Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network

The following data was obtained from the NeuroTac tactile sensor mounted on a Dobot magician robotic arm. The NeuroTac was brought into contact with a sharp edge either through sliding horizontally across it ("sliding") or tapping vertically downwards on it ("tapping"). Each type of tactile interaction was repeated 20 times for angles from 0-180 degrees in steps of 10 degrees.

Supported by EPSRC Doctoral Training Partnership (EP/R513179/1), Royal Academy of Engineering Fellowship on "Shared autonomy neuroprosthetics: Bridging the gap between artificial and biological touch" (RF\202021\20\171) and a Leadership Award from the Leverhulme Trust on `A biomimetic forebrain for robot touch' (RL-2016-39)

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Creator(s) Benjamin Ward-Cherrier, Fraser Macdonald
Contributor(s) Nathan Lepora
Publication date 13 Sep 2022
Language eng
Publisher University of Bristol
Licence Non-Commercial Government Licence for public sector information
DOI 10.5523/bris.2r2olkmnek54f2233zgufotruo
Citation Benjamin Ward-Cherrier, Fraser Macdonald (2022): Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network. https://doi.org/10.5523/bris.2r2olkmnek54f2233zgufotruo
Total size 83.1 MiB

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