A memristive autapse-synapse neural network: application to image encryption

With the advent of the physical memristor, various memristive neural network models have been designed and analyzed to mimic some human brain functions. However, there is a realistic issue because many works reported the coupling of neuron models using either memristive synapse or memristive autapse, whereas in the real brain, a neuron can interact with both another neuron (memristive synapse) and with itself (memristive autapse). Two main ideas are developed in this work. First, we investigate the dynamics of two different neurons coupled via memristive synapse and memristive autapse. The analyses indicate that the global dynamics of this highly relies on the neuron's coupling strength. Second, a cryptographic scheme based on both S-Box driven block compressive sensing and the memristive autapse synapse model is proposed. Performance analyses indicate that the coupling strength of the proposed neural network model can be adjusted to increase or decrease the security of medical data.

Automated summary

This paper investigates the dynamics of two different neurons coupled via memristive synapse and memristive autapse. The results suggest that the global dynamics of the system highly depends on the coupling strength. A cryptographic scheme based on both S-Box driven block compressive sensing and the memristive autapse synapse model is proposed, and performance analysis indicates that the coupling strength of the neural network model can be adjusted to increase or decrease the security of medical data.