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This paper presents a novel memristive synapse-coupled ring neural network (MSCRNN) through introducing a nonvolatile memristor into a three-neuron Hopfield neural network connected by a unidirectional ring topology. Complex dynamics relying on control parameters and initial states is thoroughly explored using numerical analysis techniques. Numerical analyses show that the MSCRNN not only exhibits bistability, tristability, but also in particular evolves an intriguing phenomenon known as homogeneous multistability, characterized by the emergence of an infinite number of homogeneous coexisting attractors triggered by the memristor initial states. In addition, a hardware test platform based on the CH32 microcontroller is built to experimentally validate these numerical findings. Finally, a new pseudorandom number generator is developed taking advantage of memristor initial-regulated chaotic sequences derived from the MSCRNN. Performance analysis outcomes indicate that these chaotic sequences possess the capability to generate pseudorandom numbers demonstrating exceptional randomness, rendering them highly advantageous for utilization in various chaos-based engineering applications. • A new memristor model is used to emulate synaptic plasticity among the ring neural network. • The MSCRNN exhibits an infinite number of homogeneous coexisting attractors. • A pseudorandom number generator is developed utilizing the chaotic sequences. [ABSTRACT FROM AUTHOR]

Image encryption, as a critical branch, has attracted increasing attention to the demand for information security specifically in the era of artificial intelligence (AI). A chaotic sequence is regarded as an important encryption source, and compressive sensing provides an effective technology for obtaining and reconstructing sparse or compressible signals in applied electronic engineering. In this work, the detouring matching pursuit algorithm and DNA coding are utilized to increase the performance based on a newly developed chaotic firing neuron. A memristor as the electromagnetic component is proven to enhance synaptic plasticity and emulate the synaptic connections in the brain. A unique discrete memristive neuron is derived for exploring the dynamics of neuron firing. By modifying the feedback from the memristor various coexisting neuronal chaotic firing are possessed. Because of the periodic evolution of the resistor from the memristor, the derived memristive Rulkov neuron exhibits coexisting homogeneous and heterogeneous multistability, which enables amplitude controllability and different types of coexisting chaotic firings. Circuit implementation based on CH32 is built to verify the controllability of the coexisting dynamics. • Cross-research of encryption and neuroscience is explored. • DMP is applied for the reconstruction of sparse signals. • Memristive Rulkov neuron is constructed to exhibit firing signals. • Homogeneous and heterogeneous multistability are captured. • The circuit based on CH32 is built to verify theory. [ABSTRACT FROM AUTHOR]

By transforming the past states of a variable to another variable, and returning the evolvement along with various functions of self-feedback, a unique gallery of chaotic maps with coexisting hollow attractors is proposed. Strikingly, these peculiar attractors stand on steep parameter intervals, some of which could only happen when the parameter accurately approaches the integer 1 instead of 1. Some maps also have direct amplitude controllers, which regulate the dynamics together with the initial condition leaving unpredicted complexity. The nested hollow attractors are arranged in phase space with great distribution diversity in the form of homogeneous and heterogeneous multistability. CH32-based digital circuit experiments show agreement with numerical simulation and theoretical prediction. Furthermore, the application in secure optical communication is explored accordingly proving higher performance in the steep parameter interval. [ABSTRACT FROM AUTHOR]

When both an absolute value function and a cosine function are introduced for chaotic sequence generation, coexisting attractors with different polarities and locations could be extracted by the offset boosting of the initial condition, and thus a hyperchaotic map with distance-increasing coexisting attractors is constructed. It is found that this map has two controllers for rescaling the oscillation totally and partially. The digital circuit implementation shows good consistency with numerical simulation. Moreover, the parameter adaptation in the basic Pelican Optimization Algorithm (POA) is improved by the above-mentioned hyperchaotic map. Correspondingly, the Hyperchaotic Pelican Optimization Algorithm (HCPOA) is constructed, where the hyperchaotic sequences are employed as the sampling pool when random numbers are required in the basic POA. Furthermore, the impact of chaos regulation on the performance of HCPOA is investigated, and the indicators of HCPOA are analyzed by employing five benchmark functions, where the single attractor shows better performance than the double-cavity attractor. The methods of HCPOA proposed in this paper bring the chance to improve the quality of solutions and the ability to escape local optima. • The initially-controlled offset boosting gives birth to various regimes of double-cavity hyperchaotic attractors. And two control knobs are extracted for amplitude rescaling, including total and partial controllers. • An improved HyperChaotic Pelican Optimization Algorithm (HCPOA) is firstly developed by employing the hyperchaotic map, and the performance influenced by the chaotic property is exhaustively explored. • The performance criteria of HCPOA with various chaotic sequences associated with five benchmark functions are compared, indicating that the solution performance of the proposed algorithm can be modified flexibly by different initial conditions and amplitude. [ABSTRACT FROM AUTHOR]