XPP model

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

# This is an XPP realization of the noisy ring model that appears in 
# Goldberg JA, Rokni U and Sompolinsky H. Patterns of Ongoing Activity and the
# Functional Architecture of the Primary Visual Cortex. Neuron, 42:489-500 (2004).

# must have the file CosIntCol20.tab in the same directory as NoisyRing.ode

# In order to see the population activity do
# Viewaxes, Array and then fill out 
# *Column 1:m11
# NCols:20
# Row 1:0
# NRows:4000
# RowSkip:5
# Zmin:0
# and adjust Zmax according to your simulation

# The phase diagram of this model is shown in fig. 3A of the above paper. In order to 
# get a noisy "hill" of activity lambda must be larger than 1 and mu must be positive.


# parameters (gain and mean of Gaussian noise)
p lambda=1.8, mu=1
# mu here is equivalent to T/Sigma_n in the figure in the paper.
# note lambda=1.1, mu=5 are other possible defaults

# threshold linear gain function
sl(x)=max(x,0)

# stochastic integration, w is local uncorrelated Gaussian white noise
weiner w[11..30]

#the model
m[11..30]'=-m[j]+sl(2*lambda*h([j-11])+w[j]+mu)

#Coupling Matrix
table coscol CosIntCol20.tab
# this table is cosine coupling divided by 20
# recurrent feedback is given by h
special h=mmult(20,20,coscol,m11);

# order parameters of model
rnull=sum(0,19) of (shift(m11,i'))/20
rfundc=sum(0,19) of (shift(m11,i')*cos(2*pi*i'/20))/20
rfunds=sum(0,19) of (shift(m11,i')*sin(2*pi*i'/20))/20
aux r0=rnull
aux r1=sqrt(rfundc^2+rfunds^2)
aux phiang=atan(rfunds/rfundc)

# r1 is order 1 for a "hill of activity" and is order 1/sqrt(20) otherwise

@ total=1000,xlo=0,xhi=1000,ylo=0,yhi=10,dt=0.05,bounds=10000,meth=euler


d
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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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  2. Redistributions in a different form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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 [5] 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
sl x max x 0

Parameters [11] name constant value unit derived unit sbo cvterm
lambda lambda = 1.8 1.8 None
mu mu = 1 1.0 None
0.0 None
0.0 dimensionless None
rnull 0.0 dimensionless None
rfundc 0.0 dimensionless None
rfunds 0.0 dimensionless None
r0 0.0 dimensionless None
r1 0.0 dimensionless None
phiang 0.0 dimensionless None
t model time 0.0 dimensionless None

Rules [8]   assignment name derived units sbo cvterm
= None None
rnull = None None
rfundc = None None
rfunds = None None
r0 = rnull None
r1 = 2 rfundc 2 rfunds 2 None
phiang = rfunds rfundc None
t = time None