Resonate-and-fire neurons

By Eugene M. Izhikevich. Neural Networks, 2001

485 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5878ae06e8f6e4c723b70721c3fd2c0fd6761fc9

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In Cognitive Neuroscience

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Pattern recognition via synchronization in phase-locked loop neural networks (2000, background): “Such a dependence is the key issue of the frequency-modulated (FM) interaction theory ([6]–[9], [13]–[15]), which shows that oscillatory networks can have other neurocomputational properties far exceeding those of Hopfield–Grossberg networks.”
Neural excitability, Spiking and bursting (2000, background): “Multiplexing of neural signals via doublets: Resonate-and-fire neurons having equal eigenfrequencies can interact selectively without any cross interference with other resonate-and-fire neurons (from [Izhikevich, 2001]).”
Which model to use for cortical spiking neurons? (2004, background): “Resonate-and-Fire The resonate-and-fire neuron [16] is a two-dimensional analogue of the I&F neuron z′ = I + (b + ıω)z , if Im z = athresh, thenz ← z0(z)”
Bursts as a unit of neural information: selective communication via resonance. (2003, methodology): “resonant doublets). The same idea is illustrated in the bottom of Fig. Ib using phase-plane analysis of the ‘resonate-and-fire’ model [ 38 ].”
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection (2009, background): “Spike response models are phenomenological models that are simpler than the detailed models and offer a compromise between computational burden and electrophysiological detail (Ermentrout & Kopell, 1986; Gerstner, 1995; Gerstner & Kistler, 2002; Izhikevich, 2001; Kistler, Gerstner, & van Hemmen, 199”