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.
journal_name
Front Neuroscijournal_title
Frontiers in neuroscienceauthors
Kungl AF,Schmitt S,Klähn J,Müller P,Baumbach A,Dold D,Kugele A,Müller E,Koke C,Kleider M,Mauch C,Breitwieser O,Leng L,Gürtler N,Güttler M,Husmann D,Husmann K,Hartel A,Karasenko V,Grübl A,Schemmel J,Meier K,Petdoi
10.3389/fnins.2019.01201subject
Has Abstractpub_date
2019-11-14 00:00:00pages
1201eissn
1662-4548issn
1662-453Xjournal_volume
13pub_type
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