=============================
PyNN 0.8 beta 2 release notes
=============================

January 6th 2015

Welcome to the second beta release of PyNN 0.8!

For full information about what's new in PyNN 0.8, see the :doc:`0.8-alpha-1` and :doc:`0.8-beta-1`.

NEST 2.4
--------

The main new feature in this release is support for NEST_ 2.4. Previous versions of NEST are no longer supported.

Python 3
--------

With the rewrite of PyNEST in NEST 2.4, PyNN now has two backend simulators (NEURON_ being the other) that support
Python 3. There was really no longer any excuse not to add Python 3 support to PyNN, which turned out to be very
straightforward.

Standardization of random distributions
---------------------------------------

Since its earliest versions PyNN has supported swapping in and out different random number generators, but until
now there has been no standardization of these RNGs; for example the GSL_ random number library uses "gaussian" where
NumPy_ uses "normal". This limited the usefulness of this feature, especially for the :class:`NativeRNG` class, which
signals that random number generation should be passed down to the simulator backend rather than being done at the
Python level.

This has now, finally, been fixed. The names of random number distributions and of their parameters have now been
standardized, based for the most part on the nomenclature used by Wikipedia_. A quick example:

.. code-block:: python

   from pyNN.random import NumpyRNG, GSLRNG, RandomDistribution

   rd1 = RandomDistribution('normal' mu=0.5, sigma=0.1, rng=NumpyRNG(seed=922843))
   rd2 = RandomDistribution('normal' mu=0.5, sigma=0.1, rng=GSLRNG(seed=426482))

API changes
-----------

  * :meth:`Population.record()` now has an optional `sampling_interval` argument, allowing recording at intervals larger
    than the integration time step.
  * :class:`FixedNumberPostConnector` now has an option `with_replacement`, which controls how the post-synaptic
    population is sampled, and affects the incidence of multiple connections between pairs of neurons ("multapses").
  * The default value of the `min_delay` argument to :func:`setup()` is now "auto", which means that the simulator
    should calculate the minimal synaptic delay itself. This change can lead to large speedups for NEST and NEURON code.

Other changes
-------------

  * Reimplemented Izhikevich model for NEURON to allow current injection.
  * :class:`Connector`\s that can generate multiple connections between a given pair of neurons ("multapses") now work
    properly with the pyNN.nest backend.
  * Added a version of :class:`CSAConnector` for the NEST backend that passes down the CSA object to PyNEST's
    :func:`CGConnect()` function. This greatly speeds up :class:`CSAConnector` with NEST.
  * Added some new example scripts, deleted some of the more trivial, repetitive examples, and merged the several
    variants of the "VAbenchmarks" example into a single script.
  * When data blocks from different MPI nodes are merged, the spike trains are now by default sorted by neuron ID.
    If this sorting proves to be too time-consuming we can in future expose sort/don't sort as an option.
  * Added :class:`IF_cond_exp_gsfa_grr` standard model (integrate and fire neuron with spike frequency adaption and
    relative refractory period) to Brian_ backend, and fixed broken :class:`HH_cond_exp` model.
  * Improvements to callback handling.
  * `Assorted bug fixes`_


.. _Wikipedia: http://en.wikipedia.org/wiki/List_of_probability_distributions
.. _GSL: https://www.gnu.org/software/gsl/manual/html_node/Random-Number-Generation.html
.. _NumPy: http://docs.scipy.org/doc/numpy/reference/routines.random.html
.. _Brian: http://http://briansimulator.org
.. _NEST: http://www.nest-initiative.org/
.. _NEURON: http://www.neuron.yale.edu/neuron/
.. _`Assorted bug fixes`: https://github.com/NeuralEnsemble/PyNN/issues?q=is%3Aclosed+closed%3A2013-11-15..2015-01-06+milestone%3A0.8.0