Hello lvtx, [As a courtesy to all members of the NEST mailing list, kindly sign messages with your name.] It is not possible to give a proper answer to your question without more details. For a full analysis, one would need to compare your own code and your NEST implementation. The NEST examples contain several random balanced networks, especially Brunel networks with delta, exponential and alpha shaped post-synaptic currents. These models have been used for many years and can be considered correct references. I'd suggest that you check your implementation and results against these examples. 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(a)nmbu.no Home http://arken.nmbu.no/~plesser On 25/08/2022, 05:54, "lvtx(a)mail2.sysu.edu.cn" <lvtx(a)mail2.sysu.edu.cn> wrote: [Some people who received this message don't often get email from lvtx(a)mail2.sysu.edu.cn. Learn why this is important at https://aka.ms/LearnAboutSenderIdentification ] I wrote a simple python code(without nest) for a E-I random balanced network with poisson noise stimulus. Then I try to build it on nest. However, the response firing rates are changed.(not a computing error) Is there anything wrong? neurons -> spike synapse -> wait delay post_neurons -> add weight from connected synapses to corresponding input channel neurons -> update ODE each step I figured out that without connections but with poisson noise input, the firing rates of my python code is the same as the code on nest. (Seems that the ODE calculations are the same in my python code and nest) However, the firing rates show differ greatly when I add the connections on both. _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org