genn-team/genn: GeNN 4.3.0

This release adds a number of significant new features to GeNN as well as making small improvements to PyGeNN. It also includes a number of bug fixes that have been identified since the 4.2.1 release.

1. Previously GeNN performed poorly with large numbers of populations. This version includes a new code generator which effectively solves this problem (see paper).
2. InitSparseConnectivitySnippet::Base row build state and NeuronModels::Base additional input variables could previously only be initialised with a numeric value. Now they can be initialised with a code string supporting substitutions etc.
3. Added GeNN implementation of cortical microcircuit model to userprojects (discussed further in paper). Also demonstrates how to dynamically load GeNN models rather than linking against them.
4. Previously one pushed states and spikes to and from device in PyGeNN using methods like pygenn.genn_model.GeNNModel.push_current_spikes_to_device which was somewhat cumbersome. These have now been wrapped in methods like pygenn.genn_groups.NeuronGroup.push_current_spikes_to_device which is somewhat nicer.
5. The CodeGenerator::generateAll function now returns memory estimates which are, in turn, returned from pygenn.genn_model.GeNNModel.build.
6. To better support batching of inputs into multiple instances of the same model, added ModelSpec::addSlaveSynapsePopulation to add synapse populations which share per-synapse state with a 'master' synapse group.
7. Added extra global parameters to variable initialisation snippets - can be used for lookup table style functionality.
8. Added support for host initialisation of sparse connectivity initialisation snippet extra global parameters. This allows host-based initialisation to be encapsulated within an InitSparseConnectivitySnippet::Base class.

1. Fixed issues preventing spike recorder classes from compiling with GCC 4.9, thanks to Christoph Ostrau for this one!
2. The initialisers for pre and postsynaptic weight update model variables were not searched for references to an RNG when determining whether a neuron group requires an initialisation RNG.
3. Fixed issue with PyGeNN and custom var init snippets that led to segfaults.