A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)

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Accession:137259
The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.
Reference:
1 . Baker JL, Perez-Rosello T, Migliore M, Barrionuevo G, Ascoli GA (2011) A computer model of unitary responses from associational/commissural and perforant path synapses in hippocampal CA3 pyramidal cells. J Comput Neurosci 31:137-58 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Baker, John L [jbakerb at gmu.edu];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal cell; AMPA; NMDA; Glutamate;
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ca3-synresp
readme.html
cacumm.mod
cagk.mod *
cal2.mod *
can2.mod *
cat.mod *
distr.mod
exp2nmdar.mod
h.mod *
kadist.mod *
KahpM95.mod *
kaprox.mod *
kd.mod *
kdrca1.mod *
km.mod *
na3n.mod *
ama-c30573.CNG.hoc
ama-c31162.CNG.hoc
ama-c60361.CNG.hoc
ama-c62563.CNG.hoc
ama-c73164.CNG.hoc
ama-c81463.CNG.hoc
axon-common.hoc
bar-cell1zr.CNG.hoc
bar-cell2zr.CNG.hoc
bar-cell3zr.CNG.hoc
bar-cell4zr.CNG.hoc
bar-cell5zr.CNG.hoc
bar-cell6zr.CNG.hoc
bar-cell7zr.CNG.hoc
bar-cell8zr.CNG.hoc
demo.hoc
demo.png
demo.ses
demo-fig2a-raw-data.csv
demo-fig2a-raw-time.csv *
demo-fig2a-smoothed-data.csv
demo-fig2a-smoothed-time.csv *
mosinit.hoc
out-vc-ampar-c31162-ad67-022.csv
out-vc-ampar-c62563-ad2-01667.csv
out-vc-ampar-c62563-ad54-054.csv
out-vc-fastampar-c62563-ad2-01667.csv
out-vc-nmdar-c81463-ad87-082.csv
out-vc-nmdar-l51-ad7-036.csv
params-by-fig.csv
synresp.hoc
synresp-c30573.hoc
synresp-c31162.hoc
synresp-c60361.hoc
synresp-c62563.hoc
synresp-c73164.hoc
synresp-c81463.hoc
synresp-cell1zr.hoc
synresp-cell2zr.hoc
synresp-cell3zr.hoc
synresp-cell4zr.hoc
synresp-cell5zr.hoc
synresp-cell6zr.hoc
synresp-cell7zr.hoc
synresp-cell8zr.hoc
synresp-l24b.hoc
synresp-l51.hoc
synresp-l56a.hoc
tur-l24b.CNG.hoc
tur-l51.CNG.hoc
tur-l56a.CNG.hoc
                            
// Demo.hoc - demonstrate interactive use of the passive cell model by
// showing plots similar to those found in figure 2a of the article by
// Baker et al. (2010) Journal of Computational Neuroscience (online first).

// There are some assumptions made here. synresp.hoc is assumed to have
// isInteractive=1, runStim=0, isCC=0, useAMPAR=1, and useNMDAR=0.
// We also assume that the current directory is set to the directory
// containing the model .HOC files, which should be true as long
// as the demo was launched from the same directory as the model.


// Load the file that defines the target cell.
// This automatically loads synresp.hoc.
// Normally isInteractive will be set in synresp.hoc,
// so it is an error if this did not occur.

isInteractive = -1 // an invalid value
load_file("synresp-c31162.hoc")

objref errDialog
if (isInteractive<0) {

  errDialog = new VBox()
  errAns=errDialog.dialog("Required files were not loaded. Quit now?","Yes","No")
  if (errAns==1) quit()
}

// Load a session file defining the desired plots
load_file("demo.ses")

// Just to make sure, set critical parameters defining
// the simulation. Normally these should be set in
// synresp.hoc before starting the procedure.
isCC=0
useAMPAR=1
useNMDAR=0

// Run the simulation corresponding with figure 2A in the article,
// and put the command on the console for general information.
// By default, getSynResp runs the simulation only as long as
// necessary to extract response half-height width. Setting
// the continuation time, tCont, extends the simulation time to
// permit more of the trace to be plotted as in Figure 2a.

print "Running the simulation corresponding with Figure 2a"
print "apical_dendrite[67] {getSynResp(0.22)}"

tCont=100
apical_dendrite[67] {getSynResp(0.22)}


objref rawTime,rawData,rawFileX,rawFileY
objref smoothedTime,smoothedData,smoothedFileX,smoothedFileY
objref tracePlot

if (1) { // do the experimental data plot (or not)

print "Plotting experimental trace from Figure 2a"

// Read experimental data and add it to the plot.
// Data is in the form of column vectors of time and data values.
// To overlap the smoothed data trace with the simulation, the resting
// values must be aligned prior to plotting. Experimental data
// values have been converted to mA, the unit of measure for seClamp.
// iRest is set in funtion runIt() in synresp.hoc as a side-effect of
// getSynResp().
 
tracePlot = graphList[1].object(0)

rawTime = new Vector(700)
rawData = new Vector(700)
rawFileX = new File("demo-fig2a-raw-time.csv")
rawFileY = new File("demo-fig2a-raw-data.csv")

smoothedTime = new Vector(700)
smoothedData = new Vector(700)
smoothedFileX = new File("demo-fig2a-smoothed-time.csv")
smoothedFileY = new File("demo-fig2a-smoothed-data.csv")

rawFileX.ropen()
rawTime.scanf(rawFileX)
rawFileX.close()

rawFileY.ropen()
rawData.scanf(rawFileY)
rawFileY.close()

restAdj = iRest - rawData.x[0] + 0.005
for (i=0;i<rawData.size();i=i+1) {
  rawData.x[i]=rawData.x[i]+restAdj
}

smoothedFileX.ropen()
smoothedTime.scanf(smoothedFileX)
smoothedFileX.close()

smoothedFileY.ropen()
smoothedData.scanf(smoothedFileY)
smoothedFileY.close()

restAdj = iRest - smoothedData.x[0]
for (i=0;i<smoothedData.size();i=i+1) {
  smoothedData.x[i]=smoothedData.x[i]+restAdj
}

rawData.plot(tracePlot,rawTime,3,1)
smoothedData.plot(tracePlot,smoothedTime,2,1)

} // end experimental data plot

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