Activity dependent conductances in a neuron model (Liu et al. 1998)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:93321
"... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales."
Reference:
1 . Liu Z, Golowasch J, Marder E, Abbott LF (1998) A model neuron with activity-dependent conductances regulated by multiple calcium sensors. J Neurosci 18:2309-20 [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):
Channel(s): I Na,t; I L high threshold; I T low threshold; I A; I K; I K,Ca; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Bursting; Temporal Pattern Generation; Homeostasis;
Implementer(s): Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  I Na,t; I L high threshold; I T low threshold; I A; I K; I K,Ca; I Potassium;
// fig3bottom.ses
// Tom Morse 20080809
// This figure was created by
// 1) creating seven windows by
// the NEURON main menu: Graph -> State Axis, right click on graph
// then select "Plot what?" and then selecting each conductance, e.g.
// soma.gbarcat_gbarcat.  
// 2) saving all seven windows simultaneously under the NEURON main
// menu: Window -> Print and File Window Manager.  (I clicked on all
// seven windows and then saved them under Session: fig3bottom.ses file name)
// 3) I opened this file in an editor and added fig3bottom_g[] variables
// so that I could control these windows by hoc code.  I also added HBox
// statements so that the windows would appear similarly to the paper.
// and I added repositionlabel_ variables
objref hbox
hbox = new HBox()
hbox.intercept(1)
objref fig3bottom_g[7]  // used to execute View = plot latter
repositionlabel_x = 0.95 // 0-1 represents the graph size for these
repositionlabel_y = 0.47

objectvar save_window_, rvp_
objectvar scene_vector_[10]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{
save_window_ = new Graph(0)
fig3bottom_g[0] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[6] = save_window_
{save_window_.view(0, 0, 500000, 1, 347, 736, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarcat_gbarcat( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[1] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[4] = save_window_
{save_window_.view(0, 0, 500000, 1, 127, 658, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarcas_gbarcas( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[2] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[5] = save_window_
{save_window_.view(0, 0, 500000, 1, 16, 735, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbara_gbara( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[3] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[7] = save_window_
{save_window_.view(0, 0, 500000, 1, 676, 734, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarkca_gbarkca( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[4] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[3] = save_window_
{save_window_.view(0, 0, 500000, 1, 476, 653, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarh_gbarh( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[5] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[9] = save_window_
{save_window_.view(0, 0, 500000, 1, 1016, 733, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarna_gbarna( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
{
save_window_ = new Graph(0)
fig3bottom_g[6] = save_window_
save_window_.size(0,500000,0,1)
scene_vector_[8] = save_window_
{save_window_.view(0, 0, 500000, 1, 944, 653, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addvar("soma.gbarkd_gbarkd( 0.5 )", 1, 1,repositionlabel_x,repositionlabel_y, 2)
}
objectvar scene_vector_[1]
{doNotify()}

hbox.intercept(0)
hbox.map()

Loading data, please wait...