@inproceedings{petrovici_pattern_2017,
  title = {Pattern representation and recognition with accelerated analog neuromorphic systems},
  doi = {10.1109/ISCAS.2017.8050530},
  booktitle = {2017 {IEEE} {International} {Symposium} on {Circuits} and {Systems} ({ISCAS})},
  author = {Petrovici, M. A. and Schmitt, S. and Klähn, J. and Stöckel, D. and Schroeder, A. and Bellec, G. and Bill, J. and Breitwieser, O. and Bytschok, I. and Grübl, A. and Güttler, M. and Hartel, A. and Hartmann, S. and Husmann, D. and Husmann, K. and Jeltsch, S. and Karasenko, V. and Kleider, M. and Koke, C. and Kononov, A. and Mauch, C. and Müller, E. and Müller, P. and Partzsch, J. and Pfeil, T. and Schiefer, S. and Scholze, S. and Subramoney, A. and Thanasoulis, V. and Vogginger, B. and Legenstein, R. and Maass, W. and Schüffny, R. and Mayr, C. and Schemmel, J. and Meier, K.},
  month = may,
  year = {2017},
  keywords = {accelerated analog neuromorphic systems, analog components, analogue circuits, artificial neural networks, auxiliary network, biological archetypes, Biomembranes, central nervous system, fast low-power neuromorphic hardware, Hardware, hardware-emulated networks, low-power electronics, neural chips, neural net architecture, Neuromorphics, Neurons, pattern recognition, pattern representation, realistic spiking networks, Robustness, system dynamics, Training},
  pages = {1--4},
  url = {https://ieeexplore.ieee.org/abstract/document/8050530}
}