Two-neuron-based non-autonomous memristive Hopfield neural network: Numerical analyses and hardware experiments

This paper explores a two-neuron-based non-autonomous memristive Hopfield neural network (mHNN) through numerical analyses and hardware experiments. It is interested that the locus and stability of the AC equilibrium point for the mHNN change with the time evolution. Dynamical behaviors associated with the self-coupling strength of the memristive synapse are numerically investigated by bifurcation diagrams, Lyapunov exponents and phase portraits. Particularly, bursting behaviors are revealed when the order gap exists between the natural frequency and external stimulus frequency. The interesting phenomena are illustrated through phase portraits, transmitted phase portraits, and time-domain waveforms of two cases. Moreover, breadboard experimental investigations are carried out, which effectively verify the numerical simulations. (C) 2018 Elsevier GmbH. All rights reserved.