XPP model

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

# Modified Morris-Lecar model 
# modified from ml_salka.ode

dV/dt = (i_dc-gna*minf(V)*(V-Vna)-gk*y*(V-VK)-gl*(V-Vl)-gsub*z*(V-Vsub))/c
dy/dt = phi_y*(yinf(V)-y)/tauy(V)
dz/dt = phi_z*(zinf(V)-z)/tauz(V)
param c=2

# HERE IS EVERYTHING YOU NEED TO KNOW ABOUT THE STIMULuS

# DC OFFSET 
# this is controlled by i_dc
param i_dc=0

## noise not included here.  
## To add it, uncomment lines below by removing one "#" per line, and add "i_noise" to line 3 (dv/dt=...) above
## NOISE
## This is modeled as an Ornstein-Uhlenbeck process, gives new noise on each trial
## Here is the Wiener variable
#wiener nz
## With scale=0 you get no noise 
## effects of changing dt are automatically controlled for in XPP
## However, variance of i_noise also depends on tau_inoise (variance = sigma^2*tau/2) 
## Therefore, if you want to keep the same variance, you must manually change sigma_inoise if you change tau_inoise 
#di_noise/dt=-1/tau_inoise*(i_noise-i_avg)+sigma*nz
#param sigma=0, tau_inoise=5, i_avg=0

## frozen noise can be repeated on multiple trials by saving i_noise to a .tab file and playing it back
## see xpp documentation about tables

# HERE IS EVERYTHING YOU NEED TO KNOW ABOUT INTRINSIC CURRENTS
# Initial conditions
V(0)=-70
y(0)=0.000025
z(0)=0
# if you want to make sure initial conditions are at steady state
# run trial with no stim, then select "initial conditions/last" from main menu... this will start you at the conditions at the end of your previous trial

# FAST INWARD CURRENT (INa or activation variable)
# This is assumed to activate instantaneously with changes in voltage
# voltage-dependent activation curve is described by m
minf(V)=.5*(1+tanh((V-beta_m)/gamma_m))
# maximal conductance and reversal potential
param beta_m=-1.2, gamma_m=18
param gna=20, vna=50

# DELAYED RECTIFIER CURRENT (IKdr or recovery variable)
# this current activates more slowly than INa, but is still faster than Isub or Iadapt (not included here)
# In this code, activation of IKdr is controlled by y
yinf(V)=.5*(1+tanh((V-beta_y)/gamma_y))
tauy(V)=1/cosh((V-beta_y)/(2*gamma_y))
# in the 2D model, varying beta_w shifts the w activation curve (w=y here) and can convert the neuron between class 1, 2, and 3 
param beta_y=-10, gamma_y=10
# maximal conductance and reversal potential
param gk=20, vk=-100, phi_y=0.15

# LEAK CURRENT (Il)
# just a passive leak conductance
param gl=2, vl=-70

# SLOW SUBTHRESHOLD INWARD OR OUTWARD CURRENT (Isub)
zinf(V)=.5*(1+tanh((V-beta_z)/gamma_z))
tauz(V)=1/cosh((V-beta_z)/(2*gamma_z))
param beta_z=-21, gamma_z=15
# parameters below are for outward current
param gsub=7, Vsub=-100, phi_z=0.15
# for inward current, change to gsub=3, Vsub=50, phi_z=0.5
# these parameters for Isub correspond to those used in Figure 4 of the paper

# slow adaptation is not included in this 3D model.

# following parameters control duration of simulation and axes of default plot
@ total=100000,dt=.1,xlo=-100,xhi=60,ylo=-.125,yhi=.6,xp=v,yp=y
@ meth=euler
@ MAXSTOR=1000000

done
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Terms of use

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:

  1. Redistributions of this SBML file must retain the above copyright notice, this list of conditions and the following disclaimer.
  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 [9] 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
minf v beta_m gamma_m 0.5 1 v beta_m gamma_m
yinf v beta_y gamma_y 0.5 1 v beta_y gamma_y
tauy v beta_y gamma_y 1 v beta_y 2 gamma_y
zinf v beta_z gamma_z 0.5 1 v beta_z gamma_z
tauz v beta_z gamma_z 1 v beta_z 2 gamma_z

Parameters [22] name constant value unit derived unit sbo cvterm
c c = 2 2.0 None
i_dc i_dc = 0 0.0 None
v v = -70 -70.0 None
y y = 0.000025 2.5e-05 None
z z = 0 0.0 None
beta_m beta_m = -1.2 -1.2 None
gamma_m gamma_m = 18 18.0 None
gna gna = 20 20.0 None
vna vna = 50 50.0 None
beta_y beta_y = -10 -10.0 None
gamma_y gamma_y = 10 10.0 None
gk gk = 20 20.0 None
vk vk = -100 -100.0 None
phi_y phi_y = 0.15 0.15 None
gl gl = 2 2.0 None
vl vl = -70 -70.0 None
beta_z beta_z = -21 -21.0 None
gamma_z gamma_z = 15 15.0 None
gsub gsub = 7 7.0 None
vsub vsub = -100 -100.0 None
phi_z phi_z = 0.15 0.15 None
t model time 0.0 dimensionless None

Rules [4]   assignment name derived units sbo cvterm
d v/dt = i_dc gna minf v beta_m gamma_m v vna gk y v vk gl v vl gsub z v vsub c None
d y/dt = phi_y yinf v beta_y gamma_y y tauy v beta_y gamma_y None
d z/dt = phi_z zinf v beta_z gamma_z z tauz v beta_z gamma_z None
t = time None