexdist* 250/450)
    ppd.setup_probs_from_seglist_with_refaxis(segreflist, ra, tmpstr)
    utils.list_copy(scthickmed_inputs, scall_inputs, n, n + nsyn_zone.x(2) - 1)
    ppd.distribute_pps_from_list(scthickmed_inputs, scthickmed_inputs_sri)
    n = n + nsyn_zone.x(2)
    
    // Synapses on thick medial dendrites
    sprint(tmpstr, "((d>=1.5) && (H>=%f) && (H<%f))*l" , apexdist * 250/450, apexdist)
    ppd.setup_probs_from_seglist_with_refaxis(segreflist, ra, tmpstr)
    utils.list_copy(scthickdist_inputs, scall_inputs, n, n + nsyn_zone.x(3) - 1)
    ppd.distribute_pps_from_list(scthickdist_inputs, scthickdist_inputs_sri)
    
    // Make federated list
    scall_inputs_sri = new Vector()
    scall_inputs_sri.append(screc_inputs_sri, scthin_inputs_sri, scthickprox_inputs_sri, scthickmed_inputs_sri,  scthickdist_inputs_sri)
    
    // Set input parameters
    set_all_input_pars()
    
    // Turn on synapses
    enable_synapses()
}

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