Memory issues arising in small neural networks

Wednesday, 25 March 2020

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

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