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
                            
source("figures-common.R")

## Set colours of lines
palette(c("black","gray","red","blue","green","orange"))

## Change name to dataset in datastore directory
dataset <- "dendburst-s250-j01t1-a200-n45-bv-r170-sc1-Ra050-nr0005-cv1"
dataset <- "ca1_poirazi-dendburst-s240-j00t1-a200-n45-bv-r170-sc0-Ra050-nr0100-cv1"

## Load the data
r <- get.dataset(dataset, dir=".")

## Uncomment next 3 lines if you want to print to file
colwidth <- 3.6                         # Width of column in paper
stdpostscript(file=dataset, group="", width=colwidth, height=3)
## par(mfrow=c(3, 3))                      # Sets 2x2 grid
stdpars()
  
## Top row: peak
## figmessage("Figure A: m.p amp vs. distance")
## vrest <- -70
## plot.feature.dist(r, feature=r$vsrimax_mean - vrest, liw=r$vsrimax_stderr,
##                   ylab="Peak m.p. amp. (mV)")
## panlabel("A")

## figmessage("Figure B: m.p integral vs. distance")
## plot.feature.dist(r, feature=r$vsriint_mean-r$tstop*vrest,
##                   liw=r$vsriint_stderr,
##                   sem.max=10,
##                   ylab=expression(paste("m.p. integral (", mu, "Vs)")))
## panlabel("B")

## figmessage("Figure C: m.p. delay vs. distance")
## plot.feature.dist(r, feature=r$vsridel_mean, liw=r$vsridel_stderr,
##                   ylab="Delay to peak m.p. (ms)",
##                   legend.pos="topleft")
## panlabel("C")
  
## ## Bottom row: delay

## figmessage("Figure D: Ca amp vs. distance")
## plot.feature.dist(r, feature=r$casrimax_mean*1000, liw=r$casrimax_stderr,
##                   ylab=expression(paste("Peak [Ca] (", mu, "M)",sep="")))
## panlabel("D")

## figmessage("Figure 2E: Ca integral vs. distance")
## plot.feature.dist(r, feature=r$casriint_mean, liw=r$casriint_stderr,
##                   ylab=expression(paste("[Ca] integral (", mu, "Ms)",sep="")))
## panlabel("E")

## figmessage("Figure F: Ca delay vs. distance")
## plot.feature.dist(r, feature=r$casridel_mean, liw=r$casridel_stderr,
##                   ylab="Delay to peak [Ca] (ms)",
##                   legend.pos="topleft")
## panlabel("F")

figmessage("Figure G: m.p width vs. distance")
plot.feature.dist(r, feature=r$vsriwidth_mean,
                  liw=r$vsriwidth_stderr/1000,
                  ylab=expression(paste("m.p. half-width (ms)")))
panlabel("G")

## figmessage("Figure H: m.p width vs. m.p")
## plot(r$vsrimax_mean - vrest, r$vsriwidth_mean,
##      xlab="Peak m.p. amp. (mV)",
##      ylab=expression(paste("m.p. half-width (ms)")))
## panlabel("H")


## Uncomment if printing to file
dev.off()

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