Hi Adrien,

If you can describe your stimulation pattern by a mathematical function f(t), you can try to create a generator following the approach of the sinusoidal_gamma_generator. Take a look at the documentation for that model and set lambda(t) = f(t). Ideally, f(t) should be integrable in closed form to give Lambda(t). Then, you can numerically compute h(t), although I think you need to modify the expression given for h(t) in the documentation—the Gamma function is specific to the gamma generator. See also the reference given in the documentation.

Otherwise, if you need a large number of spike_generators with different pre-produced spike trains and are simulating on a large number of virtual processes, you should consider the new spike_train_injector model. It essentially does the same as a spike_generator, but is more efficient on large numbers of threads unless you have extremely high spike rates sent from the generator. It is not yet in NEST 3.4, but available in the master version on Github and will be released with NEST 3.5.

Best,

Hans Ekkehard

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Prof. Dr. Hans Ekkehard Plesser

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Norwegian University of Life Sciences

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From: adrien.dhollande@etu.u-paris.fr <adrien.dhollande@etu.u-paris.fr>
Date: Wednesday, 10 May 2023 at 14:48
To: users@nest-simulator.org <users@nest-simulator.org>
Subject: [NEST Users] Re: How to repeatedly stimulate random neurons in a population ?

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Dear Hans,

Thank you for your reply !

Unfortunately it is not what I am looking for
What I would like is the following:
- each second each neuron of a subpopulation has a given probability to receive a single spike (according to a specific stimulation pattern).

For the moment I achieve this by declaring a spike generator for each neuron, each spike generator is initialized by a pre-established random sequence of firing times.

This certainly work for a small number of neurons (e.g. a few tens), I worry if it is applicable for a big number of neurons. Is there an elegant way of doing this ?

Best regards,
Adrien
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