ArticleThe journal of physical chemistry letters2021
A Frequency Domain Analysis of the Excitability and Bifurcations of the FitzHugh-Nagumo Neuron Model.
Article in The journal of physical chemistry letters, 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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6 citing papers in PubMed.
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Bifurcation and Frequency Properties of S-Type Neuronic Oscillators.The journal of physical chemistry letters · 2025Review
- Ultra robust negative differential resistance memristor for hardware neuron circuit implementation.Nature communications · 2025Article
- Electrical Charge Coupling Dominates the Hysteresis Effect of Halide Perovskite Devices.The journal of physical chemistry letters · 2023Article
- Dynamic Instability and Time Domain Response of a Model Halide Perovskite Memristor for Artificial Neurons.The journal of physical chemistry letters · 2022Article
- Chemical Inductor.Journal of the American Chemical Society · 2022Article
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Abstract
The dynamics of neurons consist of oscillating patterns of a membrane potential that underpin the operation of biological intelligence. The FitzHugh-Nagumo (FHN) model for neuron excitability generates rich dynamical regimes with a simpler mathematical structure than the Hodgkin-Huxley model. Because neurons can be understood in terms of electrical and electrochemical methods, here we apply the analysis of the impedance response to obtain the characteristic spectra and their evolution as a function of applied voltage. We convert the two nonlinear differential equations of FHN into an equivalent circuit model, classify the different impedance spectra, and calculate the corresponding trajectories in the phase plane of the variables. In analogy to the field of electrochemical oscillators, impedance spectroscopy detects the Hopf bifurcations and the spiking regimes. We show that a neuron element needs three essential internal components: capacitor, inductor, and negative differential resistance. The method supports the fabrication of memristor-based artificial neural networks.
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