# Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling

arXiv: https://arxiv.org/abs/2402.06284 · HTML: https://mlsanity.com/paper/2402.06284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Training Spiking Neural Networks with Local Tandem Learning](https://mlsanity.com/paper/2210.04532/) (2022)
- [Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System](https://mlsanity.com/paper/1703.01909/) (2017)
- [Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning](https://mlsanity.com/paper/2210.04236/) (2022)
- [Maximum a Posteriori Estimators as a Limit of Bayes Estimators](https://mlsanity.com/paper/1611.05917/) (2016)
- [Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks](https://mlsanity.com/paper/2307.06084/) (2023)
- [Energy-Efficient High-Accuracy Spiking Neural Network Inference Using Time-Domain Neurons](https://mlsanity.com/paper/2202.02015/) (2022)

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

- [Learning to Control Dynamical Agents via Spiking Neural Networks and Metropolis-Hastings Sampling](https://mlsanity.com/paper/2507.09540/) (2025)
- [OpenMENA: An Open-Source Memristor Interfacing and Compute Board for Neuromorphic Edge-AI Applications](https://mlsanity.com/paper/2511.03747/) (2025)

## Abstract (arXiv)

This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error (backprop) algorithm and surrogate gradients, widely used to train SNNs in literature. Simulations are conducted within a chip-in-the-loop training context, where an SNN subject to unknown distortion must be trained to detect cancer from measurements, within a biomedical application context. Our results show that the proposed approach strongly outperforms the use of backprop by up to 27% higher accuracy when subject to strong hardware non-idealities. Furthermore, our results also show that the proposed approach outperforms backprop in terms of SNN generalization, needing >10 × less training data for achieving effective accuracy. These findings make the proposed training approach well-suited for SNN implementations in analog subthreshold circuits and other emerging technologies where unknown hardware non-idealities can jeopardize backprop.