--- abstract: 'Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition community and only a few studies have been published in the scientific literature. This paper reviews HTM architecture and related learning algorithms by using formal notation and pseudocode description. Novel approaches are then proposed to encode coincidence-group membership (fuzzy grouping) and to derive temporal groups (maxstab temporal clustering). Systematic experiments on three line-drawing datasets have been carried out to better understand HTM peculiarities and to extensively compare it against other well-know pattern recognition approaches. Our results prove the effectiveness of the new algorithms introduced and that HTM, even if still in its infancy, compares favorably with other existing technologies.' altloc: - http://bias.csr.unibo.it/maltoni/HTM_TR_v1.0.pdf chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - davide.maltoni@unibo.it creators_name: - family: Maltoni given: Davide honourific: Prof. lineage: '' date: 2011-04-13 date_type: published datestamp: 2014-02-25 12:49:04 department: DEIS dir: disk0/00/00/91/87 edit_lock_since: ~ edit_lock_until: 0 edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 9187 fileinfo: /style/images/fileicons/application_pdf.png;/9187/1/HTM_TR_v1.0.pdf full_text_status: public importid: ~ institution: University of Bologna isbn: ~ ispublished: unpub issn: ~ item_issues_comment: [] item_issues_count: ~ item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: ~ lastmod: 2014-02-25 12:49:04 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: ~ pubdom: FALSE publication: ~ publisher: ~ refereed: FALSE referencetext: ~ relation_type: [] relation_uri: [] reportno: ~ rev_number: 10 series: ~ source: ~ status_changed: 2014-02-25 12:49:04 subjects: - comp-neuro-sci - comp-sci-mach-vis - comp-sci-neural-nets succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Pattern Recognition by Hierarchical Temporal Memory type: techreport userid: 22383 volume: ~