PyNN 0.7 release notes

4th February 2011

This release sees a major extension of the API with the addition of the PopulationView and Assembly classes, which aim to make building large, structured networks much simpler and cleaner. A PopulationView allows a sub-set of the neurons from a Population to be encapsulated in an object. We call it a “view”, rather than a “sub-population”, to emphasize the fact that the neurons are not copied: they are the same neurons as in the parent Population, and any operations on either view or parent (setting parameter values, recording, etc.) will be reflected in the other. An Assembly is a list of Population and/or PopulationView objects, enabling multiple cell types to be encapsulated in a single object. PopulationView and Assembly objects behave in most ways like Population: you can record them, connect them using a Projection, you can have views of views…

The “low-level API” (rechristened “procedural API”) has been reimplemented in in terms of Population and Projection. For example, create() now returns a Population object rather than a list of IDs, and connect() returns a Projection object. This change should be almost invisible to the user, since Population now behaves very much like a list of IDs (can be sliced, joined, etc.).

There has been a major change to cell addressing: Populations now always store cells in a one-dimensional array, which means cells no longer have an address but just an index. To specify the spatial structure of a Population, pass a Structure object to the constructor, e.g.:

p = Population((12,10), IF_cond_exp)

is now:

p = Population(120, IF_cond_exp, structure=Grid2D(1.2))

although the former syntax still works, for backwards compatibility. The reasons for doing this are:

  1. we can now have more interesting structures than just grids

  2. efficiency (less juggling addresses, flattening)

  3. simplicity (less juggling addresses, less code).

The API for setting initial values has changed: this is now done via the initialize() function or the Population.initialize() method, rather than by having v_init and similar parameters for cell models.

Other API changes

  • simplification of the record_X() methods.With the addition of the PopulationView class, the selection logic implemented by the record_from and rng arguments duplicated that in Population.__getitem__() and Population.sample(), and so these arguments have been removed, and the record_X() methods now record all neurons within a Population, PopulationView or Assembly. Examples of syntax changes:

    pop.record_v([pop[0], pop[17]]) --> pop[(0, 17)].record_v()
    pop.record(10, rng=rng) --> pop.sample(10, rng).record()
  • enhanced describe() methods: can now use Jinja2 or Cheetah templating engines to produce much nicer, better formatted network descriptions.

  • connections and neuron positions can now be saved to various binary formats as well as to text files.

  • added some new connectors: SmallWorldConnector and CSAConnector (CSA = Connection Set Algebra)

  • native neuron and synapse models are now supported using a NativeModelType subclass, rather than specified as strings. This simplifies the code internally and increases the range of PyNN functionality that can be used with native models (e.g. you can now record any variable from a native NEST or NEURON model). For NEST, there is a class factory native_cell_type(), for NEURON the NativeModelType subclasses have to be written by hand.

Backend changes

  • the NEST backend has been updated to work with NEST version 2.0.0.

  • the Brian backend has seen extensive work on performance and on bringing it to feature parity with the other backends.


  • Where Population.initial_values() contains arrays, these arrays now consistently contain only enough values for local cells. Before, there was some inconsistency about how this was handled. Still need more tests to be sure it’s really working as expected.

  • Allow override of default_maxstep for NEURON backend as setup paramter. This is for the case that the user wants to add network connections across nodes after simulation start time.

  • Discovered that when using NEST with mpi4py, you must import nest first and let it do the MPI initialization. The only time this seems to be a problem with PyNN is if a user imports pyNN.random before pyNN.nest. It would be nice to handle this more gracefully, but for now I’ve just added a test that NEST and mpi4py agree on the rank, and a hopefully useful error message.

  • Added a new setup() option for pyNN.nest: recording_precision. By default, recording_precision is 3 for on-grid and 15 for off-grid.

  • Partially fixed the pyNN.nest implementation of TsodyksMarkramMechanism (cf ticket:172). The ‘tsodyks_synapse’ model has a ‘tau_psc’ parameter, which should be set to the same value as the decay time constant of the post-synaptic current (which is a parameter of the neuron model). I consider this only a partial fix, because if ‘tau_syn_E’ or ‘tau_syn_I’ is changed after the creation of the Projection, ‘tau_psc’ will not be updated to match (unlike in the pyNN.neuron implementation. I’m also not sure how well it will work with native neuron models.

  • reverted pyNN.nest to reading/resetting the current time from the kernel rather than keeping track of it within PyNN. NEST warns that this is dangerous, but all the tests pass, so let’s wait and see.

  • In HH_cond_exp, conductances are now in µS, as for all other conductances in PyNN, instead of nS.

  • NEURON now supports Tsodyks-Markram synapses for current-based exponential synapses (before it was only for conductance-based).

  • NEURON backend now supports the IF_cond_exp_gsfa_grr model.

  • Added a sample() method to Population, which returns a PopulationView of a random sample of the neurons in the parent population.

  • Added the EIF_cond_exp/alpha_isfa/ista and HH_cond_exp standard models in Brian.

  • Added a gather option to the Population.get() method.

  • brian.setup() now accepts a number of additional arguments in extra_params, For example, extra_params={'useweave': True} will lead to inline C++ code generation

  • Wrote a first draft of a developers’ guide.

  • Considerably extended the core.LazyArray class, as a basis for a possible rewrite of the connectors module.

  • The random module now uses mpi4py to determine the MPI rank and num_processes, rather than receiving these as arguments to the RNG constructor (see ticket:164).

  • Many fixes and performance enhancements for the brian module, which now supports synaptic plasticity.

  • No more GSL warning every time! Just raise an Exception if we attempt to use GSLRNG and pygsl is not available.

  • Added some more flexibility to init_logging(): logfile=None -> stderr, format includes size & rank, user can override log-level

  • NEST changed to query NEST for filling NEST_SYNAPSE_TYPES.

  • Started to move synapse dynamics related stuff out of Projection and into the synapse dynamics-related classes, where it belongs.

  • Added a new “spike_precision” option to nest.setup() (see

  • Updated the NEST backend to work with version 2.0.0

  • Rewrote the test suite, making a much cleaner distinction between unit tests, which now make heavy use of mock objects to better-isolate components, and system tests. Test suite now runs with nose (, in order to facilitate continuous integration testing.

  • Changed the format of connection files, as written by saveConnections() and read by FromFileConnector: files no longer contain the population label. Connections can now also be written to NumpyBinaryFile or PickleFile objects, instead of just text files. Same for Population.save_positions().

  • Added CSAConnector, which wraps the Connection Set Algebra for use by PyNN. Requires the csa package:

  • Enhanced distance expressions by allowing expressions such as (d[0] < 0.1) & (d[1] < 0.2). Complex forms can therefore now be drawn, such as squares, ellipses, and so on.

  • Added an n_connections flag to the DistanceDependentProbabiblityConnector in order to be able to constrain the total number of connections. Can be useful for normalizations.

  • Added a simple SmallWorldConnector. Cells are connected within a certain degree d. Then, all the connections are rewired with a probability given by a rewiring parameter and new targets are uniformly selected among all the possible targets.

  • Added a method to save cell positions to file.

  • Added a progress bar to connectors. Now, a verbose flag allows to display or not a progress bar indicating the percentage of connections established.

  • New implementation of the connector classes, with much improved performance and scaling with MPI, and extension of distance-dependent weights and delays to all connectors. In addition, a safe flag has been added to all connectors: on by default, a user can turn it off to avoid tests on weights and delays.

  • Added the ability to set the atol and rtol parameters of NEURON’s cvode solver in the extra_params argument of setup() (patch from Johannes Partzsch).

  • Made pyNN.nest’s handling of the refractory period consistent with the other backends. Made the default refractory period 0.1 ms rather than 0.0 ms, since NEST appears not to handle zero refractory period.

  • Moved standard model (cells and synapses) machinery, the Space class, and Error classes out of common into their own modules.

Release 0.7.1

This bug-fix release added copyright statements to all files, together with some minor bug fixes.

Release 0.7.2

Release 0.7.3

Release 0.7.4

Release 0.7.5