Memristive hierarchical temporal memory

Olga Krestinskaya; Irina Dolzhikova; Alex Pappachen James

doi:10.1007/978-3-030-14524-8_14

Memristive hierarchical temporal memory

Olga Krestinskaya, Irina Dolzhikova, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Scopus citations

Abstract

This chapter covers the memristive HTM implementations on mixed-signal and analog hardware. Most of the implemented memristive systems are based on modified HTM algorithm. The HTM is often used as a feature encoding and feature extraction tool, and these features are then used with conventional nearest neighbor method for classification.

Original language	English (US)
Title of host publication	Modeling and Optimization in Science and Technologies
Publisher	Springer [email protected]
Pages	181-194
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-030-14524-8_14
State	Published - Jan 1 2020
Externally published	Yes

ASJC Scopus subject areas

Modeling and Simulation
Medical Assisting and Transcription
Applied Mathematics

Access to Document

10.1007/978-3-030-14524-8_14

Cite this

@inbook{67db932bf6fa410898e45913c21fd03a,

title = "Memristive hierarchical temporal memory",

abstract = "This chapter covers the memristive HTM implementations on mixed-signal and analog hardware. Most of the implemented memristive systems are based on modified HTM algorithm. The HTM is often used as a feature encoding and feature extraction tool, and these features are then used with conventional nearest neighbor method for classification.",

author = "Olga Krestinskaya and Irina Dolzhikova and James, {Alex Pappachen}",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-23",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-14524-8_14",

language = "English (US)",

pages = "181--194",

booktitle = "Modeling and Optimization in Science and Technologies",

publisher = "Springer [email protected]",

}

TY - CHAP

T1 - Memristive hierarchical temporal memory

AU - Krestinskaya, Olga

AU - Dolzhikova, Irina

AU - James, Alex Pappachen

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23

PY - 2020/1/1

Y1 - 2020/1/1

N2 - This chapter covers the memristive HTM implementations on mixed-signal and analog hardware. Most of the implemented memristive systems are based on modified HTM algorithm. The HTM is often used as a feature encoding and feature extraction tool, and these features are then used with conventional nearest neighbor method for classification.

AB - This chapter covers the memristive HTM implementations on mixed-signal and analog hardware. Most of the implemented memristive systems are based on modified HTM algorithm. The HTM is often used as a feature encoding and feature extraction tool, and these features are then used with conventional nearest neighbor method for classification.

UR - http://link.springer.com/10.1007/978-3-030-14524-8_14

UR - http://www.scopus.com/inward/record.url?scp=85064751517&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-14524-8_14

DO - 10.1007/978-3-030-14524-8_14

M3 - Chapter

SP - 181

EP - 194

BT - Modeling and Optimization in Science and Technologies

PB - Springer [email protected]

ER -

Memristive hierarchical temporal memory

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this