Hippocampus temporo-septal engram shift model (Lytton 1999)

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Accession:7400
Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.
Reference:
1 . Lytton WW, Lipton P (1999) Can the hippocampus tell time? The temporo-septal engram shift model. Neuroreport 10:2301-6 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Temporal Pattern Generation; Spatio-temporal Activity Patterns; Simplified Models;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu];
Search NeuronDB for information about:  GabaA; AMPA; I Na,t; I K;
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lytton99
README
AMPA.mod
GABAA.mod
kdr.mod
matrix.mod *
naf.mod *
passiv.mod *
pulse.mod *
sinstim.mod *
vecst.mod
vecst.mod.orig
bg.inc *
bg_cvode.inc
boxes.hoc *
declist.hoc *
decvec.hoc *
default.hoc *
directory
fig1.gif
grvec.hoc
init.hoc
ivl.vecs
labels.hoc
loadr.hoc *
local.hoc
mosinit.hoc
net.hoc
netcon.inc
nrnoc.hoc
ovl.vecs
params.hoc *
params.hoc.SAV *
proc.hoc
run.hoc
simctrl.hoc *
spkts.hoc
syncode.hoc
tmpl.hoc
                            
: $Id$ 
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)
        VERBATIM
        in_passiv_breakpoint();
        ENDVERBATIM
}

VERBATIM
void in_passiv_breakpoint() {}
ENDVERBATIM



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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