# hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2

arXiv: https://arxiv.org/abs/2212.12210 · HTML: https://mlsanity.com/paper/2212.12210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture](https://mlsanity.com/paper/2404.02248/) (2024)
- [Causal pieces: analysing and improving spiking neural networks piece by piece](https://mlsanity.com/paper/2504.14015/) (2025)

## Abstract (arXiv)

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.