Novel memristor and memristor-based applications

Wang, Hengtong; Li, Chunlai; Banerjee, Santo; He, Songbai

doi:10.1140/epjs/s11734-022-00697-1

Cited by 9 publications

(1 citation statement)

References 25 publications

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“…Yu et al [50] considered a class of neurons by using memristors as electromagnetic radiation. Currently, most of the work is on the application of continuous type memristors to continuous neuron models [51], and it is a highly attractive work on how to apply discrete type memristors to discrete neuron models [51,52].…”

Section: Introductionmentioning

confidence: 99%

Hidden dynamics of memristor-coupled neurons with multi-stability and multi-transient hyperchaotic behavior

Ma,

Mou,

Al-Barakati

et al. 2023

Phys. Scr.

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In continuous neural network systems, considering the coupling state of the memristor has been widely studied. However, it is rarely mentioned in discrete neural network systems. A discrete memristor-coupled Rulkov neuron is constructed by coupling a discrete memristor to a discrete neural system in this paper. The rich and complex dynamical behavior of the discrete coupled neural network system is explored and analyzed, including hidden attractor, multi-stability, multi-transient, and hyperchaos. Neuron firing patterns were similarly explored. Implementation of the circuit and practical applications of the system are also considered. The experimental and numerical results demonstrate that the dynamics of various parameters in the map create a more complex and interesting discrete neuron system.

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Section: Introductionmentioning

confidence: 99%

Hidden dynamics of memristor-coupled neurons with multi-stability and multi-transient hyperchaotic behavior

Ma,

Mou,

Al-Barakati

et al. 2023

Phys. Scr.

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Learning Model Based on Electrochemical Metallization Memristor with Cluster Residual Effect

Sun,

Chen

2024

Physica Status Solidi (b)

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Although a memristor model, subjected to electrochemical metallization mechanism, has been proposed based on the spontaneous decay of clusters in the previous work, it does not agree with the human forgetting accurately. Therefore, an improved model is meaningfully presented for the memristor with the cluster spontaneous decay by adding the residual effect. The former is due to the inward contraction of atoms driven by surface energy, while the latter is because of the balance of attractive and repulsive forces between atoms. The model fits well with the actual device. The forgetting is caused by the spontaneous decay. Memory retention is generated due to the added effect, which is also the internal cause of good agreement with the actual forgetting. Additionally, short‐term plasticity is converted to long‐term plasticity through the repeated learning. The efficiency of experiential learning using this model is much higher than that using the previous. It is shown that the physical mechanism of spontaneous decay in the cluster‐based channel is different from that in vacancy‐based or atom‐based channel. The model working under a non‐ideal condition with the temperature influence is discussed. Potential applications based on the model are stated.

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Design of Hybrid CMOS-Memristor Combinational Circuits: Maximizing Efficiency with Low Power, Area, and Delay

Rasheed,

Nelapati

2024

Circuits Syst Signal Process

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Novel memristor and memristor-based applications

Cited by 9 publications

References 25 publications

Hidden dynamics of memristor-coupled neurons with multi-stability and multi-transient hyperchaotic behavior

Hidden dynamics of memristor-coupled neurons with multi-stability and multi-transient hyperchaotic behavior

Learning Model Based on Electrochemical Metallization Memristor with Cluster Residual Effect

Design of Hybrid CMOS-Memristor Combinational Circuits: Maximizing Efficiency with Low Power, Area, and Delay

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