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Docta Complutense author Shin, Joo-Heon author Smith, David author Swiercz, Waldemar author Staley, Kevin author Rickard, J. Terry author Montero De Juan, Francisco Javier author Kurgan, Lukasz A. author Cios, Krzysztof J. 2023-06-20T03:48:23Z 2023-06-20T03:48:23Z 2010 Joo-Heon Shin, Smith, D., Swiercz, W., Staley, K., Rickard, J.T., Montero, J., Kurgan, L.A., Cios, K.J.: Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons. IEEE Trans. Neural Netw. 21, 1697-1709 (2010). https://doi.org/10.1109/TNN.2010.2050600 1163-6046 10.1109/TNN.2010.2050600 https://hdl.handle.net/20.500.14352/44475 http//:dx.doi.org/10.1109/TNN.2010.2050600 http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5617367&abstractAccess=no&userType=inst In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines. eng Recognition of partially occluded and rotated images with a network of spiking neurons journal article URL https://docta.ucm.es/bitstreams/68501117-575d-4f94-9be8-6e08132c0002/download File MD5 3333f5ffda1a45f41203f4ba1fc09ff3 1604632 application/pdf Montero143.pdf