Flexible cascade and parallel operations of discrete memristor

Yuan, Fang; Xing, Guibin; Deng, Yue

doi:10.1016/j.chaos.2022.112888

Cited by 25 publications

(6 citation statements)

References 25 publications

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“…[37][38][39][40][41][42] Therefore, discrete neuron models are more suitable for simulating large-scale neu-ral networks and conducting general research, such as generating chaotic trajectories, pattern recognition, and analyzing synchronized firing behaviors. [43][44][45][46][47][48][49][50][51] As a result, research on discrete neurons has become a hot topic in recent years. In discrete memristor-coupled neural networks, complex dynamical behaviors have been discovered, including coexisting attractors, [52][53][54][55][56] synchronization transitions, and synchronization coexistence.…”

Section: Introductionmentioning

confidence: 99%

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Ma 马,

Xiong 熊,

Li 李

et al. 2024

Chinese Phys. B

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Synaptic crosstalk is a prevalent phenomenon among neuronal synapses, playing a crucial role in the transmission of neural signals. Therefore, considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks is highly necessary. In this paper, we propose a heterogeneous discrete neural network (HDNN) consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete neuron. These two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is considered. The impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov exponents. It is observed that the HDNN exhibits different coexisting attractors under varying crosstalk strengths. Furthermore, the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated, revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

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Section: Introductionmentioning

confidence: 99%

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Ma 马,

Xiong 熊,

Li 李

et al. 2024

Chinese Phys. B

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“…For instance, Liu et al [30] proposed a modulo plus parallelism method to construct enhanced chaotic maps with uniform distribution through self-parallel and hybrid parallelism. Yuan et al [31] utilized a discrete memristor (DM) as a nonlinear component to construct a DM chaotic map by cascading and paralleling, and composited operations on the DM to form a new discrete memory system. Lai et al [32] introduced a novel neuron model with considerable chaotic properties and improved the chaotic complexity of discrete neurons by coupling a memristor to this neuron.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of multicavity hyperchaotic maps with rotational control operation and its applications

Zhu,

Sun,

et al. 2024

Phys. Scr.

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To provide more complex and random chaotic maps to applications and algorithms, we propose a boundary coupled modulation (BCM) model. By introducing the rotation-matrix (ROT), the corresponding rotation boundary coupled modulation (RBCM) model are constructed, and a series of hyperchaotic maps are generated with various attractors and numerous fixed points. The shape and size of the multicavity can be adjusted by controlling the parameters. Interestingly, RBCM maps are controlled by changing rotation coefficients (Rot-C, d, e, and θ), which can rotate the attractor of the enhanced BCM at any angle and direction. RBCM maps produce a more uniform topological space, and have multiple pairs of symmetric coexisting attractors. The BCM and RBCM maps exhibit rich dynamical behaviors, high complexity, and strong randomness.To verify the engineering practicability, we apply the BCM and RBCM maps to design pseudo-random number generators (PRNG), and test it with NIST, quadrature amplitude modulation (QAM) system. Finally, the FPGA implementation of the proposed chaotic map verifies.

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“…Yuan et al introduced various structures of memristive chaotic maps utilizing discrete memristors (DMs), which include cascades and parallel configurations of multiple DMs, as well as hybrid structures. Three chaotic maps employing DMs are proposed based on these structures, and the investigation of their dynamical behaviors revealed extreme multistability [20]. Extreme multistability is characterized by the exceptional scenario of the infinite coexistence of multiple attractors [21].…”

Section: Introductionmentioning

confidence: 99%

Dynamical analysis of a novel memristor-type chaotic map

Yu,

Abu Bakar,

et al. 2024

Phys. Scr.

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As a unique nonlinear component, the discrete memristor, with its simple structure, is associated with the ability to lead to excellent chaotic performance in the construction of chaotic systems. This characteristic has elevated the discrete memristor to a hot topic in the field of chaos. This paper introduces a cosine hyperchaotic map. Numerical simulations reveal its rich dynamical behaviors. The chaotic map exhibits diverse chaotic control models, including partial amplitude control, total amplitude control, initial boosting, and parameter-offset boosting, with dynamical distribution diagram plotted for amplitude control to quantify the range of amplitude modulation. Additionally, a localized boosting free region is identified, and dual offset parameters are introduced to control this localized boosting free region, enhancing the flexibility of the system. Finally, the map is implemented on STM32 to validate the numerical simulation results.

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Flexible cascade and parallel operations of discrete memristor

Cited by 25 publications

References 25 publications

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Dynamics of multicavity hyperchaotic maps with rotational control operation and its applications

Dynamical analysis of a novel memristor-type chaotic map

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