Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00305558" target="_blank" >RIV/68407700:21230/16:00305558 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S1877050916316866" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1877050916316866</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2016.07.430" target="_blank" >10.1016/j.procs.2016.07.430</a>
Alternative languages
Result language
angličtina
Original language name
Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection
Original language description
Our presented idea is to integrate artificial neural network (probably of BICA type) with a real biological network (ideally in the future with the human brain) in order to extend or enhance cognitive- and sensory- capabilities (e.g. by associating existing and artificial sensory inputs). We propose to design such neuro-module using Hierarchical Temporal Memory (HTM) which is a biologically-inspired model of the mammalian neocortex. A complex task of contextual anomaly detection was chosen as our case-study, where we evaluate capabilities of a HTM module on a specifically designed synthetic dataset and propose improvements to the anomaly model. HTM is framed within other common AI/ML approaches and we conclude that HTM is a plausible and useful model for designing a direct brain-extension module and draft a design of a neuromorphic interface for processing asynchronous inputs. Outcome of this study is the practical evaluation of HTM's capabilities on the designed synthetic anomaly dataset, a review of problems of the HTM theory and the current implementation, extended with suggested interesting research direction for the future.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
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
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Number of pages
7
Pages from-to
232-238
Publisher name
Elsevier B.V.
Place of publication
New York
Event location
New York City, NY
Event date
Jul 16, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391723200033