Biologically Inspired SNN for Robot Control
This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is imple...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2013-02, Vol.43 (1), p.115-128 |
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| container_end_page | 128 |
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| container_title | IEEE transactions on cybernetics |
| container_volume | 43 |
| creator | Nichols, Eric McDaid, Liam J. Siddique, Nazmul |
| description | This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented. |
| doi_str_mv | 10.1109/TSMCB.2012.2200674 |
| format | Article |
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A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.</description><subject>Artificial Intelligence</subject><subject>Biological system modeling</subject><subject>Computer Simulation</subject><subject>Dynamic synapses</subject><subject>Models, Neurological</subject><subject>Neural Networks, Computer</subject><subject>Neuronal Plasticity</subject><subject>Neurons</subject><subject>Neurotransmitters</subject><subject>Robot sensing systems</subject><subject>Robotics - methods</subject><subject>self-organization</subject><subject>spiking neural network (SNN)</subject><subject>temporal difference (TD) learning rule</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNo9kMtOwzAQRS0EolXpD4CEskRCKfb4EXtJIx6VSpFoWVuJ46CgtC52sujf49LS2cxo5txZHISuCZ4QgtXDavmWTyeACUwAMBYZO0NDIEKmABk_P80iG6BxCN84lowrJS_RICJUCI6H6H7auNZ9NaZo210y24Rt422VLBeLpHY--XCl65LcbTrv2it0URdtsONjH6HP56dV_prO319m-eM8NRRolxpuSCkqVjJJlClBWk5AMcmVUhKMlCVgSyvLFTMVqwso6opywbPKAisw0BG6O_zdevfT29DpdROMbdtiY10fNMlAUaaAq4jCATXeheBtrbe-WRd-pwnWe0_6z5Pee9JHTzF0e_zfl2tbnSL_ViJwcwAaa-3pLGIeMkl_AYTRac0</recordid><startdate>201302</startdate><enddate>201302</enddate><creator>Nichols, Eric</creator><creator>McDaid, Liam J.</creator><creator>Siddique, Nazmul</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>201302</creationdate><title>Biologically Inspired SNN for Robot Control</title><author>Nichols, Eric ; McDaid, Liam J. ; Siddique, Nazmul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c323t-c5c1b6d4b4819cb28e512948599982c88b20e3de594cd4fa2afd35657de24a023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial Intelligence</topic><topic>Biological system modeling</topic><topic>Computer Simulation</topic><topic>Dynamic synapses</topic><topic>Models, Neurological</topic><topic>Neural Networks, Computer</topic><topic>Neuronal Plasticity</topic><topic>Neurons</topic><topic>Neurotransmitters</topic><topic>Robot sensing systems</topic><topic>Robotics - methods</topic><topic>self-organization</topic><topic>spiking neural network (SNN)</topic><topic>temporal difference (TD) learning rule</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nichols, Eric</creatorcontrib><creatorcontrib>McDaid, Liam J.</creatorcontrib><creatorcontrib>Siddique, Nazmul</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nichols, Eric</au><au>McDaid, Liam J.</au><au>Siddique, Nazmul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Biologically Inspired SNN for Robot Control</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TCYB</stitle><addtitle>IEEE Trans Cybern</addtitle><date>2013-02</date><risdate>2013</risdate><volume>43</volume><issue>1</issue><spage>115</spage><epage>128</epage><pages>115-128</pages><issn>2168-2267</issn><eissn>2168-2275</eissn><coden>ITCEB8</coden><abstract>This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. 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| ispartof | IEEE transactions on cybernetics, 2013-02, Vol.43 (1), p.115-128 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial Intelligence Biological system modeling Computer Simulation Dynamic synapses Models, Neurological Neural Networks, Computer Neuronal Plasticity Neurons Neurotransmitters Robot sensing systems Robotics - methods self-organization spiking neural network (SNN) temporal difference (TD) learning rule |
| title | Biologically Inspired SNN for Robot Control |
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