{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:05:00Z","timestamp":1777521900201,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2008,10,1]],"date-time":"2008-10-01T00:00:00Z","timestamp":1222819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2008,10]]},"abstract":"<jats:p>In this article, we describe an adaptive controller for an autonomous mobile robot with a simple structure. Sensorimotor connections were made using a three-layered spiking neural network (SNN) with only one hidden-layer neuron and synapses with spike timing-dependent plasticity (STDP). In the SNN controller, synapses from the hidden-layer neuron to the motor neurons received presynaptic modulation signals from sensory neurons, a mechanism similar to that of the withdrawal reflex circuit of the sea slug, Aplysia. The synaptic weights were modified dependent on the firing rates of the presynaptic modulation signal and that of the hidden-layer neuron by STDP. In experiments using a real robot, which uses a similar simple SNN controller, the robot adapted quickly to the given environment in a single trial by organizing the weights, acquired navigation and obstacle-avoidance behavior. In addition, it followed dynamical changes in the environment. This associative learning scheme can be a new strategy for constructing adaptive agents with minimal structures, and may be utilized as an essential mechanism of an SNN ensemble that binds multiple sensory inputs and generates multiple motor outputs.<\/jats:p>","DOI":"10.1177\/1059712308093869","type":"journal-article","created":{"date-parts":[[2008,10,2]],"date-time":"2008-10-02T07:23:29Z","timestamp":1222932209000},"page":"306-324","source":"Crossref","is-referenced-by-count":9,"title":["A Simple\n                    <i>Aplysia<\/i>\n                    -Like Spiking Neural Network to Generate Adaptive Behavior in                 Autonomous Robots"],"prefix":"10.1177","volume":"16","author":[{"given":"Fady","family":"Alnajjar","sequence":"first","affiliation":[{"name":"Department of System Design Engineering, University of Fukui, Japan,                             -u.ac.jp"}]},{"given":"Kazuyuki","family":"Murase","sequence":"additional","affiliation":[{"name":"Department of System Design Engineering, University of Fukui, Japan,                             -u.ac.jp, Department of Human and                         Artificial Intelligence Systems, Graduate School of Engineering, University                         of Fukui, Japan, Research and Education Program for Life Science, University                         of Fukui, Japan"}]}],"member":"179","published-online":{"date-parts":[[2008,10,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/6.3.406"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065706000640"},{"key":"atypb3","volume-title":"International Joint Conference on Neural Networks (IJCNN'08)","author":"Alnajjar, F."},{"key":"atypb4","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/5254.867912","volume":"15","author":"Atkeson, C.G.","year":"2000","journal-title":"IEEE Intelligent Systems: Special Issue on Humanoid Robotics"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.neuro.24.1.139"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1109\/JRA.1986.1087032"},{"key":"atypb7","volume-title":"Proceedings of the 12th International Joint Conference on Artificial Intelligence","author":"Brooks, R.A."},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.16-02-00425.1996"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2003.1256"},{"key":"atypb10","volume-title":"Proceedings of Evolutionary Robotics IV","author":"Floreano, D."},{"key":"atypb11","doi-asserted-by":"publisher","DOI":"10.1162\/1064546053278900"},{"key":"atypb12","volume-title":"Proceedings of the 7th International Conference on the Simulation of Adaptive Behavior","author":"French, R.L.B."},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706"},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.94.24.12740"},{"key":"atypb15","volume-title":"The organization of behavior","author":"Hebb, D.O.","year":"1949"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.98.3.1282"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.2608-04.2004"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1111\/j.1460-9568.2007.05386.x"},{"key":"atypb19","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.813832"},{"key":"atypb20","volume-title":"In search of memory: The emergence of a new science of mind","author":"Kandel, E.R.","year":"2006"},{"key":"atypb21","volume-title":"Proceedings of the IEEE International Symposium on Computational Intelligence in Robotic and Automation (CIRA2005), F_8054 (CD-ROM)","author":"Kubota, N."},{"key":"atypb22","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"atypb23","doi-asserted-by":"crossref","unstructured":"Maass, W.                  (1999). Computing with spiking neurons. In                      W. Maass                  &                      C. M. Bishop                  (Eds.), Pulsed neural networks (pp. 55-85).                         Cambridge, MA: MIT                     Press.","DOI":"10.7551\/mitpress\/5704.003.0006"},{"key":"atypb24","doi-asserted-by":"publisher","DOI":"10.1016\/S0896-6273(00)81072-7"},{"key":"atypb25","volume-title":"Proceedings of the 3rd International Conference on Experimental Robotics","author":"Mondada, F."},{"key":"atypb26","doi-asserted-by":"crossref","unstructured":"Natschl\u00e4ger, T.                 ,                      Ruf, B. &                      Schmitt, M.                  (2001). Unsupervised learning and self-organization in                     networks of spiking neurons. In                      U. Seiffert                  &                      L. C. Jain                  (Eds.), Self-organizing neural networks: Recent advances and                     applications, studies in fuzziness and soft computing (Vol. 78, pp.                     45- 73). Heidelberg:                         Springer-Verlag.","DOI":"10.1007\/978-3-7908-1810-9_3"},{"key":"atypb27","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/2889.001.0001","volume-title":"Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines","author":"Nolfi, S.","year":"2000"},{"key":"atypb28","doi-asserted-by":"crossref","unstructured":"Nolfi, S.                 ,                      Floreano, D.                 ,                      Miglino, O. &                      Mondada, F.                  (1994). How to evolve autonomous robots: Different approaches                     in evolutionary robotics. In                      R. Brooks                  &                      P. 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