genn-team/ml_genn: mlGeNN 2.0

As well as continuing to support the conversion of ANNs trained using TensorFlow to SNNs, this release adds a large amount of new functionality which enables SNNs to be defined from scratch in mlGeNN and trained directly using e-prop.

• New model description API for model description inspired by Keras (see documentation)
• Extensible Callback system allowing custom logic including for recording state to be triggered mid-simulation (see documentation)
• Extensible metrics system, allowing various metrics to be calculated efficiently (see documentation)
• Training using e-prop learning rule
• Conversion of ANNs trained in TensorFlow is now handled through the ml_genn_tf module (see documentation)

• The SpikeNorm algorithm for converting deep ANNs to rate-coded SNNs is currently broken - if you require this functionality please stick with mlGeNN 1.0