Hi Tom,

As a DIY workaround, you can use the RunManager context to simulate in small steps and break if it gets too slow. I haven't tested the code, just sketching from memory. Instead of calling nest.Simulate(1000), use

with nest.RunManager():

    for _ in range(100):

        t = time.time()

        nest.Run(10)

        if time.time() - t > 5:

            break

The logic is as follows: You split the 1000 ms into 100 times 10 ms. This is fast with Run() within the RunManager(). You then use Python's time to see how long it takes to simulate 10 ms and break if it takes too long, here a 5 s limit. You can then use GetKernelStatus to get the current time in the simulation.

It would be interesting to add this as a kernel feature. Let me know if it works!

Best,

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

Home http://arken.nmbu.no/~plesser

On 06/05/2021, 16:08, "TOM BUGNON" <bugnon@wisc.edu> wrote:

Hi all,

Under some circumstances simulations can slow down up to the point where the nest.Simulate() does not advance anymore and stays stuck at a given virtual time, with a "realtime factor" of 0. I suppose this can happen for instance when a network falls into a regime of runaway excitation in which a massive number of spikes are being exchanged.

I'm looking for a way to stop the simulation in such a case, (say when the realtime factor goes below a set threshold, or when the output files are not updated for a certain duration), ideally in such a way that the program can continue running rather than crashing. If anyone has a suggestion about how to go around this issue I'd be happy to hear it.
Thanks in advance! Best, Tom