Please use this identifier to cite or link to this item: https://doi.org/10.1109/CIHLI.2013.6613261
Title: Perception and prediction - A connectionist model
Authors: Iyer, L.R.
Ho, S.-B. 
Issue Date: 2013
Citation: Iyer, L.R.,Ho, S.-B. (2013). Perception and prediction - A connectionist model. Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 : 25-32. ScholarBank@NUS Repository. https://doi.org/10.1109/CIHLI.2013.6613261
Abstract: Generating appropriate responses to incoming stimuli is a fundamental task of an organism. However, in order to generate intelligent responses, it is important to have a deeper understanding of the environment, and make predictions based on this knowledge. Although the ability to make predictions is intrinsic in humans and many animals, it is still a difficult task for a machine with no in built knowledge about the situation. In this paper we present a biologically inspired neural network model that predicts the future trajectory of a moving object after observing its current trajectory. © 2013 IEEE.
Source Title: Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/111628
ISBN: 9781467359238
DOI: 10.1109/CIHLI.2013.6613261
Appears in Collections:Staff Publications

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