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.
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
Hi Hans,
[Sorry for signing messages without my name before] Thanks for your advices. I will check the implementation of both.
Best regards, Tianxiang Lyu
Tianxiang Lyu Department of Mechanic Sun Yet-Sen University No. 66, Gongchang Road, Guangming District, Shenzhen, 518107, P. R. China
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Hans Ekkehard Plesser -
lvtx＠mail2.sysu.edu.cn -
Tianxiang Lyu