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dc.contributor.authorWennekers, Thomas
dc.contributor.authorCangelosi, Angelo
dc.date.accessioned2018-05-10T15:16:28Z
dc.date.issued2018-12
dc.identifier.issn2162-2388
dc.identifier.issn2162-2388
dc.identifier.urihttp://hdl.handle.net/10026.1/11494
dc.description.abstract

We present here a learning system using the iCub humanoid robot and the SpiNNaker neuromorphic chip to solve the real-world task of object-specific attention. Integrating spiking neural networks with robots introduces considerable complexity for questionable benefit if the objective is simply task performance. But, we suggest, in a cognitive robotics context, where the goal is understanding how to compute, such an approach may yield useful insights to neural architecture as well as learned behavior, especially if dedicated neural hardware is available. Recent advances in cognitive robotics and neuromorphic processing now make such systems possible. Using a scalable, structured, modular approach, we build a spiking neural network where the effects and impact of learning can be predicted and tested, and the network can be scaled or extended to new tasks automatically. We introduce several enhancements to a basic network and show how they can be used to direct performance toward behaviorally relevant goals. Results show that using a simple classical spike-timing-dependent plasticity (STDP) rule on selected connections, we can get the robot (and network) to progress from poor task-specific performance to good performance. Behaviorally relevant STDP appears to contribute strongly to positive learning: "do this" but less to negative learning: "don't do that." In addition, we observe that the effect of structural enhancements tends to be cumulative. The overall system suggests that it is by being able to exploit combinations of effects, rather than any one effect or property in isolation, that spiking networks can achieve compelling, task-relevant behavior.

dc.format.extent6132-6144
dc.format.mediumPrint-Electronic
dc.languageeng
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subjectCognitive
dc.subjectlearning
dc.subjectmultiscale
dc.subjectneuromorphic
dc.subjectrobotics
dc.subjectspike-timing-dependent plasticity (STDP)
dc.titleBehavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000451230100029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue12
plymouth.volume29
plymouth.publication-statusPublished
plymouth.journalIEEE Transactions on Neural Networks and Learning Systems
dc.identifier.doi10.1109/TNNLS.2018.2816518
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Admin Group - REF
plymouth.organisational-group/Plymouth/Admin Group - REF/REF Admin Group - FoSE
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
dc.publisher.placeUnited States
dcterms.dateAccepted2018-03-05
dc.rights.embargodate2018-6-15
dc.identifier.eissn2162-2388
dc.rights.embargoperiodNot known
rioxxterms.funderEPSRC
rioxxterms.identifier.projectBABEL
rioxxterms.versionofrecord10.1109/TNNLS.2018.2816518
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
plymouth.funderBABEL::EPSRC


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