Yes, this should be possible in NESTML. Define two input ports for b1 and b2 (see the
multisynapse example in the documentation for how to do this). The membrane potential
(say, *V_m*) can be used to affect the synaptic currents, e.g.:
I_b1 = f(V_m) * convolve(I_shape, spikes_b1)
I_b2 = g(V_m) * convolve(I_shape, spikes_b2)
for some functions *f* and *g*. Then simply enter the membrane potential dynamics as you
want it to be, e.g. for multiplicative interaction
V_m' = const * I_b1 * I_b2
You could of course also define different membrane voltage for each individual
I would suggest to look at the multisynapse example
) and go
from there. Please feel free to post updates and questions to this thread, or if you run
into any errors or bugs to post an issue on https://github.com/nest/nestml/
On Tue, Feb 18, 2020, at 22:20, Benedikt Rank wrote:
thank you for your support!
If I understand the connection topology correctly the distal
compartment is connected to the proximal compartment which in turn is
connected to the soma (d->p->s.)
I am looking for a model with two separate dendritic compartments that
are not interconnected yet both connect directly to the soma:
Modelling the exact spatial relations, dimensions of compartments is
not important to me.
Potentially NestML is an alternative here.
I have read the NestML language documentation but am not sure whether
there are limitations imposed on the model esp. the update block.
There is mainly three things I need:
1) topology (b1->s<-b2)
2)passing membrane voltages from branches to soma
3)multiplicative aggregation at soma
Soma is essentially only integrating membrane voltages from the
compartments in a multiplicative manner, ie. concurrent spike arrival
at both compartments evoke significantly higher depolarizations of the
membrane potential at soma.
The soma then fires when a certain threshold is reached.
Can this be done with NestML?
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