A device harnessing capacitive-coupled memristive effects for leaky integrate-and-fire neuron and electrocardiogram signal recognition implementation

Artificial neurons are essential for constructing neuromorphic networks, enabling information computation with high parallelism and efficiency akin to the human brain. Nonetheless, artificial synapses fabricated from conventional silicon-based materials exhibit limited adaptability in complex biological environments, making it challenging for them to replicate the intricate synaptic plasticity of biological synapses. In this work, Pt/La0.5Sr0.5CoO3(LSCO)/BST:CeO2/LSCO parallel-plate capacitor structure devices with capacitive-coupled memristive effects were fabricated. The current−voltage curves of the device under different voltages were investigated, revealing a remarkable evolution of the device from capacitive-dominated responses to pure memory behaviors. The pure memristive state of the device was utilized to simulate biological synaptic functions, while the capacitive-coupled memristive effects serve as the linchpin for emulating biological neuronal functionalities. By integrating these devices into bespoke circuits, leaky integrate-and-fire (LIF) functions are faithfully replicated, encompassing the precise mimicry of leakage, spatiotemporal integration, and firing processes. Moreover, a spiking neural network (SNN) based on the devices was constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 91%. The research findings thus herald a significant stride in neuromorphic computing, proffering a potent avenue for architecting brain-inspired computational frameworks.