A Universal Mutator for Transformations Among Memristor, Memcapacitor, and Meminductor

Yu, Dongsheng; Liang, Yan; Iu, Herbert Ho-Ching; Chua, Leon O.

doi:10.1109/tcsii.2014.2345305

Cited by 77 publications

(24 citation statements)

References 30 publications

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“…The theoretical analysis is confirmed in the same paper by use of a separately presented memristor emulator circuit from Ref. [17]. However, the results in the analysis is based on a memristor which exhibits a linear relationship between memductance and flux.…”

Section: Introductionmentioning

confidence: 53%

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

Eshraghian¹,

Iu²,

KamranEshraghian³

2018

Memristor and Memristive Neural Networks

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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.

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Section: Introductionmentioning

confidence: 53%

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

Eshraghian¹,

Iu²,

KamranEshraghian³

2018

Memristor and Memristive Neural Networks

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“…The time constant of the reconstruction filter was τr = T = 10 μs, which smoothed reasonably its staircase output, and the processing delay was tc = 3 μs. Then the roots of the characteristic equation (8) are as follows: λ1,2 = 0.9978 ± j 0.002289, λ3 = 0.3692, λ4 = -3.4988 10 -4 , which indicates a stable operation. It can be shown that the doublet λ1,2 corresponds to the poles of (28) because the FHN model is driven by a high-resistance source, which behaves like a stimulation by a current source (see Figure 5).…”

Section: ) Direct Digital Emulationmentioning

confidence: 99%

“…Singlepurpose emulators such as [4][5][6] are simple and elegant, but their disadvantage is the inability to change easily their characteristics. Emulators of memristive, memcapacitive and meminductive devices based on mutators provide large universality because they transform a nonlinear resistor, which can be easily modified, to the respective constitution relation of the mem element [7][8].…”

Section: Introductionmentioning

confidence: 99%

Emulation of Bio-Inspired Networks

Kolka¹,

Biolková²,

Biolek³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The paper deals with hardware emulation of bio-inspired devices and nonlinear dynamic processes of complex nature by means of mixed-mode analog-digital emulators. The discretized state model of the emulated system serves for real-time calculation of dependent quantities. In contrast to input-output emulation known in control systems, the proposed approach emulates the ports of an electrical multiport network. The paper discusses the stability of the emulation process and the possibility of partitioning the system into two parts, one being emulated digitally and the other via an analog circuitry. The procedure is illustrated on the example of emulating the Fitzhugh-Nagumo model of neuron and the model of amoeba adaptation. The paper is an extension of our paper presented at the NGCAS 2018 conference in Valletta, Malta. The extended version deals newly with the choice of the integration method and provides a deeper stability analysis and more examples of emulation of biological models.

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“…where u is a vector field with ∇.u = −Γ{F, G}. (34) In this case, the dissipation function on phase space is given by By Eq. (34) we have ∇.u = −Γ.…”

Section: B Symplectic Noise Modelsmentioning

confidence: 99%

“…Markovian models that include dissipation may then be formulated on purely phenomenological grounds, however, it is clear that it is desirable to have a theory that is capable of dealing with non-linear dissipation, in particular, we wish to include charge and current dependent resistances, which brings us to the formalism introduced by Chua [27] for a unified axiomatic description of passive circuit components. While originally a theoretical construct [27]- [33], there is evidence to suggest that ideal memristors also occur in quantum models [34].…”

Section: Introductionmentioning

confidence: 99%

Classical and quantum stochastic models of resistive and memristive circuits

Gough

2017

Journal of Mathematical Physics

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The purpose of this paper is to examine stochastic Markovian models for circuits in phase space for which the drift term is equivalent to the standard circuit equations. In particular we include dissipative components corresponding to both a resistor and a memristor in series. We obtain a dilation of the problem for which is canonical in the sense that the underlying Poisson Brackets structure is preserved under the stochastic flow. We do this first of all for standard Wiener noise, but also treat the problem using a new concept of symplectic noise where the Poisson structure is extended to the noise as well as the circuit variables, and in particular where we have canonically conjugate noises. Finally we construct a dilation which describes the quantum mechanical analogue.

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A Universal Mutator for Transformations Among Memristor, Memcapacitor, and Meminductor

Cited by 77 publications

References 30 publications

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

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

Emulation of Bio-Inspired Networks

Classical and quantum stochastic models of resistive and memristive circuits

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