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dc.contributor.authorDe Azambuja, Ren
dc.contributor.authorKlein, FBen
dc.contributor.authorStoelen, MFen
dc.contributor.authorAdams, SVen
dc.contributor.authorCangelosi, Aen
dc.date.accessioned2018-01-10T03:17:53Z
dc.date.available2018-01-10T03:17:53Z
dc.date.issued2016-01-01en
dc.identifier.isbn9783319466866en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/10026.1/10539
dc.description.abstract

© Springer International Publishing AG 2016. How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker.

en
dc.format.extent195 - 204en
dc.language.isoenen
dc.titleGraceful degradation under noise on brain inspired robot controllersen
dc.typeConference Contribution
plymouth.volume9947 LNCSen
plymouth.publication-statusPublisheden
plymouth.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.identifier.doi10.1007/978-3-319-46687-3_21en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/00 Groups by role/Professional Services staff
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Institute of Health and Community
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
dc.identifier.eissn1611-3349en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1007/978-3-319-46687-3_21en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeConference Paper/Proceeding/Abstracten


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