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
                            
// Version information
if (name_declared("pkgversions") != 4 ) {  execute("strdef pkgversions") }
sprint(pkgversions,"%sepspsizes = $Revision: 1.18 $, ",pkgversions)

// Required packages
load_file("utils.hoc")
load_file("bpap-record.hoc")
load_file("bpap-dendburst.hoc")
load_file("NmdaAmpaSynStim.hoc")
load_file("NmdaAmpaSpineSynStim.hoc")
load_file("SimpleSpine.hoc")

// For a spine with an AMPA/NMDA synapse placed at each position 
// on the dendrtic tree, we would like to know:
// * The EPSP amplitude at the soma
// * The EPSP amplitude at the spine
// * The Ca amplitude at the spine
//
// We would also like to know the EPSP amplitude of the standard
// spine/synapse placed at the soma, as this can then be used to 
// compute the attenuation associated with any point on the dendritic
// tree. 
// 
// To allow us to compute the EPSP amplitude and Ca amplitude of an 
// individual synapse when there is activity, we first need to perform
// a baseline simulation where we measure the membrane potential at
// the soma and  the EPSP and Ca amplitude at each spine.
//
// Hence the pseudo-code reads:
// 
// insert_spine(positions)
// record(spines,features,baseline) 
// record(soma  ,v       ,baseline)
// run()
// 
// record_remove()
//
// foreach position {
//   insert_spine(position)
//   record(spine ,features,active)
//   record(soma  ,v       ,active)
//   run()
//   record_remove()
// }   

// The business
objref syn, nc, netstim
objref spine
objref epsp_soma,     epsp_syn
objref epsp_soma_amp, epsp_syn_amp
objref epsp_ca_syn
objref epsp_ca_syn_amp
objref baseline_soma,     baseline_syn
objref baseline_soma_amp, baseline_syn_amp
objref baseline_ca_syn
objref baseline_ca_syn_amp
objref baseline_synapse
objref epsp_attenuation
objref epsp_srl

proc measure_epspsizes() { local x, i, ncols, maxntestsegs
    if (numarg() == 1 ) {
        maxntestsegs = utils.min(1+$1,segreflist.srl.count()) // Maximum segment index to test
    } else {
        maxntestsegs = segreflist.srl.count()
    }
        
    // Set up storage space for baseline and EPSP recordings
    baseline_synapse = new List()
    baseline_soma = new List()
    baseline_syn = new List()
    baseline_ca_syn = new List()
    epsp_soma = new List()
    epsp_syn = new List()
    epsp_ca_syn = new List()
    
    baseline_soma_amp   = new Vector(maxntestsegs)
    baseline_syn_amp    = new Vector(maxntestsegs)
    baseline_ca_syn_amp = new Vector(maxntestsegs)
    epsp_soma_amp       = new Vector(maxntestsegs)
    epsp_syn_amp        = new Vector(maxntestsegs)
    epsp_ca_syn_amp     = new Vector(maxntestsegs)
    epsp_attenuation    = new Vector(maxntestsegs)
    
    //
    // Baseline run
    //
    
    // Add spines and recordings
    for i= 0, maxntestsegs - 1 {        
        baseline_soma.append(new Vector()) 
        baseline_syn.append(new Vector())           
        baseline_ca_syn.append(new Vector())           

        // Set up spines
        segreflist.srl.object(i).secref.sec baseline_synapse.append(new NmdaAmpaSpineSynStim(segreflist.srl.object(i).x))
        
        // Set up no input to the synapses
        baseline_synapse.object(i).netstim.number = 0
        
        // Set up recordings
        baseline_soma.object(i).record(&v(0),Dt)
        baseline_synapse.object(i).spine.head baseline_syn.object(i).record(&v(0.5),Dt)
        baseline_synapse.object(i).spine.head baseline_ca_syn.object(i).record(&cai(0.5),Dt)
    }
    
    // run the simulation
    run()
    
    // Remove recordings
    for i= 0, maxntestsegs - 1 {        
        baseline_syn.object(i).play_remove()
        baseline_ca_syn.object(i).play_remove()
        baseline_soma.object(i).play_remove()
    }
    // Remove synapses
    baseline_synapse.remove_all()
    
    //
    // EPSP runs    
    // 
    
    // Iterate through segments
    for i= 0, maxntestsegs - 1 {        
        // Go through each segment in turn
        // Locate a spine
        segreflist.srl.object(i).secref.sec syn = new NmdaAmpaSpineSynStim(segreflist.srl.object(i).x)
        
        // Set parameters
        syn.ampasyn.e = e_ampa
        syn.netcon.weight = gsbar_ampa
        
        if (name_declared("gsbar_nmda")) { 
            syn.nmdasyn.e = e_nmda
            syn.nmdanetcon.weight = gsbar_nmda
            syn.spine.head insert cacum
            syn.spine.head insert car_mag
            set_spine_pars(syn)
        }
        
        // netcon parameters
        syn.netstim.noise = 0
        syn.netstim.start = epsp_start
        syn.netstim.interval = tstop
        syn.netstim.number = 1
        
        //
        // Recordings
        // 
        epsp_soma.append(new Vector()) 
        epsp_syn.append(new Vector())           
        epsp_ca_syn.append(new Vector())           
        
        epsp_soma.object(i).record(&v(0),Dt)
        syn.spine.head epsp_syn.object(i).record(&v(0.5),Dt)
        syn.spine.head epsp_ca_syn.object(i).record(&cai(0.5),Dt)
                
        // Run the simulation
        run()
        
        // Analysis
        epsp_soma.object(i).sub(baseline_soma.object(i))
        epsp_syn.object(i).sub(baseline_syn.object(i))
        epsp_ca_syn.object(i).sub(baseline_ca_syn.object(i))
        
        epsp_soma_amp.x(i) =   epsp_soma.object(i).max()
        epsp_syn_amp.x(i) =    epsp_syn.object(i).max()
        epsp_ca_syn_amp.x(i) = epsp_ca_syn.object(i).max()
        
        // This assumes that the soma is first segment in segreflist
        epsp_attenuation.x(i) = 1-(epsp_soma_amp.x(i)/epsp_soma_amp.x(0))
        
        // Remove recordings
        epsp_syn.object(i).play_remove()
        epsp_ca_syn.object(i).play_remove()
        epsp_soma.object(i).play_remove()
    }
    
    // Add to data list
    datalist.appendVectorList("epsp_soma", "", 0)
    datalist.appendVectorList("epsp_syn",  "", 0)
    datalist.appendVectorList("epsp_ca_syn", "", 0)
    datalist.appendVector("epsp_soma_amp")
    datalist.appendVector("epsp_syn_amp")
    datalist.appendVector("epsp_ca_syn_amp")
    datalist.appendVector("epsp_attenuation")
}






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