Sparse distributed memory: Difference between revisions

Revision as of 15:00, 15 December 2024

Revision as of 09:08, 27 May 2025

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==Definition==

Human memory has a tendency to [[Multiple trace theory|congregate memories]] based on similarities between them (although they may not be related), such as "firetrucks are red and apples are red".<ref name=ship>{{cite web|title=General Psychology|url=http://webspace.ship.edu/cgboer/memory.html|publisher=Shippensburg University|author=C. George Boeree|year=2002}}</ref> Sparse distributed memory is a mathematical representation of human memory, and uses [[Clustering high-dimensional data|high-dimensional space]] to help model the large amounts of memory that mimics that of the human neural network.<ref name=psu>{{cite journal|title=Sparse Distributed Memory and Related Models|pages=50–76|citeseerx=10.1.1.2.8403|publisher=Pennsylvania State University|author=Pentti Kanerva|year=1993}}</ref><ref name=stanford>{{citeCite FTP web|title=Sparse Distributed Memory: Principles and Operation|url=ftp://reports.stanford.edu/pub/cstr/reports/csl/tr/89/400/CSL-TR-89-400.pdf|publisher=Stanford University|access-date=1 November 2011|author1=M. J. Flynn|author2=P. Kanerva|author3=N. Bhadkamkar|name-list-style=amp|dateserver=DecemberStanford 1989}}{{University|url-status=dead link|date=May 2018 |bot=InternetArchiveBot |fix-attempted=yesDecember 1989}}</ref> An

important property of such high dimensional spaces is that two randomly chosen vectors are relatively far away from each other, meaning that they are uncorrelated.<ref name=integerSDM/> SDM can be considered a realization of [[locality-sensitive hashing]].