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
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
On 30.06.23 08:09, Jona Scholz wrote: 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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Hi Jona,
To follow up on Anno’s reply, what you describe is how the connectivity of the Blue Brain project model is constrained (see e.g. Markram et al., 2015; Nolte et al., 2020). It is computationally very expensive to construct the connectivity matrix in this way.
In NEST, the compartmental model architecture has no concept of space at the moment. Therefor, it is impossible to do this computation natively in NEST. Once you have the connectivity matrix though, it can be passed to NEST efficiently.
Hope this helps!
Best regards, Willem
References Markram et al. Reconstruction and Simulation of Neocortical Microcircuitry. Cell, 2015. Nolte et al. Impact of higher order network structure on emergent cortical activity. Network Neuroscience, 2020.
---------------------------------------------------------------------- Dr. Willem Wybo, Institute of Neuroscience and Medicine (INM-6) Tel. +49 2461/61-4748 Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6) Theoretical Neuroscience & JARA Institute Brain Structure-Function Relationships (INM-10) ---------------------------------------------------------------------- Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Frauke Melchior ----------------------------------------------------------------------
On 30. Jun 2023, at 16:07, Anno Kurth a.kurth@fz-juelich.de wrote:
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
On 30.06.23 08:09, Jona Scholz wrote:
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
NEST Users mailing list -- users@nest-simulator.org mailto:users@nest-simulator.org To unsubscribe send an email to users-leave@nest-simulator.org mailto:users-leave@nest-simulator.org
Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Dr. Ir. Pieter Jansens, Prof. Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior
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