Please use this identifier to cite or link to this item: https://doi.org/10.1109/CCMB.2013.6609161
Title: Spiking-timing based pattern recognition with real-world visual stimuli
Authors: Hu, J.
Tang, H.
Tan, K.C. 
Issue Date: 2013
Citation: Hu, J.,Tang, H.,Tan, K.C. (2013). Spiking-timing based pattern recognition with real-world visual stimuli. Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 : 23-28. ScholarBank@NUS Repository. https://doi.org/10.1109/CCMB.2013.6609161
Abstract: Pattern recognition has been widely studied in the field of computational intelligence. However, primates outperform existing algorithms in cognitive tasks without any difficulty and most of current methods lack enough biological plausibility. Inspired by recent biological findings, a spike-timing based computational model is described, in which information is represented by temporal codes with explicit firing times rather than firing rates of neurons. Visual stimulation is converted into precisely timed spikes by a retina-like model. Encoded spatiotemporal patterns are learned by a temporal learning algorithm based on spiking-timing-dependent plasticity (STDP). The computational model integrates encoding and learning with a unified neural representation closing the gap between them. We show that our integrated model is capable of recognizing real world stimuli such as images successfully with fast and efficient neural computation. © 2013 IEEE.
Source Title: Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/71843
ISBN: 9781467358712
DOI: 10.1109/CCMB.2013.6609161
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