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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>{{
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]].
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==Related patents==
* Method and apparatus for a sparse distributed memory system US 5113507 A, [[Universities Space Research Association]], 1992<ref>Method and apparatus for a sparse distributed memory system US 5113507 A, by Louis A. Jaeckel, Universities Space Research Association, 1992, URL:
* Method and device for storing and recalling information implementing a kanerva memory system US 5829009 A, [[Texas Instruments]], 1998<ref>Method and device for storing and recalling information implementing a kanerva memory system US 5829009 A, by Gary A. Frazier, Texas Instruments Incorporated, 1998, URL: https://
* Digital memory, Furber, Stephen. US 7512572 B2, 2009<ref>Furber, Stephen B. "Digital memory." U.S. Patent No. 7,512,572. 31 Mar. 2009.___URL: https://
==Implementation==
{{external links|section|date=February 2023}}
* C Binary Vector Symbols (CBVS): includes SDM implementation in [[C (programming language)|C]] as a part of [[vector symbolic architecture]]<ref>{{cite journal|doi=10.1016/j.bica.2014.11.015|title=Vector space architecture for emergent interoperability of systems by learning from demonstration|journal=Biologically Inspired Cognitive Architectures|volume=11|pages=53–64|year=2015|last1=Emruli|first1=Blerim|last2=Sandin|first2=Fredrik|last3=Delsing|first3=Jerker|url=http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-4068 }}</ref> developed by EISLAB at [[Luleå University of Technology]]: http://pendicular.net/cbvs.php {{Webarchive|url=https://web.archive.org/web/20150925123906/http://pendicular.net/cbvs.php |date=2015-09-25 }}<ref>{{cite journal | last1 = Emruli | first1 = Blerim | last2 = Sandin | first2 = Fredrik | year = 2014 | title = Analogical mapping with sparse distributed memory: A simple model that learns to generalize from examples | url = http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-14994| journal = Cognitive Computation | volume = 6 | issue = 1| pages = 74–88 | doi=10.1007/s12559-013-9206-3| s2cid = 12139021 }}</ref>
* CommonSense ToolKit (CSTK) for realtime sensor data processing developed at the [[Lancaster University]] includes implementation of SDM in [[C++]]:
*[[Julia (programming language)|Julia]] implementation by [[Brian Hayes (scientist)|Brian Hayes]]: https://github.com/bit-player/sdm-julia <ref>The Mind Wanders by B. Hayes, 2018. url: http://bit-player.org/2018/the-mind-wanders</ref>
* [[LIDA (cognitive architecture)|Learning Intelligent Distribution Agent (LIDA)]] developed by [[Stan Franklin]]'s lab at the [[University of Memphis]] includes implementation of SDM in [[Java (programming language)|Java]]: http://ccrg.cs.memphis.edu/framework.html
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