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
                            
STATE {
    A (uS)        : rising component
    B (uS)        : fast decyaing component
    C (uS)        : slow decyaing component
    tmax (ms)     : point at which function is maximal
}

NONLINEAR peak {
    ~ 1/taurise*exp(-tmax/taurise)-afast/taufast*exp(-tmax/taufast)-aslow/tauslow*exp(-tmax/tauslow) = 0
}

INITIAL {
    taurise = q10^(-(celsius-T_exp)/10(degC))*taurise_exp
    taufast = q10^(-(celsius-T_exp)/10(degC))*taufast_exp
    tauslow = q10^(-(celsius-T_exp)/10(degC))*tauslow_exp
    aslow = 1 - afast
    : Estimate time of peak
    tmax = log(taufast/taurise)/(1/taurise-1/taufast) 
    : normfac = -exp(-tmax/taurise)+afast*exp(-tmax/taufast)+aslow*exp(-tmax/tauslow)
    : printf("tmax: %g, normfac: %g\n", tmax, normfac)
    : Find time of peak and normfac numerically
    : (seem to be quite similar to estimate, so maybe this is a waste of time!)
    SOLVE peak
    normfac = -exp(-tmax/taurise)+afast*exp(-tmax/taufast)+aslow*exp(-tmax/tauslow)
    : printf("tauslow: %g, tmax: %g, normfac: %g\n", tauslow, tmax, normfac)
    total = 0
    A = 0
    B = 0
    C = 0
}

DERIVATIVE state {
    A' = -A/taurise
    B' = -B/taufast
    C' = -C/tauslow
}

NET_RECEIVE(weight (uS)) {
    LOCAL normweight
    normweight = weight/normfac
    state_discontinuity(A, A + normweight)
    state_discontinuity(B, B + normweight*afast)
    state_discontinuity(C, C + normweight*aslow)
    total = total+normweight
}

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