### Segfault when running long simulation with many neurons

Posted:

**Wed Jun 25, 2014 12:35 pm**I am using the Python interface to run simulations of about 200 (compartmental) neurons and a whole bunch of synapses. That works fine for about the first 3 seconds, but shortly afterwards I get a segmentation fault. The output does not help much:
Any ideas how to track down/fix/work around this problem?

Some other information which might be relevant:

Code: Select all

```
NEURON -- VERSION 7.3 ansi (1106:9f00197f3a2d) 2014-06-19
Duke, Yale, and the BlueBrain Project -- Copyright 1984-2014
See http://www.neuron.yale.edu/neuron/credits
loading membrane mechanisms from ../BahlEtAl2012/channels/x86_64/.libs/libnrnmech.so
Additional mechanisms from files
IKM.mod SlowCa.mod cad.mod h.mod kca.mod kfast.mod kslow.mod nap.mod nat.mod
1
exp(119900) out of range, returning exp(700)
exp(43101.4) out of range, returning exp(700)
exp(119900) out of range, returning exp(700)
exp(119900) out of range, returning exp(700)
No more errno warnings during this execution
exp(15859.5) out of range, returning exp(700)
exp(5697.09) out of range, returning exp(700)
exp(15859.5) out of range, returning exp(700)
exp(15859.5) out of range, returning exp(700)
No more errno warnings during this execution
exp(4436.34) out of range, returning exp(700)
exp(1590.26) out of range, returning exp(700)
exp(4436.34) out of range, returning exp(700)
exp(4436.34) out of range, returning exp(700)
No more errno warnings during this execution
exp(2802.81) out of range, returning exp(700)
exp(1002.98) out of range, returning exp(700)
exp(2802.81) out of range, returning exp(700)
exp(2802.81) out of range, returning exp(700)
No more errno warnings during this execution
exp(1766.39) out of range, returning exp(700)
exp(1766.39) out of range, returning exp(700)
exp(1766.39) out of range, returning exp(700)
exp(1766.39) out of range, returning exp(700)
No more errno warnings during this execution
exp(2157.57) out of range, returning exp(700)
exp(771.003) out of range, returning exp(700)
exp(2157.57) out of range, returning exp(700)
exp(2157.57) out of range, returning exp(700)
No more errno warnings during this execution
exp(4700.02) out of range, returning exp(700)
exp(1685.05) out of range, returning exp(700)
exp(4700.02) out of range, returning exp(700)
exp(4700.02) out of range, returning exp(700)
No more errno warnings during this execution
exp(15226.9) out of range, returning exp(700)
exp(5469.65) out of range, returning exp(700)
exp(15226.9) out of range, returning exp(700)
exp(15226.9) out of range, returning exp(700)
No more errno warnings during this execution
exp(71216.3) out of range, returning exp(700)
exp(25598.8) out of range, returning exp(700)
exp(71216.3) out of range, returning exp(700)
exp(71216.3) out of range, returning exp(700)
No more errno warnings during this execution
exp(403193) out of range, returning exp(700)
exp(144950) out of range, returning exp(700)
exp(403193) out of range, returning exp(700)
exp(403193) out of range, returning exp(700)
No more errno warnings during this execution
exp(2.59729e+06) out of range, returning exp(700)
exp(933766) out of range, returning exp(700)
exp(2.59729e+06) out of range, returning exp(700)
exp(2.59729e+06) out of range, returning exp(700)
No more errno warnings during this execution
exp(1.8068e+07) out of range, returning exp(700)
exp(6.49573e+06) out of range, returning exp(700)
exp(1.8068e+07) out of range, returning exp(700)
exp(1.8068e+07) out of range, returning exp(700)
No more errno warnings during this execution
exp(1.34135e+08) out of range, returning exp(700)
exp(4.82239e+07) out of range, returning exp(700)
exp(1.34135e+08) out of range, returning exp(700)
exp(1.34135e+08) out of range, returning exp(700)
No more errno warnings during this execution
[1] 5271 segmentation fault (core dumped) python sq_pure.py
```

Some other information which might be relevant:

- I am repeatedly call the run function and run the model for an additional 1ms with each call.
- To each neuron's soma an APCount object is attached, recording spikes into individual vectors.
- Before each call to run all vectors will be resized to a size of 0 (and their size will be retrieved afterwards).