A device harnessing capacitive-coupled memristive effects for leaky integrate-and-fire neuron and electrocardiogram signal recognition implementation
Year: 2025
Type: article
Abstract: 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.
Source: Frontiers of Physics
Authors: Jingjuan Wang, Biao Yang, Hui He, Changliang Li, Xiaobing Yan
Institutions: Hebei GEO University, Hebei University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415505045
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