Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Design of Neuromorphic Cognitive Module based on Hierarchical Temporal Memory and Demonstrated on Anomaly Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Computer Science

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

7

Strana od-do

232-238

Název nakladatele

Elsevier B.V.

Místo vydání

New York

Místo konání akce

New York City, NY

Datum konání akce

16. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391723200033