Heterogeneous neural network based on locally active memristor with multiple firing patterns

With the development of information technology and neurobiology, there is an urgent need for a memory element with bionic properties to simulate the interactions among neurons. On this basis, a novel locally active memristor (LAM) model is designed, of which the memory properties are utilized to construct a coupled system of three-dimensional Hopfield neural network (HNN) and Hindmarsh–Rose (HR) neurons to simulate neuronal activities. First, the nonvolatility and local activity of the memristor is verified by the power-off plot (POP) and its direct current (DC) $V$ - $I$ plot. The device exhibits typical bistable resistance-switching behavior, maintaining two stable resistance states upon power-off, which confirms its non-volatile memory characteristics. A significant negative differential resistance (NDR) region observed in DC $V$ - $I$ curves directly verifies its local activity, indicating potential for active signal processing. Second, the complex dynamical behavior of HNN-HR is probed by numerical simulation, adjusting the coupling strength, synaptic weights and HR neuron parameters to demonstrate the bionic properties. The research results show that not only are multiple hidden attractor structures exhibited by the model, but also typical nonlinear phenomena such as transient chaos and intermittent chaos can be reproduced by it, and the dynamic transition between different chaotic firing modes can be realized. In addition, the phenomena of multi- state coexisting attractors and the expansion and migration of attractor topological structures are observed in the model. Finally, by means of the TMS320F28335 digital signal processing (DSP) platform, through the system architecture featuring the MAX3232 communication interface for interaction with the computer and the DAC8552 D/A converter for output to the oscilloscope, the generation of attractors of the HNN-HR model is achieved, and the feasibility of its application in digital circuits is verified. The construction of neural networks by simulating biological synapses through memristors offers a promising avenue for exploring brain function and its bionics.