Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.

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 fo

SUBMITTER: Kungl AF 

PROVIDER: S-EPMC6868054 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature