Please use this identifier to cite or link to this item: https://doi.org/10.1109/CIHLI.2013.6613261
DC FieldValue
dc.titlePerception and prediction - A connectionist model
dc.contributor.authorIyer, L.R.
dc.contributor.authorHo, S.-B.
dc.date.accessioned2014-11-28T01:54:21Z
dc.date.available2014-11-28T01:54:21Z
dc.date.issued2013
dc.identifier.citationIyer, 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. <a href="https://doi.org/10.1109/CIHLI.2013.6613261" target="_blank">https://doi.org/10.1109/CIHLI.2013.6613261</a>
dc.identifier.isbn9781467359238
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/111628
dc.description.abstractGenerating 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CIHLI.2013.6613261
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1109/CIHLI.2013.6613261
dc.description.sourcetitleProceedings of the 2013 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
dc.description.page25-32
dc.identifier.isiutNOT_IN_WOS
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