Local variable time step method (Lytton, Hines 2005)

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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];
NEURON { SUFFIX kdr }
NEURON { USEION k WRITE ik }
ASSIGNED { ik }
PARAMETER {
	erev 		= -90.  (mV)
	gmax 		= 0.009    (mho/cm2)
        vrest           = 0.

	exptemp		= 27
	maflag 		= 3
	malphaA 	= -0.01
	malphaB		= -10.0
	malphaV0	= -34.
	mbflag 		= 1
	mbetaA 		= 0.125
	mbetaB		= -80.
	mbetaV0		= -44.
	mq10		= 5
	mexp 		= 4

	haflag 		= 0
	halphaA 	= 0
	halphaB		= 0
	halphaV0	= 0
	hbflag 		= 0
	hbetaA 		= 0
	hbetaB		= 0
	hbetaV0		= 0
	hq10		= 5
	hexp 		= 0

	cao                (mM)
	cai                (mM)

	celsius			   (degC)
	dt 				   (ms)
	v 			       (mV)

	vmax 		= 100  (mV)
	vmin 		= -100 (mV)
} : end PARAMETER

INCLUDE "geneval_cvode.inc"

PROCEDURE iassign () { i = g*(v-erev) ik=i }

:* SYNAPSES

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