Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112640
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Type: Conference paper
Title: A neurobiologically plausible vector symbolic architecture
Author: Padilla, D.
McDonnell, M.
Citation: Proceedings of the 8th IEEE International Conference on Semantic Computing (ICSC 2014), 2014 / pp.242-245
Publisher: IEEE
Publisher Place: online
Issue Date: 2014
Series/Report no.: IEEE International Conference on Semantic Computing
ISBN: 147994002X
9781479940028
ISSN: 2325-6516
Conference Name: 8th IEEE International Conference on Semantic Computing (ICSC 2014) (16 Jun 2014 - 18 Jun 2014 : California)
Statement of
Responsibility: 
Daniel E. Padilla and Mark D. McDonnell
Abstract: Vector Symbolic Architectures (VSA) are approaches to representing symbols and structured combinations of symbols as high-dimensional vectors. They have applications in machine learning and for understanding information processing in neurobiology. VSAs are typically described in an abstract mathematical form in terms of vectors and operations on vectors. In this work, we show that a machine learning approach known as hierarchical temporal memory, which is based on the anatomy and function of mammalian neocortex, is inherently capable of supporting important VSA functionality. This follows because the approach learns sequences of semantics-preserving sparse distributed representations.
Rights: © 2014 IEEE
RMID: 0030013756
DOI: 10.1109/ICSC.2014.40
Grant ID: http://purl.org/au-research/grants/arc/DP1093425
Published version: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6881542
Appears in Collections:Electrical and Electronic Engineering publications

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