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dc.contributor.authorMain, Linda
dc.contributor.authorThornton, John
dc.date.accessioned2018-06-20T23:20:30Z
dc.date.available2018-06-20T23:20:30Z
dc.date.issued2017
dc.identifier.doi10.1109/SSCI.2017.8285217
dc.identifier.urihttp://hdl.handle.net/10072/377284
dc.description.abstractAbstract: Hierarchical Predictive Coding systems that have adopted prediction as their primary goal, are heavily reliant on the stable sparse coding of sensory input. Furthermore, such systems will require their spatial coding function to be adaptive and able to reform to reflect changes within the environment. These properties of stability and adaptiveness should emerge naturally from the spatial coding system and not be reliant on additional control mechanisms. Hierarchical Temporal Memory is a cortically inspired model that encapsulates both sparse coding and temporal processing functions. We present an investigation into the stability and adaptiveness of three alternative versions of the spatial pooling function. Our results show that two of these SP algorithms are able to form stable sparse distributed representations of audio input, while still remaining adaptive to changes within the input data.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameSSCI 2017
dc.relation.ispartofconferencetitle2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017)
dc.relation.ispartofdatefrom2017-11-27
dc.relation.ispartofdateto2017-12-01
dc.relation.ispartoflocationHonolulu, Hawaii, United States
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleStable sparse encoding for predictive processing
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorThornton, John R.
gro.griffith.authorMain, Linda M.


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    Contains papers delivered by Griffith authors at national and international conferences.

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