Dynamic behaviors of hyperbolic-type memristor-based Hopfield neural network considering synaptic crosstalk.

Journal: Chaos (Woodbury, N.Y.)
Published Date: Mar 1, 2020

Abstract

Crosstalk phenomena taking place between synapses can influence signal transmission and, in some cases, brain functions. It is thus important to discover the dynamic behaviors of the neural network infected by synaptic crosstalk. To achieve this, in this paper, a new circuit is structured to emulate the Coupled Hyperbolic Memristors, which is then utilized to simulate the synaptic crosstalk of a Hopfield Neural Network (HNN). Thereafter, the HNN's multi-stability, asymmetry attractors, and anti-monotonicity are observed with various crosstalk strengths. The dynamic behaviors of the HNN are presented using bifurcation diagrams, dynamic maps, and Lyapunov exponent spectrums, considering different levels of crosstalk strengths. Simulation results also reveal that different crosstalk strengths can lead to wide-ranging nonlinear behaviors in the HNN systems.

Authors

  • Yang Leng
    School of Electrical and Power Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Dongsheng Yu
    School of Electrical and Power Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Yihua Hu
    School of Electrical and Power Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, Jiangsu 221116, China.
  • Samson Shenglong Yu
    School of Engineering, Deakin University, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia.
  • Zongbin Ye
    School of Electrical and Power Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, Jiangsu 221116, China.

Keywords

External Resources

Popular Topics
Recent Journals