* Fixed bug when loading a network saved before 4.5.3.
* Added the ``every`` decorator allowing functions to be called periodically during a simulation::
result = []
every(period=1000.)
def set inputs(n):
Set inputs to the network
pop.I = Uniform(0.0, 1.0)
Save the output of the previous step
result.append(pop.r)
simulate(100 * 1000.)
* Fixed installation with non-standard Python distribution (e.g. Anaconda).
* Added a ``HomogeneousCorrelatedSpikeTrains`` class allowing to generate homogeneous correlated spike trains::
pop = HomogeneousCorrelatedSpikeTrains(geometry=200, rates=10., corr=0.3, tau=10.)
* Installing through pip does not forget CUDA files anymore.
* Added ``Population.clear()`` to clear all spiking events (also delayed) without resetting the network.
* ``Population.reset()`` and ``Projection.reset()`` now accept a list of attributes to be reset, instead of resetting all of them.
* Unit tests are now performed on Travis CI to get a badge.
* Bug fixed: min/max bounds on g_target was wrongly analyzed when depending on a parameter.
* ``parallel_run()`` now accepts additional arbitrary arguments that can be passed to the simulation callback.
* Added an ``ite(cond, statement1, statement2)`` conditional function replicating ``if cond: statement1 else: statement2``, but which can be combined::
r = 1.0 + ite(sum(exc) > 1.0, sum(exc), 0.0) + ite(sum(inh) > 1.0, -sum(inh), 0.0)
* The ``Network`` class has several bugs fixed (e.g. disabled populations stay disabled when put in a network).
* Populations have now an "enabled" attribute to read their status.