Annarchy

* ``pre_spike`` and ``post_spike`` fields of a spiking neuron can now modify pre and post synaptic variables::

pre_spike = """
g_target += w
post.nb_spikes += 1
"""

* ``SpikeSourceArray`` available on CUDA.
* ``Monitor.mean_fr()`` and ``Monitor.histogram()`` now work with PopulationViews (thanks ilysym).
* Many bug fixes with Python3 and CUDA.

* Rate-coded neurons can now use ``sum()`` to sum over all targets, instead of specifying them.
* ``Constant`` objects can now be created to define global-level parameters which do not need to be explicitly defined in Neurons/Synapses.
* Parameter definitions can use constants. Note that changing the constant after compilation does not change the parameter.
* The initial value of a variable (``init=``) can use constants instead of numerical values.
* ``report()`` has been improved and can now generate Markdown reports, which can be converted to html or pdf by pandoc.

* Issue 44: The performance of spiking networks with CUDA has been improved.
* ``Population.compute_firing_rate`` can now be called anytime to change the window over which the mean firing rate of a spiking neuron is computed.
* The performance of the exponential numerical method has been improved by computing the step size outside the loop (when possible).
* Mean-firing rate computation is now available for CUDA devices (slow!).
* Issue 43: the path to the CUDA installation can now be set in a config file at ``~/.config/ANNarchy/annarchy.json``.
* Simulations on GPU can be started on the command line by using ``--gpu`` or ``--gpu=2`` if you have multiple GPUs.

* CUDA implementation for rate-code networks revised and fixed several errors.
* CUDA now supports now the simulation of spiking neurons (please refer to the documentation to discover limitations).
* CUDA allows now the monitoring of dendrites.
* Added the command line argument --cuda to run simulations on CUDA devices.
* Issue 38: user-defined functions are now available in user space::

add_function('sigmoid(x) = 1.0 / (1.0 + exp(-x))')

compile()

x = np.linspace(-10., 10., 1000)
y = functions('sigmoid')(x)

* Major reimplementation of data structures for the connectivity patterns (LIL, CSR).
* Added the ``TimedArray`` population to store the temporal evolution of a population's firing rate internally. See :doc:`../manual/Inputs`.
* Fixed structural plasticity and added an example in ``examples/structural_plasticity``.
* Added the ``projection`` keyword allowing to declare a single parameter/variable for the whole projection, instead of one value per synapse (the default) or one value per post-synaptic neuron (keyword ``postsynaptic``).
* Parameters can now also be recorded by Monitors.
* Projections can now be monitored, if the user knows what he does...
* The global parameters of populations and projections can now be saved/loaded to/from a JSON file for manual parameterization.
* Many bug fixes.
* Deprecated recording functions and objects such as ``RateNeuron`` are removed.

* Sympy 1.0 introduced a regression to the != operator.
* Issue 30: PopulationViews had a bug hen modifying variables on a multidimensional population.
* Issue 33: connect_one_to_one now accepts any kind of popview (even with random indices), as long as they have the same size.
* Issue 33: recording spikes on popviews did not work correctly.
* The normal distribution is fixed in CUDA.
* Delays are now rounded to the next multiple of dt, not cast into a int (i.e. 1.99 is now 2, not 1 with dt=1.0).
* SpikeSourceArray is now tolerant to very high firing schedules.

* Spiking networks can now use variable delays, at the cost of extra computations.
* Fixed bug in parser when a synapse uses pre/post variables with one name containing the other (e.g. r and r_mean).
* Fixed bug when assigning postsynptic variable a random distribution.
* Added the ability to access the weighted sums of a rate-coded population with::

pop.sum('exc')

* Added the ability to record these weighted sums::

m = monitor(pop, ['r', 'sum(exc)'])