| Jahr | 2016 |
| Autor(en) | Mihai A. Petrovici*, Luziwei Leng*, Oliver Breitwieser*, David Stöckel*, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier |
| Titel | Stochastic inference with spiking neural networks |
| KIP-Nummer | HD-KIP 16-90 |
| KIP-Gruppe(n) | F9 |
| Dokumentart | Paper |
| Quelle | BMC Neuroscience 2016, 17(Suppl 1):P96 |
| doi | 10.1186/s12868-016-0283-6 |
| Abstract (en) | Brains are adept at creating an impressively accurate internal model of their surrounding based on incomplete and noisy sensory data. Understanding this inferential prowess is not only interesting for neuroscience, but may also inspire computational architectures and algorithms for solving hard inference problems. Here, we give an overview of our work on probabilistic inference with brain-inspired spiking networks, their advantages compared to classical neural networks and their implementation in neuromorphic hardware. |
| bibtex | @inproceedings{petrovici2016stochastic,
author = {Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier},
title = {Stochastic inference with spiking neural networks},
booktitle = {},
year = {2016},
volume = {17},
pages = {P96}
} |
| Datei | Abstract |
| Datei | Poster |
