  Neuromorphic systems require user-friendly software to support the design and
optimization of experiments. In this work, we address this need by presenting
our development of a machine learning-based modeling framework for the
BrainScaleS-2 neuromorphic system. This work represents an improvement over
previous efforts, which either focused on the matrix-multiplication mode of
BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn,
enables the hardware-in-the-loop training of spiking neural networks within
PyTorch, including support for auto differentiation in a fully-automated
hardware experiment workflow. In addition, hxtorch.snn facilitates seamless
transitions between emulating on hardware and simulating in software. We
demonstrate the capabilities of hxtorch.snn on a classification task using the
Yin-Yang dataset employing a gradient-based approach with surrogate gradients
and densely sampled membrane observations from the BrainScaleS-2 hardware
system.
