Annarchy

* Monitoring projections failed when the post-synaptic projection was a PopulationView not starting at 0.
* Bugfixes on CUDA:
* for higher order numerical methods (midpoint) the RNGs need to be drawn outside the update method.
* if a projection between spiking neurons was targeting two channels (e. g. ampa and nmda), a wrong code was generated.

* Moved the documentation to mkdocs: https://github.com/ANNarchy/ANNarchy.github.io
* Added a ``TimedPoissonPopulation`` allowing to schedule the rate.
* ``HomogeneousCorrelatedSpikeTrains`` can also take a schedule for rates and corr.
* Python 2 has been depreciated.
* Fixed bugs related to cython, colab, sympy 1.6.1.

* The ``tensorboard`` extension allows to log various infos and visualize them using tensorboard.
* The API of the ``convolution`` extension has slightly changed, check the documentation.
* Changed default behavior from the built-in Izhikevich neuron model from fast spiking to regular spiking.
* ``PopulationView.rank_from_coordinates`` and ``PopulationView.coordinates_from_rank`` now accept coordinates relative to the geometry of the popview using the ``local`` flag.
* Fixed installation on Anaconda distributions. Now works in colab with ``!pip install ANNarchy``.
* Several bug fixes (variable-dependent refractory periods, conditionals of the type "if (t==1)", SpikeSourceArray, etc).
* Improved performance with CUDA.

* MacOS compatibility.
* Bugfix on ``Projection.load()``.

* ``SpikeSourceArray`` can now receive connections (which will be ignored, but may be used to investigate plasticity mechanisms).
* Added a ``clear_all_callbacks()`` method when using every.
* Added a `CurrentInjection` population allowing to connect a rate-coded population to the conductance of a spiking population.
* Several bug fixes and improvements.

* Feature: delays can now be changed after ``compile()``, even if the maximum delay is higher than before.
* Improved performance for spiking networks with variable delays through the use of ring buffers.
* Improved control of single/double precision.
* Improved performance on GPUs.
* Fixed bug when SpikeSourceArray, TimedArray and HomogeneousCorrelatedSpikeTrains were used inside a network or parallel_run().
* Fixed bug when using different seeds in ``parallel_run()``.
* Global functions defined by ``add_function()`` were not available on CUDA devices.
* The use of ``dendrite.rank`` is deprecated, use ``dendrite.pre_ranks`` instead.
* GPU hardware detection has been improved.