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Local variable time step method (Lytton, Hines 2005)
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: 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];
Model files   Download zip file   Auto-launch             Help downloading and running models
\
locstepperf
fig1
rings
README
cur.mod
fvpre.mod
GABAA.mod
kdr.mod
naf.mod
fitgabaa-5atol.hoc
init.hoc
mosinit.hoc
net1.hoc
cmpwb96.hoc
spkplot.hoc
vinit.hoc
fitgabaa-5atol.ses.fd1
ctrl1.ses
fitgabaa-5atol.ses
test1.ses
fitgabaa-5atol.ses.ft1
geneval_cvode.inc
netcon.inc
wb96fig3a-5atol.dat
                            
"Independent variable timestep integration of individual neurons for
network simulations" is a methods paper which demonstrates that
the local variable step method has better performance than the fixed
step method for networks in which only a few cells are active at a time.

The files reproduce fig 1 and 3 and provide a test framework for
observing performance for the worst case Wang and Buzsaki model
and the best case Ring model.

Michael.Hines@yale.edu should be contacted for comments/questions.



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