Treffer: Modeling of Coupled Memristive-Based Architectures Applicable to Neural Network Models

Title:
Modeling of Coupled Memristive-Based Architectures Applicable to Neural Network Models
Authors:
Source:
MODID-6d55e02e354:IntechOpen
Publisher Information:
IntechOpen
Publication Year:
2018
Document Type:
Fachzeitschrift article in journal/newspaper
File Description:
application/pdf
Language:
English
ISBN:
978-953-51-3947-8
953-51-3947-9
DOI:
10.5772/intechopen.69327
Accession Number:
edsbas.69DAD7FD
Database:
BASE

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This chapter explores the dynamic behavior of dual flux coupled memristor circuits in order to explore the uncharted territory of the fundamental theory of memristor circuits. Neuromorphic computing anticipates highly dense systems of memristive networks, and with nanoscale devices within such close proximity to one another, it is anticipated that flux and charge coupling between adjacent memristors will have a bearing upon their operation. Using the constitutive relations of memristors, various cases of flux coupling are mathematically modeled. This involves analyzing two memristors connected in composite, both serially and in parallel in various polarity configurations. The new behavior of two coupled memristors is characterized based on memristive state equations, and memductance variation represented in terms of voltage, current, charge and flux. The rigorous mathematical analysis based on the fundamental circuit equations of ideal memristors affirms the memristor closure theorem, where coupled memristor circuits behave as different types of memristors with higher complexity.