Maximilian

If I may chime in: I would not go down the road of checking the state of the repository. As @AlexVanMeegen said, you will end up reimplementing something like sumatra...

I think that @albada is right in saying that for some populations, there were probably fewer than 2000 neurons entering the calculation, simply because these population are small in the...

Okay, sorry, I missed that point, I thought you had run your own simulations. Let me take a look at that tomorrow then, I'll get back to you.

I can reproduce your finding for `FEF, 6E`. I am getting the same value as you for the cross-correlation coefficient. I can also see a deviation for the LvR value...

Thanks for your tests. Of course, it is possible to produce the exact same rates with different 2nd order statistics, but to achieve that with two different runs of the...

I think you are probably right that this padding of silent neurons with 0.0 values to the results had not been used in the publication and the published data. The...

> I found that if in the code snippet above on the LvR calculation `check` is chosen to be `True` and `num` is chosen to be `len(np.unique(spikes[:, 0]))`, the results...

> @akorgor has kindly shared the full set of spike trains with me so I thought I'd share this (very bad) plot of the distribution of LvR metrics for 6E:...

Hi, yes that's of course an important concern. Thus, we have to re-initialize `NEST_SYNAPSE_TYPES` after each `nest.ResetKernel`. With "moving into the `State` class", do you mean setting `NEST_SYNAPSE_TYPES` in `State.clear()`,...

Yes, I looked a bit in the code, but I think the problem is in the construction of the test value for my observation variable (which becomes negative for very...