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Authors: Igor Peric ; Alexandru Lesi ; Daniel Spies ; Stefan Ulbrich ; Arne Roennau ; Marius Zoellner and Ruediger Dillman

Affiliation: FZI Forschungszentrum Informatik, Germany

ISBN: 978-989-758-270-7

Keyword(s): Vector Symbolic Architectures, Associative Memories, Symbol Encoding, Symbolic Scripting.

Related Ontology Subjects/Areas/Topics: Health Engineering and Technology Applications ; Information Processing ; Learning Systems and Memory ; Neurocomputing ; Neurotechnology, Electronics and Informatics

Abstract: Vector Symbolic Architectures (VSAs) define a set of operations for association, storage, manipulation and retrieval of symbolic concepts, represented as fixed-length vectors in IRn. A specific instance of VSAs, Holo- graphic Reduced Representations (HRRs), have proven to exhibit properties similar to human short-term mem- ory and as such are interesting for computational modelling. In this paper we extend the HRR approach by introducing implicit, topology-preserving encoding and decoding procedures. We propose to replace unique symbolic representations with symbols based on probability density functions. These symbols must be ran- domly permuted to ensure the uniform distribution of signals across Fourier space where embedding takes place. These novel encoding schemes eliminate the need for so-called clean-up modules after memory re- trieval (e.g., self-organizing maps). Effectively each encoding implicitly represents its scalar symbol, so no further lookup is needed. We further show that our encoding scheme has a positive impact on memory capacity in comparison to the original capacity benchmark for HRRs (Plate, 1995). We also evaluate our memories in two different robotics tasks: visual scene memory and state machine scripting (holographic controllers). (More)


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Paper citation in several formats:
Peric, I.; Lesi, A.; Spies, D.; Ulbrich, S.; Roennau, A.; Zoellner, M. and Dillman, R. (2017). Probabilistic Symbol Encoding for Convolutional Associative Memories. In Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX, ISBN 978-989-758-270-7, pages 22-29. DOI: 10.5220/0006503000220029

author={Igor Peric. and Alexandru Lesi. and Daniel Spies. and Stefan Ulbrich. and Arne Roennau. and Marius Zoellner. and Ruediger Dillman.},
title={Probabilistic Symbol Encoding for Convolutional Associative Memories},
booktitle={Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX,},


JO - Proceedings of the 5th International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX,
TI - Probabilistic Symbol Encoding for Convolutional Associative Memories
SN - 978-989-758-270-7
AU - Peric, I.
AU - Lesi, A.
AU - Spies, D.
AU - Ulbrich, S.
AU - Roennau, A.
AU - Zoellner, M.
AU - Dillman, R.
PY - 2017
SP - 22
EP - 29
DO - 10.5220/0006503000220029

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