Skip to Content
Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
book

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

by Christos Volos, Viet-Thanh Pham
June 2021
Intermediate to advanced
568 pages
18h 20m
English
Academic Press
Content preview from Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Chapter 20: Analog circuit integration of backpropagation learning in memristive HTM architecture

Olga Krestinskayaa; Alex Jamesb    aKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiabIndian Institute of Information Technology and Management, Kerala, India

Abstract

Hierarchical Temporal Memory (HTM) is a biologically plausible model of the neocortex that mimics its structure and functionality. The concepts of HTM and sparse distributed patterns produced by the HTM Spatial Pooler can be useful for various applications. This chapter covers the integration of an analog backpropagation learning circuit into memristive HTM hardware for the Spatial Pooler, which is used for extraction of the meaningful features from the input patterns ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Event-Based Neuromorphic Systems

Event-Based Neuromorphic Systems

Shih-Chii Liu, Tobi Delbruck, Giacomo Indiveri, Adrian Whatley, Rodney Douglas
Body Sensor Networking, Design and Algorithms

Body Sensor Networking, Design and Algorithms

Saeid Sanei, Delaram Jarchi, Anthony G. Constantinides

Publisher Resources

ISBN: 9780128232026