Hello Jona,

I now understand better what you want to do.

To recap (and please correct me if I am wrong):
You have  neurons in space. In order to determine whether two neurons are connect you associate to each neurons dendrites as points in space.
These dendrites are fixed for each neuron.
If a dendrite of a source neuron is sufficiently close to a potential target neuron, you connect them, possible with a probability depending on the distance between the dendrite and the target neuron.

Did I get it right?

If so, then no, there is currently no way of doing this in NEST.
It goes beyond of what - to my best knowledge - is currently possible with the topology feature in NEST.

As you already mentioned, you can generate the connectivity in python and then connect your neurons accordingly.

This should, however, not be very efficient.
It seems to me that your connection principle could be abstracted to a general connection rule that could be efficiently implemented on the C++ level.

What do other people think about this?

Best

Anno

Hello Anno,

thank you for your reply. Not sure what using compartments entails, but I'm guessing I only need distance dependent connection probabilities. The probability should be 1 if a dendrite is close enough to a given neuron and 0 otherwise.

Another point that may be relevant: it should be possible to have multiple connections between a pair of neurons. Each connection should be able to learn with a variation of STDP. Is this supported by default?

Kind regards,

Jona

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