I was also wandering if there is such possibility.
My approach is the same as Andrew's but nevertheless it takes time to connect a
complex network having multiple layers and feedback/feedforward connectivity even though I
have prepared and saved all parameters in advance, esspecially if I want to have sparse
I was also wandering is it possible to have sparse connectivity matrix W to be used in the
following manner: connect(pop1, pop2, "all_to_all", W). Now I am generating full
matrix with numerous zero elements but if I want to have dynamic synapses, initial zero
weights might result in non-zero one after some time.
On Wednesday, November 27, 2019, 4:42:32 PM GMT+2, Simon Brodeur
Is it because the network takes a long time to build?Are you working with very large
networks that need to be spread on multiple machines?
I am personally building networks with complex topologies, where creating the synaptic
connections requires a lot of time since I do random sampling with constraint satisfaction
directly in Python.I also need to compute per-synapse approximations of axonal delays,
dendritic tree attenuations and much more. I do build the network once and have written
some Python code that allow to pickle the necessary information (e.g. parameters of the
neuron models, synaptic weights) to instantiate faster the network in NEST when I want to
perform simulations. But that is just a custom solution, not general to any network.
On Tue, 2019-11-26 at 12:39 +0100, alehr wrote:
Dear NEST developer,
I am wondering if my inquiry from November 11th has been looked at.
On Mon, Nov 11, 2019 at 1:52 PM alehr <alehr(a)mun.ca> wrote:
Dear NEST developer,
I would like to initialize a network with neurons and connections and then run many
simulations with it. It would be great if I could build the network one time and then
deepcopy (or something similar) for each simulation. Is something like this possible?
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Université de Sherbrooke
Département génie électrique et génie informatique
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