Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory [Article]

dc.contributor.authorKrestinskaya, Olga
dc.contributor.authorJames, Alex Pappachen
dc.date.accessioned2019-10-25T03:26:01Z
dc.date.available2019-10-25T03:26:01Z
dc.date.issued2018-03-14
dc.descriptionhttps://arxiv.org/abs/1803.05131en_US
dc.description.abstractHierarchical temporal memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex. Feature encoding is an important step to create sparse binary patterns. This sparsity is introduced by the binary weights and random weight assignment in the initialization stage of the HTM. We propose the alternative deterministic method for the HTM initialization stage, which connects the HTM weights to the input data and preserves natural sparsity of the input information. Further, we introduce the hardware implementation of the deterministic approach and compare it to the traditional HTM and existing hardware implementation. We test the proposed approach on the face recognition problem and show that it outperforms the conventional HTM approach.en_US
dc.identifier.citationKrestinskaya, O. & James, A.P. Analog Integr Circ Sig Process (2018) 95: 457. https://doi.org/10.1007/s10470-018-1161-1en_US
dc.identifier.issn0925-1030
dc.identifier.otherhttps://doi.org/10.1007/s10470-018-1161-1
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4270
dc.language.isoen_USen_US
dc.publisherSpringer USen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectArticleen_US
dc.subjectHierarchical Temporal Memoryen_US
dc.subjectMemristorsen_US
dc.subjectSpatial Pooleren_US
dc.subjectRule based approachen_US
dc.subjectAnalog circuitsen_US
dc.titleFeature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory [Article]en_US
dc.typeArticleen_US
workflow.import.sourcescience

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