# Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain

arXiv: https://arxiv.org/abs/2107.06676 · HTML: https://mlsanity.com/paper/2107.06676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning representations in Bayesian Confidence Propagation neural networks](https://mlsanity.com/paper/2003.12415/) (2020)
- [StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs](https://mlsanity.com/paper/2106.05373/) (2021)
- [Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers](https://mlsanity.com/paper/2106.04979/) (2021)
- [TensorFlow Doing HPC](https://mlsanity.com/paper/1903.04364/) (2019)

## Abstract (arXiv)

One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning techniques that are inspired by brain models. Such methods use alternative learning rules potentially more efficiently than established learning rules. In this work, we focus on the potential of brain-inspired ML for exploiting High-Performance Computing (HPC) resources to solve ML problems: we discuss the BCPNN and an HPC implementation, called StreamBrain, its computational cost, suitability to HPC systems. As an example, we use StreamBrain to analyze the Higgs Boson dataset from High Energy Physics and discriminate between background and signal classes in collisions of high-energy particle colliders. Overall, we reach up to 69.15% accuracy and 76.4% Area Under the Curve (AUC) performance.