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dc.contributor.authorKneller, Adam
dc.contributor.authorThornton, John
dc.contributor.editorAmir Hussain
dc.description.abstractRecent theories have proposed that the unifying principle of brain function is the minimisation of variational free energy and that this is best achieved using a hierarchical predictive coding (HPC) framework. Hierarchical Temporal Memory (HTM) is a model of neocortical function that fits within the free energy framework but does not implement predictive coding. Recent work has attempted to integrate predictive coding and hierarchical message passing into the existing suite of HTM Cortical Learning Algorithms (CLA) producing a PC-CLA hybrid. In this paper we examine for the first time how such hierarchical message passing can be implemented in a pure HTM framework using distal dendrite structures that are already implemented in the CLA temporal pooler. We show this approach outperforms the more simplistic proximal dendrite structures used in the PC-CLA hybrid and also that the new CLA hierarchy is effective for anomaly detection and image reconstruction problems that are beyond the reach of the existing single-level CLA framework.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIJCNN 2015
dc.relation.ispartofconferencetitle2015 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartoflocationKillarney, Ireland
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.titleDistal Dendrite Feedback in Hierarchical Temporal Memory
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorThornton, John R.
gro.griffith.authorKneller, Adam

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

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