| year | 2017 |
| author(s) | Johannes Schemmel, Laura Kriener, Paul Müller, Karlheinz Meier |
| title | An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites |
| KIP-Nummer | HD-KIP 17-22 |
| KIP-Gruppe(n) | F9 |
| document type | Paper |
| source | Proceedings of the 2017 IEEE International Joint Conference on Neural Networks |
| doi | 10.1109/IJCNN.2017.7966124 |
| Abstract (en) | This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model. The scalable neuromorphic architecture is extended by the support for multi-compartment models and non-linear dendrites. These features are part of a 65 nm prototype ASIC. It allows to emulate different spike types observed in cortical pyramidal neurons: NMDA plateau potentials, calcium and sodium spikes. By replicating some of the structures of these cells, they can be configured to perform coincidence detection within a single neuron. Built-in plasticity mechanisms can modify not only the synaptic weights, but also the dendritic synaptic composition to efficiently train large multi-compartment neurons. Transistor-level simulations demonstrate the functionality of the analog implementation and illustrate analogies to biological measurements. |
| bibtex | @article{schemmel2017accelerated,
author = {Johannes Schemmel, Laura Kriener, Paul M\"uller, Karlheinz Meier},
title = {An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites},
journal = {Proceedings of the 2017 IEEE International Joint Conference on Neural Networks},
year = {2017},
volume = {},
pages = {},
doi = {10.1109/IJCNN.2017.7966124},
url = {http://ieeexplore.ieee.org/document/7966124/}
} |
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| URL | arXiv link |
| URL | IEEE Xplore |
