Thank you for your kind response. My primary concern revolves around weak scaling. I am specifically interested in understanding how the weak scaling results for a very large and sparse network (e.g. with a small `fixed_indegree`) would be affected by the emerging differences in sparsity patterns and overall dynamics due to an increasing number of virtual processes. My intuition suggests that these differences may "balance out," thereby making the scaling results reliable. However, I am curious to know if there exists any mathematical guarantee or theory that either supports or disproves this hypothesis. Any insights or references on this matter would be greatly appreciated. Best, Jesus Espinoza.

Hello Jesus, Several aspects come into play in when using simulations to explore the dynamics of network models. First, any given simulation will result in one specific realisation of the model dynamics. If you run a simulation twice under identical circumstances (same random seed, same number of parallel processes), you must get identical results, otherwise something is wrong with the simulator. If you change the random seed in your simulation, then you will get model dynamics that differ in detail in most cases (see below for exceptions). For networks with "boring" dynamics such as a Brunel network with asynchronous irregular activity, this variation will only be in details of spike trains and voltage traces. For models with more complex dynamics, e.g., switching between "up" and "down" states at larger, random, intervals, the differences can be larger. But if proper statistical measures are applied over long enough traces and sufficiently many realisation, consistent result shall be obtained. As Wouter already pointed out, changing the number of virtual processes in a NEST simulation implicitly changes the random number sequences provided to specific neurons (and used to randomise connections), so even if the rng_seed set in NEST is the same, varying the number of virtual processes will yield different realisations. Given that you use `spike_generator`s with fixed input spike trains and then make all-to-all connections throughout your network, there is no randomness left in your network (assuming you do not randomize connection weights or delays). Then, random numbers play no role and you get identical results for any number of VPs. Overall, changing the number of VPs should only influence the time required to simulate your model, not the dynamics the model exhibits. Best regards, Hans Ekkehard -- Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser From: espinozavalverd(a)uni-wuppertal.de &lt;espinozavalverd(a)uni-wuppertal.de&gt; Date: Wednesday, 26 July 2023 at 17:31 To: users(a)nest-simulator.org &lt;users(a)nest-simulator.org&gt; Subject: [NEST Users] Re: Results of simulation using `fixed_indegree` seem to depend on `total_num_virtual_procs` Sorry, I meant to say Strong scaling. _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org