mlGeNN 2.3.0

This release adds a number of new features to mlGeNN as well as including a number of bug fixes that have been identified since the 2.2.1 release. This version requires GeNN 5.1.0.

User Side Changes

• Added support for performing regression using EventProp (#98)
• Added support for training models with fixed and learnable heterogeneous delays with EventProp (#104)
• Added re-implementation of best-performing SHD model from "Loss shaping enhances exact gradient learning with EventProp in Spiking Neural Networks" to examples (#114)
• Added Yin yang dataset, Time To First Spike readout and event-prop implementation (#117)

Bug fixes

• Fixed issue with input neurons with batch size 1 (#103)
• Fixed several bugs in the EventProp compiler (#110, #115)

mlGeNN 2.2.1

This release includes a number of bug fixes that have been identified since the 2.2.0 release.

Bug fixes

• Fixed issues with spike recorder (#96)
• Fixed bug in event prop effecting learning accuracy (#96)
• Fixed bug in mean squared error loss function (#96)

mlGeNN 2.2

This release adds a number of new features to mlGeNN as well as including a number of bug fixes that have been identified since the 2.1 release. This version is also the first mlGeNN release to use GeNN 5.

User Side Changes

• Data parallel training support (#79)
• Added predict method to CompiledInferenceNetwork to return raw model predictions rather than metrics (#84)
• Added histogram_thresh keyword argument to ml_genn.utils.data.preprocess_tonic_spikes to ensure input spike trains don't contain duplicate spikes for the same neuron within one timstep (#86)

Bug fixes

• Fixed bug effecting non-square inputs in ml_genn.utils.data.preprocess_tonic_spikes (#83)

mlGeNN 2.1

This release adds a number of significant new features to mlGeNN including support for training models using EventProp, as well as including a number of bug fixes that have been identified since the 2.0 release.

User Side Changes

• EventProp compiler for training models with EventProp (#57, #64, #70)
• System so compilers can define default settings for neuron models e.g. reset behaviour (#63)
• Support for time varying inputs as well as a wider range of input neuron types (#69)
• Spike-like event recording (#54)
• Spike count recording (#73)

Bug fixes

• Fixed issues with manual training loops e.g. for augmentation (#74, #78)
• Fixed issue with management of callback state (#65)
• Fixed issue with loading and unloading compiled networks (#66)

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.

User Side Changes

• 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)

Known issues

• 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

mlGeNN 1.0

Release of 'legacy' mlGeNN with TensorFlow 2 dependency - supports conversion of trained ANN to SNN