The Rich Dynamics of Memristive Devices With Non-Separable Nonlinear Response

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

doi:10.1109/tcsii.2021.3115111

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Equations ( 1)-( 5) describe a second-order voltagecontrolled memristive system [1]. According to a recently proposed classification scheme [34], the system has a linear response. Figure 2 shows simulated I − V curves for 20 cycles of the triangular voltage sweep of f = 50 Hz frequency and quadruple that for the second-order memristor model with arbitrary parameters.…”

Section: Further Discussionmentioning

confidence: 99%

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Mishchenko¹,

Bolshakov²,

Lukoyanov³

et al. 2022

J. Phys. D: Appl. Phys.

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We develop a model of Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti memristive devices, and demonstrate, both experimentally and numerically, an inverted spike-rate-dependent plasticity effect. The effect consists in the reduction of the learning rate with the increase in frequency of spikes generated by the phase-locked loop neuron. The memristor model uses two internal state variables representing the number of complete filaments and concentration of charged traps. While the former state variable defines the device resistance and is associated with the distribution of oxygen vacancies, the latter affects the internal electric field and modulates the migration of vacancies. Several neural circuit configurations that include pairs and populations of memristively coupled neurons are analyzed numerically. The results of this study may contribute to the development of large-scale self-organized artificial cognitive systems based on neural synchrony.

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Section: Further Discussionmentioning

confidence: 99%

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Mishchenko¹,

Bolshakov²,

Lukoyanov³

et al. 2022

J. Phys. D: Appl. Phys.

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“…Various abstract nonlinear and nonseparable functions (11) and ( 13) were considered in ref. [200]. However, in the general case, these functions should be determined by specific mechanisms and parameters of electron transport in the corresponding memristor states [201] (see also Equations ( 6)-( 8) in Section 3.2).…”

Section: Selection Of Adequate Variables To Describe Memristor Stocha...mentioning

confidence: 99%

Variability in Resistive Memories

Roldán

Miranda

Maldonado

et al. 2023

Advanced Intelligent Systems

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Research and development efforts in the nonvolatile memory arena are focused on a reduced set of innovative components, among which we can include memristors. [1,2] Memristors are expected to be key players in the electronics landscape of the coming years largely because of the powerful applications that stand upon their unique features. [1,[3][4][5] The switching mechanisms behind memristors differ significantly depending on the physical properties of the structures and the materials involved. [1,[3][4][5][6][7] To list some of these mechanisms, we can highlight those devices based on phase-change materials, which can be switched reversibly between amorphous and crystalline phases with different electrical resistivity (phasechange memories, PCMs); [8] devices that take advantage of the magnetic and electrical properties exhibited by some materials with different architectures (magnetic RAMs, MRAMs); [9] also structures where materials with switchable electrical polarization give rise to hysteresis curves of the polarization versus electrical field that can be engineered for storing information (ferroelectric FET, FFET); [10] and, finally, resistive RAMs (RRAMs) where the dielectric conduction properties are altered by means of the internal ion movement and concurrent redox reactions used to generate different resistive states. [1,3,11,12]

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“…The multidimensionality of this space, combined with nonlinear and nonseparable memristance, provides the necessary and sufficient conditions for observing the complex dynamics of the memristor response to external periodic stimulation ( Guseinov et al, 2021a ). The corresponding transition from periodic response modes to intermittency and chaos can partially explain the variability in the parameters of real memristive devices ( Guseinov et al, 2021b ).…”

Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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The Rich Dynamics of Memristive Devices With Non-Separable Nonlinear Response

Cited by 6 publications

References 24 publications

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Inverted spike-rate-dependent plasticity due to charge traps in a metal-oxide memristive device

Variability in Resistive Memories

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

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