XPP model

This model was converted from XPP ode format to SBML using sbmlutils-0.1.5a6.

# hh200x50-LEV01.ode
# Ref: Huang et al., 2009, J Physiol Pharmacol (in press)
# 200 e and 50 i HH equations
# random applied current, random conductances
# to get it started, just set the excitatory synapses
# to some random values between 0 and 1
# you will get persistent activity.
# here are the HH functions
# slowly inactivating K (Kv3.1-encoded) current was included

am(v)=0.1*(v+40)/(1-exp(-(v+40)/10))
bm(v)=4*exp(-(v+65)/18)
ah(v)=.07*exp(-(v+65)/20)
bh(v)=1/(1+exp(-(v+35)/10))
an(v)=0.01*(v+55)/(1-exp(-(v+55)/10))
bn(v)=0.25*exp(-(v+65)/80)
an3(v)=(1/Kn)*(-0.021)*(v+8.3+eta)/(exp(-(v+8.3+eta)/9.8)-1)
bn3(v)=(1/Kn)*0.0002*exp(-(V+23.6+eta)/20.7)

# Stimulus protocol
ff(t)=heav(t-t_on)*heav(t_off-t)
par t_on=20, t_off=200
par Kn=1
% gk3: 7-->4 or 1
par gk3=7
% eta: 0-->10 mV
par eta=0

# this is the current for each cell
ihh(v,m,h,n,n3,hk)=gna*h*(v-vna)*m^3+gk*(v-vk)*n^4+gk3*(v-vk)*n3^4*hk+gl*(v-vl)
# synaptic onset parameters
# s' = a(vpre)(1-s)-s/tau
ae(x)=ae0/(1+exp(-x/5))
ai(x)=ai0/(1+exp(-x/5))
par ae0=4, ai0=1

# dont recompute the random tables every time a parameter is changed
@ autoeval=0

# random synapses - 20 % connectivity
table wee % 40000 0 39999 ran(1)<.02
table wei % 10000 0 9999 ran(1)<.02
table wie % 10000 0 9999 ran(1)<.02
table wii % 2500 0 2499  ran(1)<.02
# multiply synapses by weights
special see=mmult(200,200,wee,se0)
special sei=mmult(200,50,wei,se0)
special sie=mmult(50,200,wie,si0)
special sii=mmult(50,50,wii,si0)
# random currents applied to each cell
table r_e % 200 0 199  ran(1)-.5
table r_i % 50 0 49 ran(1)-.5

# parameters
par taue=4, taui=10
par vna=50,  vk=-80,  vl=-49,  gna=40,  gk=5, gl=0.03
par ie0=6.5, ie1=0
par ii0=0, ii1=0
par gee=0.1, gie=0.1, gii=0.1, gei=0.1
par eex=0, ein=-75

# finally the ODEs
ve[0..199]'=ie0*ff(t)+ie1*r_e([j])-ihh(ve[j],me[j],he[j],ne[j],n3e[j],hke[j])-gee*see([j])*(ve[j]-eex)-gie*sie([j])*(ve[j]-ein)
vi[0..49]'=ii0+ii1*r_i([j])-ihh(vi[j],mi[j],hi[j],ni[j],n3i[j],hki[j])-gei*sei([j])*(vi[j]-eex)-gii*sii([j])*(vi[j]-ein)
# synapses...
se[0..199]'=-se[j]/taue+ae(ve[j])*(1-se[j])
si[0..49]'=-si[j]/taui+ai(vi[j])*(1-si[j])
# gating variables
me[0..199]'=am(ve[j])*(1-me[j])-bm(ve[j])*me[j]
he[0..199]'=ah(ve[j])*(1-he[j])-bh(ve[j])*he[j]
ne[0..199]'=an(ve[j])*(1-ne[j])-bn(ve[j])*ne[j]
hke[0..199]'=0.0005*(1-hke[j])-0.0014*n3e[j]^4*hke[j]
n3e[0..199]'=an3(ve[j])*(1-n3e[j])-bn3(ve[j])*n3e[j]
mi[0..49]'=am(vi[j])*(1-mi[j])-bm(vi[j])*mi[j]
hi[0..49]'=ah(vi[j])*(1-hi[j])-bh(vi[j])*hi[j]
ni[0..49]'=an(vi[j])*(1-ni[j])-bn(vi[j])*ni[j]
hki[0..49]'=0.0005*(1-hki[j])-0.0014*n3i[j]^4*hki[j]
n3i[0..49]'=an3(vi[j])*(1-n3i[j])-bn3(vi[j])*n3i[j]

# initial data
init ve[0..199]=-75
init vi[0..49]=-75
# some numerical settings
@ total=200, meth=euler, nout=10, dt=.01
@ xlo=0, xhi=200, yhi=40, ylo=-90
done
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Copyright © 2017 Matthias Koenig

Redistribution and use of any part of this model, with or without modification, are permitted provided that the following conditions are met:

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This model is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.


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Access SBML model  L3V1

FunctionDefinitions [16] name math sbo cvterm
max minimum x y x x y y
min maximum x y x x y y
heav heavyside x 0 x 0 0.5 x 0 1 x 0 0
mod modulo x y x y x y x 0 y 0 x y x y
am v 0.1 v 40 1 v 40 10
bm v 4 v 65 18
ah v 0.07 v 65 20
bh v 1 1 v 35 10
an v 0.01 v 55 1 v 55 10
bn v 0.25 v 65 80
an3 v eta kn 1 kn 0.021 v 8.3 eta v 8.3 eta 9.8 1
bn3 v eta kn 1 kn 0.0002 v 23.6 eta 20.7
ff t t_off t_on heav t t_on heav t_off t
ihh v m h n n3 hk gk gk3 gl gna vk vl vna gna h v vna m 3 gk v vk n 4 gk3 v vk n3 4 hk gl v vl
ae x ae0 ae0 1 x 5
ai x ai0 ai0 1 x 5

Parameters [42] name constant value unit derived unit sbo cvterm
t_on t_on = 20 20.0 None
t_off t_off = 200 200.0 None
kn kn = 1 1.0 None
gk3 gk3 = 7 7.0 None
eta eta = 0 0.0 None
ae0 ae0 = 4 4.0 None
ai0 ai0 = 1 1.0 None
taue taue = 4 4.0 None
taui taui = 10 10.0 None
vna vna = 50 50.0 None
vk vk = -80 -80.0 None
vl vl = -49 -49.0 None
gna gna = 40 40.0 None
gk gk = 5 5.0 None
gl gl = 0.03 0.03 None
ie0 ie0 = 6.5 6.5 None
ie1 ie1 = 0 0.0 None
ii0 ii0 = 0 0.0 None
ii1 ii1 = 0 0.0 None
gee gee = 0.1 0.1 None
gie gie = 0.1 0.1 None
gii gii = 0.1 0.1 None
gei gei = 0.1 0.1 None
eex eex = 0 0.0 None
ein ein = -75 -75.0 None
ve[0..199] = -75 -75.0 None
vi[0..49] = -75 -75.0 None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
0.0 dimensionless None
t model time 0.0 dimensionless None

Rules [15]   assignment name derived units sbo cvterm
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
= None None
t = time None