ArticleFrontiers in neuroscience2021
Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics.Nature communications · 2025Article
- Low-energy and tunable LIF neuron using SiGe bandgap-engineered resistive switching transistor.Discover nano · 2024Article
- A bioinspired configurable cochlea based on memristors.Frontiers in neuroscience · 2022Article
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14 authors.
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Abstract
Inspired by the human brain, the spike-based neuromorphic system has attracted strong research enthusiasm because of the high energy efficiency and powerful computational capability, in which the spiking neurons and plastic synapses are two fundamental building blocks. Recently, two-terminal threshold switching (TS) devices have been regarded as promising candidates for building spiking neurons in hardware. However, how circuit parameters affect the spiking behavior of TS-based neurons is still an open question. Here, based on a leaky integrate-and-fire (LIF) neuron circuit, we systematically study the effect of both the extrinsic and intrinsic factors of NbO
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