[NEST Users] Memory issues arising in small neural networks

Hello all, Hope everyone is staying safe and well in these difficult times. I am an undergrad who is just getting started with this library, so I want to apologize in advance for the basic question. I am having issues I believe are memory-related when I call GetConnections on rather small networks. Here is my script: import nest # memory testing nest.ResetKernel() n = 5 # number of neurons epop1 = nest.Create("iaf_psc_alpha",n) epop2 = nest.Create("iaf_psc_alpha",n) nest.Connect(epop1,epop2,{'rule': 'all_to_all'}) # we can now query the synapses using GetConnections conns = nest.GetConnections(epop1,target = epop2) nest.GetStatus(conns,["target","weight"]) This runs fine for n < 5, but I get the following error with n >=5: Assertion failed: (source_lid < MAX_LID), function set_source_lid, file /usr/local/miniconda/conda-bld/nest-simulator_1583214474797/work/nestkernel/target_data.h, line 264. Abort trap: 6 I have run some of the example scripts like brunel_alpha_numpy with a few thousand neurons and they work just fine. Am I making an improper call to GetConnections here? And if so, what is a better way to dynamically check connectivity? Alternatively, might there be something wrong with my Nest environment? And if so, what should I look at to troubleshoot this? Thank you so much for your help. Best wishes, Josh