{"title":"Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks","authors":"Amir Reza Saffari Azar Alamdari","volume":6,"journal":"International Journal of Computer and Information Engineering","pagesStart":1748,"pagesEnd":1752,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9466","abstract":"<p>In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.<\/p>\r\n","references":"[1] W. Gerstner, W. M. Kistler, Spiking Neuron Model: Single Neuron,\r\nPopulations, and Plasticity. Cambridge University Press 2002.\r\n[2] W. Maass, \"Lower Bounds for the Computational Power of Spiking\r\nNeurons\" Neural Computation, vol. 8, pp 1-40, 1996.\r\n[3] A. J. Patel \"Game Programming: Path Planning\", (Online), http:\/\/wwwcs-\r\nstudents.stanford.edu\/~amitp\/gameprog.html#Paths.\r\n[4] Y. Choe, \"Perceptual Grouping in a Self-Organizing Map of Spiking\r\nNeurons\" Ph.D. dissertation, University of Texas at Austin, 2001.\r\n[5] R. Eckhorn, M. Arndt, P. Dike, \"Feature Linking via Synchronization\r\nAmong Distributed Assemblies: Simulation Results from Cat Visual\r\nCortex\" Neural Computation, vol. 2, pp 293-307, 1990.\r\n[6] R. Eckhorn, R. Bauer, W. Jordan, M. Brosch, W. Kruse, M. Munk, H. J.\r\nReitboeck, \"Coherent Oscillations: A Mechanism of Feature Linking in\r\nthe Visual Cortex?\" Biological Cybernetics, vol. 60, pp 121-130, 1988.\r\n[7] C. W. Eurich, K. Pawelzik, U. Ernst, A. Theil, J. D. Cowan, J. G. Milton\r\n\"Delay Adaptation in the Nervous System\" Neurocomputing, vol. 32-33,\r\npp 741-748, 2000.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 6, 2007"}