====================== PyNN 0.7 release notes ====================== 4th February 2011 This release sees a major extension of the API with the addition of the :class:`PopulationView` and :class:`Assembly` classes, which aim to make building large, structured networks much simpler and cleaner. A :class:`PopulationView` allows a sub-set of the neurons from a :class:`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 :class:`Population`, and any operations on either view or parent (setting parameter values, recording, etc.) will be reflected in the other. An :class:`Assembly` is a list of :class:`Population` and/or :class:`PopulationView` objects, enabling multiple cell types to be encapsulated in a single object. :class:`PopulationView` and :class:`Assembly` objects behave in most ways like :class:`Population`: you can record them, connect them using a :class:`Projection`, you can have views of views... The "low-level API" (rechristened "procedural API") has been reimplemented in in terms of :class:`Population` and :class:`Projection`. For example, :func:`create()` now returns a :class:`Population` object rather than a list of IDs, and :func:`connect()` returns a :class:`Projection` object. This change should be almost invisible to the user, since :class:`Population` now behaves very much like a list of IDs (can be sliced, joined, etc.). There has been a major change to cell addressing: :class:`Population`\s 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 :class:`Population`, pass a :class:`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 :func:`initialize()` function or the :meth:`Population.initialize()` method, rather than by having `v_init` and similar parameters for cell models. Other API changes ================= - simplification of the :meth:`record_X()` methods.With the addition of the :class:`PopulationView` class, the selection logic implemented by the `record_from` and `rng` arguments duplicated that in :meth:`Population.__getitem__()` and :meth:`Population.sample()`, and so these arguments have been removed, and the :meth:`record_X()` methods now record all neurons within a :class:`Population`, :class:`PopulationView` or :class:`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 :meth:`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: :class:`SmallWorldConnector` and :class:`CSAConnector` (CSA = Connection Set Algebra) - native neuron and synapse models are now supported using a :class:`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 :func:`native_cell_type()`, for NEURON the :class:`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. Details ======= * Where :meth:`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 :mod:`pyNN.random` before :mod:`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 :func:`setup()` option for :mod:`pyNN.nest`: `recording_precision`. By default, `recording_precision` is 3 for on-grid and 15 for off-grid. * Partially fixed the :mod:`pyNN.nest` implementation of :class:`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 :mod:`pyNN.neuron` implementation. I'm also not sure how well it will work with native neuron models. * reverted :mod:`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 :class:`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 :class:`IF_cond_exp_gsfa_grr` model. * Added a :meth:`sample()` method to :class:`Population`, which returns a :class:`PopulationView` of a random sample of the neurons in the parent population. * Added the :class:`EIF_cond_exp/alpha_isfa/ista` and :class:`HH_cond_exp` standard models in Brian. * Added a `gather` option to the :meth:`Population.get()` method. * :func:`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 :class:`core.LazyArray` class, as a basis for a possible rewrite of the `connectors` module. * The :mod:`random` module now uses :mod:`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 :mod:`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 :func:`init_logging`: ``logfile=None`` -> stderr, format includes size & rank, user can override log-level * NEST :file:`__init__.py` changed to query NEST for filling ``NEST_SYNAPSE_TYPES``. * Started to move synapse dynamics related stuff out of :class:`Projection` and into the synapse dynamics-related classes, where it belongs. * Added a new "spike_precision" option to :func:`nest.setup()` (see http://neuralensemble.org/trac/PyNN/wiki/SimulatorSpecificOptions) * 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 (https://nose.readthedocs.org/en/latest/), in order to facilitate continuous integration testing. * Changed the format of connection files, as written by :meth:`saveConnections()` and read by :class:`FromFileConnector`: files no longer contain the population label. Connections can now also be written to :class:`NumpyBinaryFile` or :class:`PickleFile` objects, instead of just text files. Same for :meth:`Population.save_positions()`. * Added CSAConnector, which wraps the Connection Set Algebra for use by PyNN. Requires the csa package: https://pypi.python.org/pypi/csa/ * 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 :class:`DistanceDependentProbabiblityConnector` in order to be able to constrain the total number of connections. Can be useful for normalizations. * Added a simple :class:`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 :func:`setup()` (patch from Johannes Partzsch). * Made :mod:`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 :class:`Space` class, and :class:`Error` classes out of :mod:`common` into their own modules. Release 0.7.1 ============= .. date? 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 =============