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Related papers: Memristors for the Curious Outsiders

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This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable…

Emerging Technologies · Computer Science 2012-12-17 Gerard Howard , Ella Gale , Larry Bull , Ben de Lacy Costello , Andy Adamatzky

Memristors can mimic the functions of biological synapse, where it can simultaneously store the synaptic weight and modulate the transmitted signal. Here, we report Nb/Nb2O5/Pt based memristors with bipolar resistive switching, exhibiting…

Applied Physics · Physics 2019-10-02 Sweety Deswal , Ashok Kumar , Ajeet Kumar

It is now widely accepted that memristive devices are perfect candidates for the emulation of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive…

Neural and Evolutionary Computing · Computer Science 2012-11-26 Farnood Merrikh-Bayat , Saeed Bagheri Shouraki , Iman Esmaili Paeen Afrakoti

Advances in materials science have led to physical instantiations of self-assembled networks of memristive devices and demonstrations of their computational capability through reservoir computing. Reservoir computing is an approach that…

Emerging Technologies · Computer Science 2015-04-28 Jens Bürger , Alireza Goudarzi , Darko Stefanovic , Christof Teuscher

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes.…

Mesoscale and Nanoscale Physics · Physics 2012-07-04 Yuriy V. Pershin , Massimiliano Di Ventra

In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for extreme-edge applications with high uncertainty, access to…

Comprehensive understanding of the world's most energy efficient powerful computer, the human brain, is an elusive scientific issue. Still, already gained knowledge indicates memristors can be used as a building block to model the brain. At…

Applied Physics · Physics 2017-06-20 Mohamed T. Ghoneim , Muhammad M. Hussain

While two-terminal HfOX (x<2) memristor devices have been studied for ion transport and current evolution, there have been limited reports on the effect of the long-range thermal environment on their performance. In this work,…

Some memristors are quite interesting from the point of view of dynamical systems. When driven by narrow pulses of alternating polarities, their dynamics has a stable fixed point, which may be useful for future applications. We study the…

Emerging Technologies · Computer Science 2019-06-26 V. A. Slipko , Y. V. Pershin

A generalized approach for the implementation of memristive two-terminal circuits with piesewise-smooth characteristics is proposed on the example of a multifunctional circuit based on a transistor switch. Two versions of the circuit are…

Adaptation and Self-Organizing Systems · Physics 2022-10-06 Vladimir V. Semenov

This paper examines the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in memristive devices, focusing on ReRAM devices, specifically the interface-type or non-filamentary analog switching devices. A…

Mesoscale and Nanoscale Physics · Physics 2024-01-30 Sahitya Yarragolla , Torben Hemke , Jan Trieschmann , Thomas Mussenbrock

We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the…

Emerging Technologies · Computer Science 2015-07-09 Yuriy V. Pershin , Fabio L. Traversa , Massimiliano Di Ventra

We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model predicts most accurately I-V relation at small non-disturbing…

Materials Science · Physics 2015-06-22 Farnood Merrikh-Bayat , Brian Hoskins , Dmitri B. Strukov

Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through…

Originally studied for their suitability to store information compactly, memristive networks are now being analysed as implementations of neuromorphic circuits. An extremely high number of elements is thus mandatory. To surpass the limited…

Emerging Technologies · Computer Science 2024-01-18 Fabrizio Di Francesco , Gabriel A. Sanca , Cynthia P. Quinteros

Memristive devices whose resistance can be hysteretically switched by electric field or current are intensely pursued both for fundamental interest as well as potential applications in neuromorphic computing and phase-change memory. When…

We introduce a Lyapunov function for the dynamics of memristive circuits, and compare the effectiveness of memristors in minimizing the function to widely used optimization software. We study in particular three classes of problems which…

Adaptation and Self-Organizing Systems · Physics 2021-09-10 Forrest C. Sheldon , Francesco Caravelli , Carleton Coffrin

This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led…

Materials Science · Physics 2015-06-24 Roberto Marani , Gennaro Gelao , Anna Gina Perri

We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors…

Emerging Technologies · Computer Science 2023-10-03 V. A. Slipko , Y. V. Pershin

Adaptive response to a varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. Here, we consider memory models…

Cell Behavior · Quantitative Biology 2013-10-29 Fabio Lorenzo Traversa , Yuriy V. Pershin , Massimiliano Di Ventra
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