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]
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_[5]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
xpanel("RunControl", 0)
v_init = -65
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 500
xvalue("t","t", 2 )
tstop = 500
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.025
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
xcheckbox("Quiet",&stdrun_quiet,"")
realtime = 33
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(13,113)
}

//Begin VariableTimeStep
{
ocbox_ = NumericalMethodPanel[0]
ocbox_ = ocbox_.b1
ocbox_.map("VariableTimeStep", 14, 511, 272.64, 106.56)
}
objref ocbox_
//End VariableTimeStep

{
save_window_ = new Graph(0)
save_window_.size(0,500,-80,40)
scene_vector_[2] = save_window_
{save_window_.view(0, -80, 500, 120, 802, 674, 300.48, 200.32)}
graphList[0].append(save_window_)
save_window_.save_name("graphList[0].")
save_window_.addvar("C_Cell[0].soma.v( 0.5 )", 1, 1, 0.515655, 1.03898, 2)
save_window_.addvar("C_Cell[ncell-1].soma.v( 0.5 )", 2, 1, 0.50607, 1.04377, 2)
}
{
xpanel("Temperature", 0)
celsius = 37
xvalue("celsius","celsius", 1,"", 0, 1 )
xpanel(15,652)
}
{
xpanel("numerics", 0)
ncell = 100
xvalue("ncell", "ncell", 1, "defnet()")
xcheckbox("second order threshold (variable step methods only)",&order_,"cvode.condition_order(order_+1)")
xradiobutton("fixed step","cvode_active(0)", 1)
xradiobutton("global variable step","cvode_active(1)")
xradiobutton("local variable step","cvode_local(1)")
xcheckbox("NetCons active",&ncact,"ncactive()")
mcur = 0.001
xvalue("mean current (sets natural frquency)","mcur", 1,"setfreq()", 0, 0 )
varcur = 0.0002
xvalue("current variation (modifies natural frequency)","varcur", 1,"setfreq()", 0, 0 )
delaymin = 3.5
xvalue("minimum delay","delaymin", 1,"setdel()", 0, 0 )
delaydel = 0
xvalue("max-min delay","delaydel", 1,"setdel()", 0, 0 )
vinitmin = -68
xvalue("minimum vinit","vinitmin", 1,"", 0, 0 )
vinitmax = -55
xvalue("maximum vinit","vinitmax", 1,"", 0, 0 )
wval = 1e-4
xvalue("n*weight","wval", 1,"setweight()", 0, 0 )
xbutton("Re-randomize frequency","ranfreq()")
xbutton("Re-randomize vinit","ranvinit()")
xbutton("Re-randomize delay","randelay()")
xpanel(309,110)
}
{
save_window_ = new Graph(0)
save_window_.size(0,500,0,1e-04)
scene_vector_[4] = save_window_
{save_window_.view(0, 0, 500, 1e-04, 805, 409, 300.48, 200.32)}
graphList[2].append(save_window_)
save_window_.save_name("graphList[2].")
save_window_.addexpr("C_Cell[0].synlist.object(0).g", 1, 1, 0.359105, 1.00543, 2)
save_window_.addexpr("C_Cell[ncell-1].synlist.object(0).g", 2, 1, 0.269649, 1.01022, 2)
}
objectvar scene_vector_[1]
{doNotify()}

Lytton WW, Hines ML (2005) Independent variable time-step integration of individual neurons for network simulations. Neural Comput 17:903-21[PubMed]

References and models cited by this paper

References and models that cite this paper

Abeles M (1991) Corticonics: Neural Circuits of the Cerebral Cortex.

Aviel Y, Mehring C, Abeles M, Horn D (2003) On embedding synfire chains in a balanced network. Neural Comput 15:1321-40 [PubMed]

Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Computational models of thalamocortical augmenting responses. J Neurosci 18:6444-65 [Journal] [PubMed]

   Thalamocortical augmenting response (Bazhenov et al 1998) [Model]

Cohen S, Hindmarsh A (1994) Cvode user guide Tech Rep

Destexhe A, Mainen Z, Sejnowski TJ (1994) An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Comput 6:14-18 [Journal]

   Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994) [Model]
   Kinetic synaptic models applicable to building networks (Destexhe et al 1998) [Model]
   Application of a common kinetic formalism for synaptic models (Destexhe et al 1994) [Model]

Destexhe A, Mainen ZF, Sejnowski TJ (1994) Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. J Comput Neurosci 1:195-230 [Journal] [PubMed]

   Application of a common kinetic formalism for synaptic models (Destexhe et al 1994) [Model]
   Kinetic synaptic models applicable to building networks (Destexhe et al 1998) [Model]

Hindmarsh A, Serban R (2002) User documentation for Cvodes, an ode solver with sensitivity analysis capabilities Tech Rep

Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123-35 [Journal] [PubMed]

   Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001) [Model]

Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM (2004) ModelDB: A Database to Support Computational Neuroscience. J Comput Neurosci 17:7-11 [Journal] [PubMed]

Jones D (1986) An empirical comparison of priority-queue and event-setimplementations Comm Acm 4:300-311

Lytton WW (1996) Optimizing synaptic conductance calculation for network simulations. Neural Comput 8:501-9 [PubMed]

Makino T (2003) A discrete-event neural network simulator for general neuron models Neural Comput App 11:210-223

Mattia M, Del Giudice P (2000) Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Comput 12:2305-29 [PubMed]

Sleator D, Tarjan R (1983) Self adjusting binary trees Proc ACM SIGACT Symposium on Theory of Computing :235-245

Traub RD, Jefferys GR, Whittington MA (1999) Fast Oscillations In Cortical Circuits

Victor JD, Purpura KP (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76:1310-26 [Journal] [PubMed]

Wang XJ, Buzsaki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 16:6402-13 [Journal] [PubMed]

   Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996) [Model]

Watts L (1994) Event-driven simulation of networks of spiking neurons Advances in Neural Information Processing Systems, Cowan J:Tesauro G:Alspector J, ed. pp.927

Brette R (2006) Exact simulation of integrate-and-fire models with synaptic conductances. Neural Comput 18:2004-27 [PubMed]

Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. J Comp Neurosci 23:349-98 [Journal] [PubMed]

   Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) [Model]

Hines ML, Markram H, Schuermann F (2008) Fully Implicit Parallel Simulation of Single Neurons J Comp Neurosci 25:439-448 [Journal] [PubMed]

   Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008) [Model]

Huang S, Hong S, De Schutter E (2015) Non-linear leak currents affect mammalian neuron physiology. Front Cell Neurosci 9:432 [Journal] [PubMed]

   Concentration dependent nonlinear K+ and Cl- leak current (Huang et al. 2015) [Model]

Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schurmann F, Hines ML (2016) Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput :1-28 [Journal] [PubMed]

   Parallelizing large networks in NEURON (Lytton et al. 2016) [Model]

McDougal RA, Hines ML, Lytton WW (2013) Reaction-diffusion in the NEURON simulator. Front Neuroinform 7:28 [Journal] [PubMed]

   Reaction-diffusion in the NEURON simulator (McDougal et al 2013) [Model]

Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [Journal] [PubMed]

   Parallel network simulations with NEURON (Migliore et al 2006) [Model]

Morrison A, Straube S, Plesser HE, Diesmann M (2007) Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Comput 19:47-79 [PubMed]

Rangan AV, Cai D (2007) Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks. J Comput Neurosci 22:81-100 [Journal] [PubMed]

Ros E, Carrillo R, Ortigosa EM, Barbour B, Agis R (2006) Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Comput 18:2959-93 [PubMed]

Rudolph M, Destexhe A (2006) Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies. Neural Comput 18:2146-210 [PubMed]

(29 refs)