  Traditional neuromorphic hardware architectures rely on event-driven
computation, where the asynchronous transmission of events, such as spikes,
triggers local computations within synapses and neurons. While machine learning
frameworks are commonly used for gradient-based training, their emphasis on
dense data structures poses challenges for processing asynchronous data such as
spike trains. This problem is particularly pronounced for typical tensor data
structures. In this context, we present a novel library (jaxsnn) built on top
of JAX, that departs from conventional machine learning frameworks by providing
flexibility in the data structures used and the handling of time, while
maintaining Autograd functionality and composability. Our library facilitates
the simulation of spiking neural networks and gradient estimation, with a focus
on compatibility with time-continuous neuromorphic backends, such as the
BrainScaleS-2 system, during the forward pass. This approach opens avenues for
more efficient and flexible training of spiking neural networks, bridging the
gap between traditional neuromorphic architectures and contemporary machine
learning frameworks.
