期刊
CHINESE PHYSICS B
卷 31, 期 6, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1674-1056/ac539a
关键词
discrete memristor; Rulkov neuron; fractional-order difference; dynamics
资金
- Major Research Plan of the National Natural Science Foundation of China [91964108]
- National Natural Science Foundation of China [61971185]
- Natural Science Foundation of Hunan Province, China [2020JJ4218]
This study constructed a new fractional-order discrete memristor model with significant nonlinearity for simulating the dynamical behaviors of neurons under electromagnetic radiation. The characteristics of integer-order and fractional-order systems were analyzed, showing that the fractional-order system has richer dynamics.
The exploration of the memristor model in the discrete domain is a fascinating hotspot. The electromagnetic induction on neurons has also begun to be simulated by some discrete memristors. However, most of the current investigations are based on the integer-order discrete memristor, and there are relatively few studies on the form of fractional order. In this paper, a new fractional-order discrete memristor model with prominent nonlinearity is constructed based on the Caputo fractional-order difference operator. Furthermore, the dynamical behaviors of the Rulkov neuron under electromagnetic radiation are simulated by introducing the proposed discrete memristor. The integer-order and fractional-order peculiarities of the system are analyzed through the bifurcation graph, the Lyapunov exponential spectrum, and the iterative graph. The results demonstrate that the fractional-order system has more abundant dynamics than the integer one, such as hyper-chaos, multi-stable and transient chaos. In addition, the complexity of the system in the fractional form is evaluated by the means of the spectral entropy complexity algorithm and consequences show that it is affected by the order of the fractional system. The feature of fractional difference lays the foundation for further research and application of the discrete memristor and the neuron map in the future.
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