ArticleFrontiers in neuroscience2021
Memristive Hodgkin-Huxley Spiking Neuron Model for Reproducing Neuron Behaviors.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modeling characteristics of neuronal firing in the thalamocortical network of connections in control and parkinsonian primates.Journal of computational neuroscience · 2025Article
- Design of a Current-Mode OTA-Based Memristor Emulator for Neuromorphic Medical Application.Micromachines · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Electrical Impedance Spectroscopy with Bacterial Biofilms: Neuronal-like Behavior.Nano letters · 2024Article
- Spiking Neural Networks and Mathematical Models.Advances in experimental medicine and biology · 2023Review
- Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch.Entropy (Basel, Switzerland) · 2022Article
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3 authors.
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Abstract
The Hodgkin-Huxley (HH) spiking neuron model reproduces the dynamic characteristics of the neuron by mimicking the action potential, ionic channels, and spiking behaviors. The memristor is a nonlinear device with variable resistance. In this paper, the memristor is introduced to the HH spiking model, and the memristive Hodgkin-Huxley spiking neuron model (MHH) is presented. We experimentally compare the HH spiking model and the MHH spiking model by applying different stimuli. First, the individual current pulse is injected into the HH and MHH spiking models. The comparison between action potentials, current densities, and conductances is carried out. Second, the reverse single pulse stimulus and a series of pulse stimuli are applied to the two models. The effects of current density and action time on the production of the action potential are analyzed. Finally, the sinusoidal current stimulus acts on the two models. The various spiking behaviors are realized by adjusting the frequency of the sinusoidal stimulus. We experimentally demonstrate that the MHH spiking model generates more action potential than the HH spiking model and takes a short time to change the memductance. The reverse stimulus cannot activate the action potential in both models. The MHH spiking model performs smoother waveforms and a faster speed to return to the resting potential. The larger the external stimulus, the faster action potential generated, and the more noticeable change in conductances. Meanwhile, the MHH spiking model shows the various spiking patterns of neurons.
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