Thalamic quiescence of spike and wave seizures (Lytton et al 1997)

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Accession:9889
A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).
Reference:
1 . Lytton WW, Contreras D, Destexhe A, Steriade M (1997) Dynamic interactions determine partial thalamic quiescence in a computer network model of spike-and-wave seizures. J Neurophysiol 77:1679-96 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Thalamus;
Cell Type(s): Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell;
Channel(s): I T low threshold;
Gap Junctions:
Receptor(s): GabaA; Glutamate;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Temporal Pattern Generation; Oscillations; Calcium dynamics;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr];
Search NeuronDB for information about:  Thalamus geniculate nucleus (lateral) principal neuron; Thalamus reticular nucleus cell; GabaA; Glutamate; I T low threshold; Gaba; Glutamate;
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lytton97
README
AMPA.mod
calciumpump_destexhe.mod *
GABAB1.mod
GABALOW.mod
gen.mod
HH_traub.mod *
IAHP_destexhe.mod
ICAN_destexhe.mod
Ih_old.mod *
IT_wang.mod
IT2_huguenard.mod
nmda.mod
passiv.mod
presyn.mod *
pulse.mod *
rand.mod
boxes.hoc *
declist.hoc *
decvec.hoc *
default.hoc *
directory
fig7.gif
geom.hoc
grvec.hoc
init.hoc
jnphys77_1679.pdf
local.hoc *
mosinit.hoc
network.hoc
nrnoc.hoc *
params.hoc
presyn.inc *
queue.inc *
run.hoc
simctrl.hoc *
snshead.inc *
synq.inc *
xtmp
                            
TITLE passive membrane channel

UNITS {
	(mV) = (millivolt)
	(mA) = (milliamp)
}

INDEPENDENT { v FROM -100 TO 50 WITH 50	(mV) }

NEURON {
	SUFFIX Pass
	NONSPECIFIC_CURRENT i
	RANGE g, erev
}

PARAMETER {
	g = .001	(mho/cm2)
	erev = -70	(mV)
}

ASSIGNED { i	(mA/cm2)}

BREAKPOINT {
	i = g*(v - erev)
}

COMMENT
The passive channel is very simple but illustrates several features of
the interface to NEURON. As a SCoP or hoc model the NEURON block is
ignored.  About the only thing you can do with this as an isolated channel
in SCoP is plot the current vs the potential. Notice that models require
that all variables be declared, The calculation is done in the EQUATION
block (This name may eventually be changed to MODEL).  The intended
semantics of the equation block are that after the block is executed, ALL
variables are consistent with the value of the independent variable.
In this case, of course a trivial assignment statement suffices.
In SCoP, INDEPENDENT gives the name and range of the independent variable,
CONSTANT declares variables which generally do not change during
solution of the EQUATION block and ASSIGNED declares variables which
get values via assignment statements (as opposed to STATE variables whose
values can only be determined by solving differential or simultaneous
algebraic equations.)  The values of CONSTANTS are the default values
and can be changed in SCoP.

The NEURON block serves as the interface to NEURON. One has to imagine
many models linked to NEURON at the same time. Therefore in order to
avoid conflicts with names of variables in other mechanisms a SUFFIX
is applied to all the declared names that are accessible from NEURON.
Accessible CONSTANTS are of two types. Those appearing in the
PARAMETER list become range variables that can be used in any section
in which the mechanism is "insert"ed.  CONSTANT's that do not appear in
the PARAMETER list become global scalars which are the same for every
section.  ASSIGNED variables and STATE variables also become range variables
that depend on position in a section.
NONSPECIFIC_CURRENT specifies a list of currents not associated with
any particular ion but computed by this model
that affect the calculation of the membrane potential. I.e. a nonspecific
current adds its contribution to the total membrane current.

The following  neuron program is suitable for investigating the behavior
of the channel and determining its effect on the membrane.
create a
access a
nseg = 1
insert Passive
g_Passive=.001
erev_Passive=0
proc cur() {
	axis(0,1,1,0,.001,1) axis()
	plot(1)
	for (v=0; v < 1; v=v+.01) {
		fcurrent()
		plot(v, i_Passive)
	}
	plt(-1)
}	

proc run() {
	axis(0,3,3,0,1,1) axis()
	t = 0
	v=1
	plot(1)
	while (t < 3) {
		plot(t,v)
		fadvance()
	}
}
/* the cur() procedure uses the fcurrent() function of neuron to calculate
all the currents and conductances with all states (including v) held
constant.  In the run() procedure fadvance() integrates all equations
by one time step. In this case the Passive channel in combination with
the default capacitance of 1uF/cm2 give a membrane with a time constant of
1 ms. Thus the voltage decreases exponentially toward 0 from its initial
value of 1.

ENDCOMMENT

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