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The synapse-like properties of memristors make them a popular choice for neuromorphic circuits, particularly in neural networks. However, most existing neural networks capable of generating complex dynamics are continuous-time models with high dimensionality, resulting in significant resource overhead for digital implementation. This study incorporates a memristor with an internal multisegment state function into two-neuron Hopfield neural network (HNN), thereby constructing a novel discrete-time memristive two-neuron HNN (DMT-HNN) capable of emerging complex multi-stripe/wave hyperchaos. DMT-HNN generates multistripe and multiwave hyperchaotic attractors, with the number of stripes/waves continuously expanding as the segment number of the memristor state function increases. By decreasing the memristor scaling factor, the multi-stripe/wave hyperchaotic attractors can be decomposed into varying numbers of coexisting hyperchaotic attractors. An FPGA hardware setup is designed based on DMT-HNN, and the multi-stripe/wave hyperchaotic attractors are successfully captured on an oscilloscope. Besides, hardware pseudorandom level generators are fabricated, and the results are confirmed by NIST randomness tests.
Bao et al. (Mon,) studied this question.