Annarchy

* Structural plasticity can be defined at the synaptic level.

* Random distributions are available in synapses.
* Populations can be momentarily disabled/enabled.
* Recording can be done with a given period instead of every time step.
* Different modes for Spike2Rate conversion.
* Extension for shared projections (convolution/pooling) improved.

* CUDA implementation for rate-coded networks with small restrictions.
* Weight-sharing for convolutions and pooling.
* Possibility to create projections using a dense connection matrix or data saved in a file.
* A seed can be defined for the random number generators.

* Simplified internal structure for the generated code.
* Major speed-up for compilation and execution times.
* Rate-coded projections can perform other functions than sum (min/max/mean) over the connected synapses.

* Neuron and Synapse replace RateNeuron, SpikeNeuron, RateSynapse, SpikeSynapse in the main interface (kept for backward compatibility).
* Default PyNN neural models are now available (Izhikevich, IF_curr_exp, IF_cond_exp, IF_curr_alpha, IF_cond_alpha, HH_cond_exp, EIF_cond_alpha_isfa_ista, EIF_cond_exp_isfa_ista).
* Bug fixes for delays and spike propagation.
* Connectivity matrices (CSR) are more efficient.
* Several PyNN examples are added to the examples/ folder.

* Spike conditions can depend on several variables.
* The midpoint numerical method is added.
* Ability to choose globally the numerical method.