Bistable discrete memristor-based hyperchaotic neuron model and hardware implementation for image encryption

Jin Li, Sen Zhang, Ziru Wang, Siyao Lu and Xin Ding

Chaos, Solitons & Fractals, 2026, vol. 210, issue P2

Abstract: Locally active memristors (LAMs) introduce history-dependent nonlinear feedback into neuron models, enabling richer firing dynamics and providing a useful mechanism for neuromorphic computing. In this paper, a bistable discrete memristor-based hyperchaotic neuron model (BDMHNM) is proposed by incorporating a discrete locally active memristor (DLAM) with hyperbolic tangent nonlinearity and sinusoidal memductance into an Integrate-and-Fire (IF) neuron model. Analytical results and simulations verify that the proposed DLAM retains nonzero states after power-off and contains locally active operating intervals. These two features make the neuron map capable of resting, periodic spiking, multi-periodic bursting, hyperchaotic spiking, and chaotic bursting. The map also shows clear dependence on parameters and initial states, including heterogeneous coexistence and abrupt firing-to-resting transitions. The BDMHNM is then implemented on an FPGA platform, and the oscilloscope waveforms show good agreement with the simulated phase portraits and time series. Finally, the hyperchaotic firing sequences are used together with linear feedback shift register (LFSR) scrambling to build a hardware image-encryption system. The experiments confirm efficient FPGA encryption of standard color images.

Keywords: Locally active memristor; Firing pattern; Hyperchaos; Integrate-and-fire neuron; FPGA implementation; Image encryption (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:210:y:2026:i:p2:s096007792600860x

DOI: 10.1016/j.chaos.2026.118719

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