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How to add synapses to an Adex model
Posted: Fri Oct 25, 2019 12:55 am
by anandhupresannan
Hi,
I am working on an Adex model. I want to add inhibitory and excitatory synapses into my model. How can I add that ?. Adding any kind of equation to the mod file help to obtain these properties or is there any reference for the Adex model development like for the multicompartmental model we use Neuron Book, any alternative like that?. My model is developed with Neuron-Python.
Re: How to add synapses to an Adex model
Posted: Mon Oct 28, 2019 12:10 pm
by ted
You want help implementing synaptic inputs to a model cell but say nothing about how you implemented the model cell--yet that is the very information required to give you a useful answer. Did you implement the model cell as an ARTIFICIAL_CELL, a POINT_PROCESS, or as a section to which NMODL-implemented mechanisms are inserted?
Re: How to add synapses to an Adex model
Posted: Tue Nov 05, 2019 1:30 am
by anandhupresannan
The mod which I am using is a POINT_PROCESS
Code: Select all
NEURON {
POINT_PROCESS ADEXTWO
RANGE C,s,i,gl,el,delT,Vt,I,Ex,In,cou_gnmda,mod_cou_gnmda
RANGE Vr,vrest,a,b,tw,fflag,thresh,thresh1,Gmax,Onset
RANGE weighti,nspike,gmax,gnmda,Isyn,ISYN,Talp,talp,onset
RANGE gampa,gampa1,gampa2,gampa3
RANGE ggaba,ggaba1,ggaba2,ggaba3,i_membrane
}
INITIAL {
vv=-70
w=0
gampa=0
gampa1=0
gampa2=0
gampa3=0
:gnmda=0
net_send(0,1)
}
PARAMETER {
Ex=0
In=0
C=150:77
nspike=0
weighti=0
onset=20
Onset=24
gl=10
el=-70
delT=4
thresh=15
thresh1=11
Vt=-50
I=0
Vr=-64
vrest=-70
a=9:81
b=250:175
i_membrane = 0
tw=13:385
fflag=1
gmax=7.78
talp=6.6:5.169
Gmax=10
Talp=3.5
}
STATE { w vv }
ASSIGNED {
s
gampa
ggaba
ggaba1
ggaba2
ggaba3
cou_gnmda
mod_cou_gnmda
gampa1
gampa2
gampa3
gnmda
Isyn
ISYN
}
BREAKPOINT {
SOLVE states METHOD derivimplicit
}
DERIVATIVE states {
if (gnmda==1 && In==0) {
if (t>=onset) {
:net_send(0,4)
gampa=gmax*(t/talp)*exp(1-t/talp)
ggaba=0
}
}
if (gnmda==2 && In==0) {
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1
ggaba=0
}
}
if (gnmda==3 && In==0) {
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2
ggaba=0
}
}
if (gnmda==4 && In==0) {
if (t>=onset) {
gampa=0
gampa1=0
gampa2=0
gampa3=0
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa3=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2+gampa3
: ggaba=0
}
}
if (gnmda==1 && In==1) {
Talp=3
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
}
}
}
if (gnmda==1 && In==2) {
Talp=3
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1
}
}
}
if (gnmda==1 && In==3) {
Talp=3
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2
}
}
}
if (gnmda==1 && In==4) {
Talp=3
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba3=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2+ggaba3
}
}
}
if (gnmda==2 && In==1) {
Talp=3.2
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
}
}
}
if (gnmda==2 && In==2) {
Talp=3.2
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1
}
}
}
if (gnmda==2 && In==3) {
Talp=3.2
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2
}
}
}
if (gnmda==2 && In==4) {
Talp=3.2
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba3=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2+ggaba3
}
}
}
if (gnmda==3 && In==1) {
thresh=11.4
Talp=3.4
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
}
}
}
if (gnmda==3 && In==2) {
Talp=3.4
thresh=8.6
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1
}
}
}
if (gnmda==3 && In==3) {
Talp=3.4
thresh=6
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2
}
}
}
if (gnmda==3 && In==4) {
Talp=3.4
thresh=3.27
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba3=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2+ggaba3
}
}
}
if (gnmda==4 && In==1) {
C=1000
talp=2.66
Talp=3.5
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa3=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2+gampa3
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
}
}
}
if (gnmda==4 && In==2) {
C=1000
talp=2.61
Talp=3.5
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa3=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2+gampa3
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1
}
}
}
if (gnmda==4 && In==3) {
C=1000
talp=2.61
Talp=3.5
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa3=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2+gampa3
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2
}
}
}
if (gnmda==4 && In==4) {
C=1000
talp=2.61
Talp=3.5
if (t>=onset) {
gampa=gmax*(t/talp)*exp(1-t/talp)
gampa1=gmax*(t/talp)*exp(1-t/talp)
gampa2=gmax*(t/talp)*exp(1-t/talp)
gampa3=gmax*(t/talp)*exp(1-t/talp)
gampa=gampa+gampa1+gampa2+gampa3
if (t>=Onset) {
ggaba=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba1=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba2=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba3=Gmax*(t/Talp)*exp(1-t/Talp)
ggaba=ggaba+ggaba1+ggaba2+ggaba3
}
}
}
Isyn=gampa*(vv-0)
ISYN=ggaba*(vv+80)
:vv'=(gl*(el-vv)+gl*delT*exp((vv-Vt)/delT)-I-w)/C-(Isyn+ISYN):gampa*(vrest-0)
i_membrane=(gl*(el-vv)+gl*delT*exp((vv-Vt)/delT)-I-w)
vv' = i_membrane/C-(Isyn+ISYN):gampa*(vrest-0)
w'=(a*(vv-el)-w)/tw
}
NET_RECEIVE (u) {
INITIAL {
gnmda=0
}
if (flag == 1) {
WATCH (vv>thresh) 2
} else if (flag == 2) {
net_event(t)
vv=Vr
w=w+b
nspike=nspike+1
if (nspike > 0) {
WATCH (vv>thresh1) 3
}
printf("The number of spikes is %f\n",nspike)
} else if (flag == 3) {
net_event(t)
vv=Vr
w=w+b
}else { :synaptic activation
gnmda=gnmda+1
printf("The gnmda value is %f\n",gnmda)
COMMENT
s=0
if (gnmda <= 4) {
s=s+1
printf("the s value is %f\n",s)
:cou_gnmda=gnmda
}else if (gnmda > 4) {
:printf("i m here\n")
:mod_cou_gnmda=gnmda-cou_gnmda
:cou_gnmda=mod_cou_gnmda
:gnmda=cou_gnmda
}
printf("the value of s is %f\n",s)
:if (Ex==4) {
: gnmda=4
:}else if (Ex==3) {
: gnmda=3
:}else if (Ex==2) {
: gnmda=2
:}else if (Ex==1) {
: gnmda=1
:}
ENDCOMMENT
}
}
This is the file I am using. I need to both inhibitory and excitatory behaviour.
Re: How to add synapses to an Adex model
Posted: Tue Nov 05, 2019 10:10 am
by ted
It's an artificial spiking cell. It ignores the membrane potential and membrane currents of whatever section to which it is attached, and makes no contribution to that section's membrane currents. It also ignores the weight associated with whatever NetCons deliver events to it. Although it contains a lot of code (controlled by a lot of conditional statements that exist for purposes that are not entirely clear) that is used to represent the effects of AMPA, NMDA, and GABA, it offers no way to influence these effects from outside the mechanism.
I need to both inhibitory and excitatory behaviour.
If you want something other than what this code provides, it will need a huge rewrite. But before that can even be attemted, you'll have to come up with a mathematical statement of how you want excitatory and inhibitory inputs to affect the artificial spiking cell. And in the process of revising the code, care must be taken not to break anything that should be preserved. The end result will be a model cell class that has to be tested extensively to make sure that it does what you want it to do, and that no bugs have been introduced.
Suggest you contact whoever developed this in the first place and explain what you want. Maybe that person will just go ahead and do it, or tell you how to proceed.
Re: How to add synapses to an Adex model
Posted: Wed Nov 06, 2019 1:16 am
by anandhupresannan
Thank you