# Accelerated physical emulation of Bayesian inference in spiking neural networks

arXiv: https://arxiv.org/abs/1807.02389 · HTML: https://mlsanity.com/paper/1807.02389/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network](https://mlsanity.com/paper/2304.11857/) (2023)
- [Differentiable graph-structured models for inverse design of lattice materials](https://mlsanity.com/paper/2304.05422/) (2023)
- [Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference](https://mlsanity.com/paper/2306.12465/) (2023)
- [Automotive Object Detection via Learning Sparse Events by Spiking Neurons](https://mlsanity.com/paper/2307.12900/) (2023)
- [Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines](https://mlsanity.com/paper/2209.00873/) (2022)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs](https://mlsanity.com/paper/2509.04506/) (2025)
- [A method for the ethical analysis of brain-inspired AI](https://mlsanity.com/paper/2305.10938/) (2023)
- [Guidance and Control Neural Network Acceleration using Memristors](https://mlsanity.com/paper/2509.02369/) (2025)

## Abstract (arXiv)

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.