CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)

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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.
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;
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Synaptic Plasticity;
Implementer(s): Sterratt, David ; Groen, Martine R [martine.groen at];
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;
apical-non-trunk-list.hoc *
apical-trunk-list.hoc *
axonpoirazi.hoc *
axon-sec-list.hoc *
basal-paths.hoc *
basal-tree-list.hoc *
gabab-gabaa-uniform-ratio.hoc *
nmda-ampa-ratio.hoc *
oblique-paths.hoc *
old20100922cell2_topadj.hoc *
soma-list.hoc *
// This function makes a list with all sections on the axon
// written by Yiota Poirazi, July 2001,

objref axon_sec_list
axon_sec_list = new SectionList()

for i = 30,34 dendrite[i] {

for i = 18,22 dendrite[i] {

/* Make a shape graph showing the axon sections

objref axon_sec_list_shape
axon_sec_list_shape=new Shape()
color_index = 3
axon_sec_list_shape.color_list(axon_sec_list, color_index)

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