Graceful degradation under noise on brain inspired robot controllers
dc.contributor.author | De Azambuja, R | en |
dc.contributor.author | Klein, FB | en |
dc.contributor.author | Stoelen, MF | en |
dc.contributor.author | Adams, SV | en |
dc.contributor.author | Cangelosi, A | en |
dc.date.accessioned | 2018-01-10T03:17:53Z | |
dc.date.available | 2018-01-10T03:17:53Z | |
dc.date.issued | 2016-01-01 | en |
dc.identifier.isbn | 9783319466866 | en |
dc.identifier.issn | 0302-9743 | en |
dc.identifier.uri | http://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.extent | 195 - 204 | en |
dc.language.iso | en | en |
dc.title | Graceful degradation under noise on brain inspired robot controllers | en |
dc.type | Conference Contribution | |
plymouth.volume | 9947 LNCS | en |
plymouth.publication-status | Published | en |
plymouth.journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
dc.identifier.doi | 10.1007/978-3-319-46687-3_21 | en |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing 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 | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
plymouth.organisational-group | /Plymouth/Users by role/Post-Graduate Research Students | |
dc.identifier.eissn | 1611-3349 | en |
dc.rights.embargoperiod | Not known | en |
rioxxterms.versionofrecord | 10.1007/978-3-319-46687-3_21 | en |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | en |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en |