# Application-oriented automatic hyperparameter optimization for spiking neural network prototyping

arXiv: https://arxiv.org/abs/2502.12172 · HTML: https://mlsanity.com/paper/2502.12172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware](https://mlsanity.com/paper/2205.15864/) (2022)
- [Parallel Hyperparameter Optimization Of Spiking Neural Network](https://mlsanity.com/paper/2403.00450/) (2024)
- [Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices](https://mlsanity.com/paper/2407.04076/) (2024)

## Abstract (arXiv)

Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established machine learning (ML) solutions to the deep learning (DL) domain and the field of spiking neural networks (SNNs). The latter introduce further complexity due to the neuronal computational units and their additional hyperparameters, whose inadequate setting can dramatically impact the final model performance. At the cost of possible reduced generalization capabilities, the most suitable strategy to fully disclose the power of SNNs is to adopt an application-oriented approach and perform extensive HPO experiments. To facilitate these operations, automatic pipelines are fundamental, and their configuration is crucial. In this document, the Neural Network Intelligence (NNI) toolkit is used as reference framework to present one such solution, with a use case example providing evidence of the corresponding results. In addition, a summary of published works employing the presented pipeline is reported as a potential source of insights into application-oriented HPO experiments for SNN prototyping.