Local variable time step method (Lytton, Hines 2005)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:33975
The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt.
Reference:
1 . Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Methods;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu];
load_file("nrngui.hoc")
objectvar save_window_, rvp_
objectvar scene_vector_[3]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
save_window_ = new Graph(0)
save_window_.size(0,500,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 500, 120, 422, 29, 300.48, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
}
objectvar scene_vector_[1]
{doNotify()}