Dynamic analysis and DNA coding-based image encryption of memristor synapse-coupled hyperchaotic IN-HNN network

The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods. To this end, the paper proposes a novel high-dimensional memristor synapse-coupled hyperchaotic neural network by using the designed memristor as the synapse to connect an inertial neuron (IN) and a Hopfield neural network (HNN). By using numerical tools including bifurcation plots, phase plots, and basins of attraction, it is found that the dynamics of this system are closely related to the memristor coupling strength, self-connection synaptic weights, and inter-connection synaptic weights, and it can exhibit excellent hyperchaotic behaviors and coexisting multi-stable patterns. Through PSIM circuit simulations, the complex dynamics of the coupled IN-HNN system are verified. Furthermore, a DNA-encoded encryption algorithm is given, which utilizes generated hyperchaotic sequences to achieve encoding, operation, and decoding of DNA. The results show that this algorithm possesses strong robustness against statistical attacks, differential attacks, and noise interference, and can effectively resist known/selected plaintext attacks. This work will provide new ideas for the modeling of large-scale brain-like neural networks and high-security image encryption.