An Active Pulse Transmission Line Simulating Nerve Axon

By J. Nagumo, S. Arimoto, S. Yoshizawa. 1962

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “Current GPUs, however, are little ovens, much hungrier for energy than biological brains, whose neurons efficiently communicate by brief spikes (Hodgkin and Huxley, 1952; FitzHugh, 1961; Nagumo et al., 1962), and often remain quiet.”
Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, unknown): “The choice of this model is motivated by various reasons, but foremost by the fact that the collection of dynamic behaviors of neurons and populations thereof can be described by a two-dimensional (2D) dynamic system (FitzHugh, 1961; Nagumo, 1962; Wilson and Cowan, 1972; Wong and Wang, 2006).”
Differential Equations, Dynamical Systems, and an Introduction to Chaos (2003, methodology): “[35], who produced a simpler model of the Hodgkin-Huxley model.”
Nonlinear dynamics of networks: the groupoid formalism (2006, methodology): “To provide a concrete example of dynamics in a symmetric network , we consider a specific system of ODEs determined by the two-cell network of Figure 1, based on the FitzHugh-Nagumo model of a neuron (FitzHugh [28], Nagumo et al. [61]).”
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems (2019, methodology): “The FitzHugh-Nagumo system was first proposed in 1961 [30] and then 244 realized as a circuit for electronic pulse transmission in [31].”
Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters (2013, background): “A well known example for the case where the system is not linear in the parameters but separability of the states and parameters is still possible, is the FitzHugh-Nagumo system in neurophysiology ([17, 35]).”
Spatiotemporal order out of noise (2007, methodology): “Nagumo et al., 1962", arrays of noise-driven electronic circuits have been used to demonstrate experimentally the existence of spatiotemporal stochastic resonance !”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “The FHN model, a simplified version of the biophysically detailed Hodgkin-Huxley model [250], is a mathematical paradigm for gaining significant insights into a variety of dynamical behaviors in real neuronal systems [251, 252].”
Fourier spectral methods for fractional-in-space reaction-diffusion equations (2014, unknown): “FitzHugh–Nagumo model – Excitable media The FitzHugh Nagumo model represents one of the simplest models for the study of excitable media [29, 30].”