Boosting neural Chaos via memcapacitive electromagnetic radiation in a unidirectional ring neural network

Fan Shi, Yinghong Cao, Xianying Xu, Santo Banerjee and Jun Mou

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: Evidence has revealed that unidirectional ring neural networks (URNN) cannot generate chaotic behavior through parameter adjustment. This paper proposes a new threshold-type memcapacitor and applies it as electromagnetic radiation in a URNN derived from a Hopfield neural network (HNN), thereby constructing a memcapacitive unidirectional ring neural network (MURNN). Numerical simulation results reveal that applying memcapacitive electromagnetic radiation to URNNs can enhance the chaotic complexity of the network. In addition, through this method, MURNN can change the spike firing interval by adjusting parameters, achieving a bionic firing mode conversion like biological neurons, and the amplitude of the attractor is also controllable under parameter adjustment. Notably, MURNN can change the symmetry of hidden attractors by changing the initial conditions, which is a very rare phenomenon. Subsequently, the validation of the numerical simulation results is implemented through two different software and hardware methods, the DSP and Multisim circuits, further proving the feasibility of this method. Finally, the practical application of the system is explored, and a novel pseudorandom number generator based on MURNN is designed. Performance analysis demonstrates its high randomness.

Keywords: Neural network; Memcapacitor; Multistability; Firing patterns; Circuit and DSP implementation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014481

DOI: 10.1016/j.chaos.2025.117435

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