mlGeNN is a new library for machine learning with Spiking Neural Networks (SNNs), built on the efficient foundation provided by our GeNN simulator. mlGeNN expose the constructs required to build SNNs using an API, inspired by modern ML libraries like Keras, which aims to reduce cognitive load by automatically calculating layer sizes, default hyperparameter values etc to enable rapid prototyping of SNN models.
While there are already a plethora of SNN simulators, most are designed for Computational Neuroscience applications and, as such, not only provide unfamiliar abstractions for ML researchers but also don't support standard ML workflows such as data-parallel batch training. Because of this, researchers have chosen to stick with familiar frameworks such as PyTorch and built libraries to adapt them for SNNs such as BindsNET, NORSE, SNNTorch and Spiking Jelly.
However, these libraries are all constrained by the underlying nature of ML frameworks where the activity of populations of neurons is typically represented as a vector of activities and, for an SNN, this vector is populated with ones for spiking and zeros for non-spiking neurons. This representation allows one to apply the existing infrastructure of the underlying ML framework to SNNs but, as spiking neurons often spike at comparatively low rates, propagating the activity of inactive neurons through the network leads to unnecessary computation.
mlGeNN provides user friendly implementations of novel SNN training algorithms such as e-prop [Bellec2020]_ and EventProp [Wunderlich2021]_ to enable spike-based ML on top of GeNN’s GPU-optimised sparse data structures and algorithms.
.. toctree::
:maxdepth: 3
:titlesonly:
usage/building_networks
usage/datasets
usage/training_networks
usage/callbacks_recording
usage/metrics
usage/converting_tf
usage/bibliography
tutorials/index
mlGeNN reference <source/ml_genn>
mlGeNN TF reference <source/ml_genn_tf>