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]
Citations  Citation Browser
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("VarList.hoc")
objref p 
a = 5
p = new DoubleVar("a")
p.printf()

strdef s 
s = "bla"
objref p1 
p1 = new StringVar("s")
p1.printf()

objref vec
vec = new Vector(10)
vec.indgen()
objref p2
p2 = new VectorVar("vec")
p2.printf()

objref mat
mat = new Matrix(10,5)

objref null

objref l
l = new List()
l.append(vec)
l.append(mat)

objref pl
pl = new VarList()
b = 2
pl.appendDouble("a")
pl.appendDouble("b")
pl.appendString("s")
pl.appendSetDouble("c", 7.3846,"print c")
pl.appendSetString("y", "hello","print y")
pl.appendVector("vec")
pl.appendVector("null")
pl.appendMatrix("mat")
pl.appendVector("vec")                  // Duplicate; should replace previous
pl.appendList("l")  
pl.printf()

objref f
f = new FormatFile("test_VarList-out","hoc")
f.wopen()
pl.fprint(f)
pl.fprint_actions(f)
f.close()

f = new FormatFile("test_VarList-out","R")
f.wopen()
pl.fprint(f)
f.close()

f = new FormatFile("test_VarList-out-s","R", "s")
f.wopen()
pl.fprint(f)
f.close()

pl.printf_actions()
pl.execute_actions()

// pl.save()

xpanel("Parameters")
pl.xstatebutton("A variable","a")
pl.xvalue("Another variable","b")
xpanel()

xpanel("Parameters")
pl.xstatebutton("A variable","sdkh")
pl.xvalue("Another variable","b")
xpanel()