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
tests
test_FormatFile.hoc
test_VarList.hoc
                            
load_file("FormatFile.hoc")

objref vec, outfile
objref mat, l
strdef str
chooser_test=0

proc print_tests() {
    // Double
    a = 5 
    outfile.printdouble("a",a)
    
    // empty vector
    vec = new Vector()
    outfile.printvec("vec",vec) 
    
    // Short vector
    vec.indgen(1,10,1)
    outfile.printvec("vec",vec)
    
    //  Partial printing
    outfile.printvec("vec",vec,4,7)
    
    // Longer vector
    vec.indgen(1,25,1)
    outfile.printvec("vec",vec)
    //     printmvec(outfile,vec,"vec",4,7)
    //    oprintmvec(outfile,vec,"vec",4,22)
    
    // A vector just on the block size
    vec.indgen(1,100,1)
    outfile.printvec("vec",vec)
    outfile.printvec("vec",vec,55,72)
    
    // A vector bigger than the block size
    vec.indgen(1,101,1)
    outfile.printvec("vec",vec)
    
    // A matrix
    mat = new Matrix(6,101)
    for i = 0, 5 {
        mat.setrow(0, vec)
    }
    outfile.printmat("mat",mat)
    
    str = "blee blee blee2"
    outfile.printstr("str",str)
    
    // Printing types without specifying type
    outfile.printobj("obja",a)
    outfile.printobj("objvec",vec)
    outfile.printobj("objmat",mat)
    outfile.printobj("objstr",str)
    
    // List test
    l = new List()
    l.append(vec)
    l.append(mat)
    outfile.printlist("l", l)
}    

// Should complain about this
outfile = new FormatFile("test_FormatFile.null","skdfh")

outfile = new FormatFile("test_FormatFile")

outfile.wopen()
print_tests()
outfile.close()

outfile = new FormatFile("test_FormatFile","R")
outfile.wopen()
print_tests()
outfile.close()

outfile = new FormatFile("test_FormatFile-struct","R","r")
outfile.wopen()
print_tests()
outfile.close()

// Chooser test
if (chooser_test==1) {
    outfile = new FormatFile("","R")
    outfile.chooser("w","Hello")
    outfile.chooser()
    outfile.wopen()
    print_tests()
    outfile.close()
}

// Hoc test

outfile = new FormatFile("/tmp/test_FormatFile","hoc")
outfile.wopen()
print_tests()
outfile.close()



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