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 connectivity.
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 <firstname.lastname@example.org> wrote:
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.
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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