| Jahr | 2010 |
| Autor(en) | Johannes Schemmel, Daniel Brüderle, Andreas Grübl, Matthias Hock, Karlheinz Meier and Sebastian Millner |
| Titel | A Wafer-Scale Neuromorphic Hardware System for Large-Scale Neural Modeling |
| KIP-Nummer | HD-KIP 10-27 |
| KIP-Gruppe(n) | F9 |
| Dokumentart | Paper |
| Quelle | Proceedings of the 2010 IEEE International Symposium on Circuits and Systems (Copyright: IEEE) |
| Abstract (en) | Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal an integrated software/hardware framework has been developed which is centered around a unified neural system description language, called PyNN, that allows the scientist to describe a model and execute it in a transparent fashion on either a neuromorphic hardware system or a numerical simulator. A very large analog neuromorphic hardware system developed within FACETS is able to use complex neural models as well as realistic network topologies, i.e. it can realize more than 10000 synapses per neuron, to allow the direct execution of models which previously could have been simulated numerically only. |
| bibtex | @article{schemmeliscas2010,
author = {Schemmel, J. and Br\"uderle, D. and Gr\"ubl, A. and Hock, M. and Meier, K. and Millner, S.},
title = {A Wafer-Scale Neuromorphic Hardware System for Large-Scale Neural Modeling},
journal = {Proceedings of the 2010 IEEE International Symposium on Circuits and Systems (ISCAS"10)},
volume = {},
year = {2010},
pages = {1947--1950}
} |
| Datei | Fulltext PDF |
