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

@article{citeulike:4483517, abstract = {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.}, author = {Kanerva, Pentti}, citeulike-article-id = {4483517}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s12559-009-9009-8}, citeulike-linkout-1 = {http://www.springerlink.com/content/966151841g415165}, day = {1}, doi = {10.1007/s12559-009-9009-8}, journal = {Cognitive Computation}, keywords = {cogsci, vector\_symbolic\_architecture}, month = {June}, number = {2}, pages = {139--159}, posted-at = {2009-05-20 03:20:58}, priority = {0}, title = {Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors}, url = {http://dx.doi.org/10.1007/s12559-009-9009-8}, volume = {1}, year = {2009} }

TY - JOUR ID - citeulike:4483517 L3 - citeulike-article-id:4483517 N2 - 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. IS - 2 JF - Cognitive Computation EP - 159 TI - Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors VL - 1 SP - 139 KW - cogsci KW - vector_symbolic_architecture AU - Kanerva, Pentti PY - 2009/06/01/ UR - http://dx.doi.org/10.1007/s12559-009-9009-8 ER -