nach oben

Cognitive Computation

Erschienen in:

01.06.2009

Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors

verfasst von: Pentti Kanerva

Erschienen in: Cognitive Computation | Ausgabe 2/2009

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The 1990s saw the emergence of cognitive models that depend on very high dimensionality and randomness. They include Holographic Reduced Representations, Spatter Code, Semantic Vectors, Latent Semantic Analysis, Context-Dependent Thinning, and Vector-Symbolic Architecture. They represent things in high-dimensional vectors that are manipulated by operations that produce new high-dimensional vectors in the style of traditional computing, in what is called here hyperdimensional computing on account of the very high dimensionality. The paper presents the main ideas behind these models, written as a tutorial essay in hopes of making the ideas accessible and even provocative. A sketch of how we have arrived at these models, with references and pointers to further reading, is given at the end. The thesis of the paper is that hyperdimensional representation has much to offer to students of cognitive science, theoretical neuroscience, computer science and engineering, and mathematics.

Vorheriger Artikel On the Natural Hierarchical Composition of Cliques in Cell Assemblies

Nächster Artikel Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Anderson JA. A simple neural network generating an interactive memory. Math Biosci. 1972;14:197–220.CrossRef

Kohonen T. Correlation matrix memories. IEEE Trans Comput. 1984;C21(4):353–9.

Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA. 1982;79(8):2554–8.PubMedCrossRef

Kanerva P. Sparse distributed memory. Cambridge, MA: MIT Press; 1988.

Karlsson R. A fast activation mechanism for the Kanerva SDM memory. In: Uesaka Y, Kanerva P, Asoh H, editors. Foundations of real-world computing. Stanford: CSLI; 2001. p. 289–93.

Aleksander I, Stonham TJ, Wilkie BA. Computer vision systems for industry: WISARD and the like. Digit Syst Ind Autom. 1982;1:305–23.

Hinton GH, Anderson JA, editors. Parallel models of associative memory. Hillsdale, NJ: Erlbaum; 1981.

Hassoun MH, editor. Associative neural memories: theory and implementation. New York, Oxford: Oxford University Press; 1993.

Kohonen T. Self-organization and associative memory. 3rd ed. Berlin: Springer; 1989.

10.

Palm G. Neural assemblies: an alternative approach to artificial intelligence. Heidelberg: Springer; 1982.

11.

Hinton GE. Mapping part–whole hierarchies into connectionist networks. Artif Intell. 1990;46(1–2):47–75.CrossRef

12.

Smolensky P. Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artif Intell. 1990;46(1–2):159–216.CrossRef

13.

Plate T. Holographic Reduced Representations: convolution algebra for compositional distributed representations. In: Mylopoulos J, Reiter R, editors. Proc. 12th int’l joint conference on artificial intelligence (IJCAI). San Mateo, CA: Kaufmann; 1991. p. 30–35.

14.

Plate TA. Holographic reduced representation: distributed representation of cognitive structure. Stanford: CSLI; 2003.

15.

Kanerva P. Binary spatter-coding of ordered K-tuples. In: von der Malsburg C, von Seelen W, Vorbruggen JC, Sendhoff B, editors. Artificial neural networks – ICANN 96 proceedings (Lecture notes in computer science, vol. 1112). Berlin: Springer; 1996. p. 869–73.

16.

Gayler RW. Multiplicative binding, representation operators, and analogy. Poster abstract. In: Holyoak K, Gentner D, Kokinov B, editors. Advances in analogy research. Sofia: New Bulgarian University; 1998. p. 405. Full poster http://cogprints.org/502/. Accessed 15 Nov 2008.

17.

Rachkovskij DA, Kussul EM. Binding and normalization of binary sparse distributed representations by context-dependent thinning. Neural Comput. 2001;13(2):411–52.CrossRef

18.

Kussul EM, Baidyk TN. On Information encoding in associative–projective neural networks. Report 93-3. Kiev, Ukraine: V.M. Glushkov Inst. of Cybernetics; 1993 (in Russian).

19.

Landauer T, Dumais S. A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychol Rev. 1997;104(2):211–40.CrossRef

20.

Kanerva P, Kristoferson J, Holst A. Random Indexing of text samples for latent semantic analysis. Poster abstract. In: Gleitman LR, Josh AK, editors. Proc. 22nd annual conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum; 2000. p. 1036. Full poster http://www.rni.org/kanerva/cogsci2k-poster.txt. Accessed 23 Nov 2008.

21.

Papadimitriou C, Raghavan P, Tamaki H, Vempala S. Latent semantic indexing: a probabilistic analysis. Proc. 17th ACM symposium on the principles of database systems. New York: ACM Press; 1998. p. 159–68.

22.

Kaski S. Dimensionality reduction by random mapping: fast similarity computation for clustering. Proc. int’l joint conference on neural networks, IJCNN’98. Piscataway, NJ: IEEE Service Center; 1999. p. 413–8.

23.

Indyk P. Algorithmic aspects of low-distortion geometric embeddings. Annual symposium on foundations of computer science (FOCS) 2001 tutorial. http://people.csail.mit.edu/indyk/tut.ps. Accessed 15 Nov 2008.

24.

Schütze H. Word space. In: Hanson SJ, Cowan JD, Giles CL, editors. Advances in neural information processing systems 5. San Mateo, CA: Kaufmann; 1993. p. 895–902.

25.

Lund K, Burgess C, Atchley R. Semantic and associative priming in high-dimensional semantic space. Proc. 17th annual conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum; 1995. p. 660–5.

26.

Sahlgren M. The word-space model. Doctoral dissertation. Department of Linguistics, Stockholm University; 2006. http://www.sics.se/∼mange/TheWordSpaceModel.pdf. Accessed 23 Nov 2008.

27.

Jones MN, Mewhort DJK. Representing word meaning and order information in a composite holographic lexicon. Psychol Rev. 2007;114(1):1–37.PubMedCrossRef

28.

Sahlgren M, Holst A, Kanerva P. Permutations as a means to encode order in word space. Proc. 30th annual conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. p. 1300–5.

29.

Widdows D. Geometry and meaning. Stanford: CSLI; 2004.

Titel: Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors
verfasst von: Pentti Kanerva
Publikationsdatum: 01.06.2009
Verlag: Springer-Verlag
Erschienen in: Cognitive Computation / Ausgabe 2/2009
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-009-9009-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2009

Ultimate Cognition à la Gödel

On the Natural Hierarchical Composition of Cliques in Cell Assemblies

Time-Scale Feature Extractions for Emotional Speech Characterization

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

Premium Partner