ArticleFrontiers in neuroscience2022
Memristive LIF Spiking Neuron Model and Its Application in Morse Code.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Real-Time Unsupervised Learning and Image Recognition via Memristive Neural Integrated Chip Based on Negative Differential Resistance of Electrochemical Metallization Cell Neuron Device.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.Cognitive neurodynamics · 2024Article
- Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks.Scientific reports · 2023Article
- SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.Frontiers in neuroscience · 2023Article
- Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch.Entropy (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
The leaky integrate-and-fire (LIF) spiking model can successively mimic the firing patterns and information propagation of a biological neuron. It has been applied in neural networks, cognitive computing, and brain-inspired computing. Due to the resistance variability and the natural storage capacity of the memristor, the LIF spiking model with a memristor (MLIF) is presented in this article to simulate the function and working mode of neurons in biological systems. First, the comparison between the MLIF spiking model and the LIF spiking model is conducted. Second, it is experimentally shown that a single memristor could mimic the function of the integration and filtering of the dendrite and emulate the function of the integration and firing of the soma. Finally, the feasibility of the proposed MLIF spiking model is verified by the generation and recognition of Morse code. The experimental results indicate that the presented MLIF model efficiently performs good biological frequency adaptation, high firing frequency, and rich spiking patterns. A memristor can be used as the dendrite and the soma, and the MLIF spiking model can emulate the axon. The constructed single neuron can efficiently complete the generation and propagation of firing patterns.
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