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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.
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
On 25/08/2022, 05:54, "lvtx(a)mail2.sysu.edu.cn" <lvtx(a)mail2.sysu.edu.cn>
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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
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.
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