Capacitive effects can make memristors chaotic

Гусейнов, Д. В.; Matyushkin, I. V.; Chernyaev, N. V.; Mikhaylov, A. N.; Pershin, Yuriy V.

doi:10.1016/j.chaos.2021.110699

Cited by 22 publications

(12 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Agudov et al [71] developed a more generic stochastic model of a memristive device that can be further used to adequately describe the observed complex dynamics of the proposed memristive interface. Another option is to use the deterministic, but at the same time higher-order memristor models based on two or more state variables in order to simulate the experimentally observed intermittency route to chaos [72].…”

Section: Resultsmentioning

confidence: 99%

Stochastic Memristive Interface for Neural Signal Processing

Gerasimova

Belov

Королев

et al. 2021

Sensors

View full text Add to dashboard Cite

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Stochastic Memristive Interface for Neural Signal Processing

Gerasimova

Belov

Королев

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…It is important to note that memristor model including tanh-nonlinearity is far from the only smooth memristor model. There are many other smooth models describing various memristor properties [19][20][21][22][23][24][25] .…”

Section: Models and Methodsmentioning

confidence: 99%

Subcritical Andronov-Hopf scenario for systems with a line of equilibria

Korneev,

Slepnev,

Vadivasova

et al. 2021

Preprint

View full text Add to dashboard Cite

Hard self-oscillation excitation in systems with infinitely many equilibrium points forming a line of equilibria in the phase space is analyzed. The demonstrated bifurcation phenomena correspond to a self-oscillation excitation mechanism being equivalent to the excitation scenario via the subcritical Andronov-Hopf bifurcation observed in classical self-oscillators with isolated equilibrium points. Hysteresis and bistability accompanying the discussed processes are shown and explained. The study is carried out on an example of a nonlinear memristor-based self-oscillator. First, a simpler model including Chua's memristor with a piecewise-smooth characteristic is explored by means of numerical modelling and analytical solution. Then the memristor characteristic is changed to a function being smooth everywhere in order to demonstrate that the observed effects are not associated with the smoothness of the memristor model. Finally, the action of the memristor forgetting effect is taken into consideration.

show abstract

“…In the equation (10), 𝑚𝑚 is the pulse number, 𝐺𝐺 𝑚𝑚 is the measured conductivity at step 𝑚𝑚, and Δ𝐺𝐺 is a conductivity change with respect to the previous pulse.…”

Section: Analytical Approaches and Vac Analysis By Partsmentioning

confidence: 99%

“…Depending on the type of memristive element used, the final structure inherits its main flaws, mostly related to the random nature of the processes occurring inside the device [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Research and Development of Parameter Extraction Approaches for Memristor Models

et al. 2021

View full text Add to dashboard Cite

Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often determined by the accuracy of their parameter extraction from experiment results. In this paper, a review of existing extraction methods was performed and new parameter extraction algorithms for an adaptive compact model were proposed. The effectiveness of the developed methods was confirmed for the volt-ampere characteristic of a memristor with a vertical structure: TiN/HfxAl1-xOy/HfO2/TiN.

show abstract

Capacitive effects can make memristors chaotic

Cited by 22 publications

References 41 publications

Stochastic Memristive Interface for Neural Signal Processing

Stochastic Memristive Interface for Neural Signal Processing

Subcritical Andronov-Hopf scenario for systems with a line of equilibria

Research and Development of Parameter Extraction Approaches for Memristor Models

Contact Info

Product

Resources

About