Learning memristive spiking neurons and beyond

Olga Krestinskaya; Alex James

doi:10.1016/B978-0-12-821184-7.00034-7

Learning memristive spiking neurons and beyond

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

The design and on-chip implementation of learning algorithms for neuromorphic spike domain memristive architectures is a challenging problem. In this chapter, we provide a short overview of the challenges, open problems, architectures and state of the art implementations of spike-based CMOS-memristive neural networks and systems. The importance of biomimicry, the feasibility of scalability, large-scale information processing, data rate challenges, and building a system of systems make this a vibrant topic for discussion.

Original language	English (US)
Title of host publication	Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
Publisher	Elsevier
Pages	517-528
Number of pages	12
ISBN (Electronic)	9780128211847
DOIs	https://doi.org/10.1016/B978-0-12-821184-7.00034-7
State	Published - Jan 1 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc. All rights reserved.

Keywords

HTM
Learning
LSTM
Spiking neural network

ASJC Scopus subject areas

General Engineering
General Arts and Humanities

Access to Document

10.1016/B978-0-12-821184-7.00034-7

Cite this

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Learning memristive spiking neurons and beyond

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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