ArticleFrontiers in neuroscience2019
Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Ultrafast neural sampling with spiking nanolasers.Nature communications · 2025Article
- Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.Brain sciences · 2022Review
- Three learning stages and accuracy-efficiency tradeoff of restricted Boltzmann machines.Nature communications · 2022Article
- Article
- Cortical oscillations support sampling-based computations in spiking neural networks.PLoS computational biology · 2022Article
- A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.Frontiers in neuroscience · 2022Article
- Brain-Inspired Hardware Solutions for Inference in Bayesian Networks.Frontiers in neuroscience · 2021Review
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Authors and funding
23 authors.
Funding
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Abstract
The massively parallel nature of biological information processing plays an important role due to its superiority in comparison 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.
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