Frequently Asked Questions

Installation

  1. If I compile NEST with MPI support, I get errors about ``SEEK_SET``, ``SEEK_CUR`` and ``SEEK_END`` being defined This is a known issue in some MPI implementations. A solution is to add –with-debug=”-DMPICH_IGNORE_CXX_SEEK” to the configure command line. More details about this problem can be found here

  2. Configure warns that Makefile.in seems to ignore the –datarootdir setting and the installation fails because of permission errors This problem is due to a change in autoconf 2.60, where the prefix directory for the NEST documentation can end up being empty during the installation. This leads to wrong installation paths for some components of NEST. If you have the GNU autotools installed, you can run ./bootstrap.sh in the source directory followed by ./configure. If you don’t have the autotools, appending --datadir=PREFIX/share/nest with the same PREFIX as in the --prefix option should help.

  3. I get ‘Error: /ArgumentType in validate’ when compiling an extension This is a known bug that has been fixed. Ask your local NEST dealer for a new pre-release. You need at least nest-1.9-7320.

  4. I get ‘collect2: ld returned 1 exit status, ld: -rpath can only be used when targeting Mac OS X 10.5 or later Please try to set the environment variable MACOSX_DEPLOYMENT_TARGET to 10.5 (export MACOSX_DEPLOYMENT_TARGET=10.5)

  5. Ipython crashes with a strange error message as soon as I import ``nest`` If ipython crashes on import nest complaining about a Non-aligned pointer being freed, you probably compiled NEST with a different version of g++ than Python. Take a look at the information ipython prints when it starts up. That should tell you which compiler was used. Then re-build NEST with the same compiler version.

  6. I get a segmentation fault wher I use SciPy in the same script together with PyNEST. We recently observed that if PyNEST is used with some versions of SciPy, a segmentation fault is caused. A workaround for the problem is to import SciPy before PyNEST. See https://github.com/numpy/numpy/issues/2521 for the official bug report in NumPy.

Where does data get stored

By default, the data files produced by NEST are stored in the directory from where NEST is called. The location can be changed by changing the property data_path of the root node using nest.SetKernelStatus({"data_path": "/path/to/data"}). This property can also be set using the environment variable NEST_DATA_PATH. Please note that the directory /path/to/data has to exist. A common prefix for all data files can be set using the property data_prefix of the root node by calling nest.SetKernelStatus({"data_prefix": "prefix"}) or setting the environment variable NEST_DATA_PREFIX.

Neuron models

  1. I cannot see any of the conductance based models. Where are they? Some neuron model need the GNU Scientific Library (GSL) to work. The conductance based models are among those. If your NEST installation does not have these models, you probably have no GSL or GSL development packages installed. To solve this problem, install the GSL and its development headers. Then reconfigure and recompile NEST.

Connections

  1. How can I create connections to multicompartment neurons? You need to create a synapse type with the proper receptor_type as in this example, which connects all 100 neurons in n to the first neuron in n:

    syns = nest.GetDefaults('iaf_cond_alpha_mc')['receptor_types']
    nest.CopyModel('static_synapse', 'exc_dist_syn', {'receptor_type': syns['distal_exc']})
    n = nest.Create('iaf_cond_alpha_mc', 100)
    nest.Connect(n, n[:1], sync_spec={'model'='exc_dist_syn'})
    nest.Simulate(10)