Motion Memory: Invariant representations of sequences in cortical L2/3 by Hierarchical Temporal Memory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00328425" target="_blank" >RIV/68407700:21230/18:00328425 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/18:00328425 RIV/68407700:21730/18:00328425
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1877050918323792" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050918323792</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2018.11.091" target="_blank" >10.1016/j.procs.2018.11.091</a>
Alternative languages
Result language
angličtina
Original language name
Motion Memory: Invariant representations of sequences in cortical L2/3 by Hierarchical Temporal Memory
Original language description
We aim to form stable representations of temporal sequences with key focus on semantic learning and streaming data. The state of the art in the Hierarchical Temporal Memory is represented by Numenta’s recently published “ColumnPooler” which emulates functionality of cortical L2/3 layer, forms stable allocentric representations of temporal sequences and/or objects, and has been applied to sensory-motor learning. Our designed experiments evaluate the ColumnPooler for such task and uncover its current limitations. Presented “Motion Memory” design defines needed modifications in order to be effectively used for sequence representation, namely: Semantic distance between the representations; Online learning on streams; ability to represent time; and representation of motion from static sensor. One of the main problems with the current design is the lack of semantic meaning in (continuously updated) representations of the object. The proposed improvement enables MotionMemory to do unsupervised learning on streaming data and resulting representation have semantic meaning, this has many practical applications in sensory processing (ie. vision), or hierarchical learning.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Procedia Computer Science
ISBN
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ISSN
1877-0509
e-ISSN
1877-0509
Number of pages
6
Pages from-to
400-405
Publisher name
Elsevier B.V.
Place of publication
New York
Event location
Praha
Event date
Aug 22, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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