A novel neural networks with memristor coupled memcapacitor-synapse neuron

With the increased understanding of information transfer and interactions between neurons, there is an urgent need for a memory element with bionic properties to probe the activity between neurons. Based on this, this paper constructs a novel Memristor Coupled Memcapacitor Synapse Hopfield Neural (MCMSHN) network by creating an element with a memristor coupled memcapacitor and applying it to a Hopfield neural network to simulate synaptic function. Firstly, the memory properties possessed by the Memristor Coupled Memcapacitor Synapse (MCMS) are demonstrated. Secondly, the complex dynamic behavior of MCMSHN is explored by means of numerical simulations to demonstrate its bionic properties. And the study focuses on the dynamical behavior of the synaptic weights and the coupling strengths, including multiple bifurcation behaviors, bionic discharges, and extreme multistability features of the MCMSHN. Finally, the attractors generated by the system are realized by Digital Signal Processing (DSP) techniques. The feasibility of MCMS for estimating synaptic activity is verified from multiple perspectives, providing insights into the complex working mechanisms of the brain.