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];
: $Id: fvpre.mod,v 1.1.1.1 2003/09/02 19:05:02 hines Exp $
COMMENT
synapse taken from Wang, X.-J. and Buzsaki G. (1996) Gamma oscillations by
synaptic inhibition in a hippocampal interneuronal network.  
J. Neurosci. 16, 6402-6413.
ENDCOMMENT
					       
NEURON {
  POINT_PROCESS fvpre
  RANGE gmax, g, i
  GLOBAL alpha,beta,thetasyn,e
  NONSPECIFIC_CURRENT i
  POINTER vpre
}

UNITS {
  (nA) = (nanoamp)
  (mV) = (millivolt)
  (uS) = (microsiemens)
}

PARAMETER {
  gmax=1e-4 (uS)
  alpha=12 (/ms)
  beta=0.1 (/ms)
  e=-75	   (mV)
  thetasyn=0 (mV) 
}

ASSIGNED { vpre (mV) v (mV) i (nA)  g (uS)}

STATE { s }

INITIAL {
  s =  alpha*F(vpre)/(alpha*F(vpre)+beta)
}

BREAKPOINT {
  SOLVE state METHOD cnexp
  g = gmax * s
  i = g*(v - e)
}

DERIVATIVE state {
  s' = alpha*F(vpre)*(1-s) - beta*s
}

FUNCTION F (v1 (mV)) {
  F = 1/(1 + exp(-(v1-thetasyn)/2))
}  

Loading data, please wait...