Neural excitability, Spiking and bursting

By Eugene M. Izhikevich. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 2000

1,908 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/335f05f4cd540ef255e88999ba07e99d6b9fb0e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Chaotic and periodic bursting phenomena in a memristive Wien-bridge oscillator (2016, methodology): “Bursting phenomenon is originally used to describe a type of neuron activities in biological neurons and endocrine cells, which appears when the oscillation alternates between the quiescent states and the repetitive spiking states [19].”
Codimension-two bursting analysis in the delayed neural system with external stimulations (2012, methodology): “As we know, in a system, when the parameters are slaved and varied periodically near the SNH singular point, the system dynamic behavior will exhibit different types of bursting [7, 31, 32].”
Synchronization in Networks of Excitatory and Inhibitory Neurons with Sparse, Random Connectivity (2003, methodology): “This is the model of section 2.1.5 of Izhikevich (2000) (see also Ermentrout, 1996, for a discussion of synapses between theta neurons). increasing near the ghost of the equilibrium point.”
Biological experimental demonstration of bifurcations from bursting to spiking predicted by theoretical models (2014, background): “gle neuron, such as the bursting and spiking patterns (detailed descriptions about the bursting and spiking are referred to Ref [12]) and bifurcations between the firing patterns of burstings or spikings in onedimensional or two-dimensional parameter spaces [13– 19].”
A Universal Model for Spike-Frequency Adaptation (2003, methodology): “The adaptation model introduced by Izhikevich (2000) is a specie c implementation of equation 6.5 for the µ neuron, which is up to a different scalingof variablesidentical to theexample 6.6.”
Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. (2003, background): “The voltage traces of these elliptic bursters show lowamplitude oscillations between bursts (Bertram et al. 1995; Izhikevich 2000; Wang and Rinzel 2003) that do not contribute to the number of maxima per burst if a threshold of 0 mV is applied.”
Bursts as a unit of neural information: selective communication via resonance. (2003, background): “However, theoretical studies and simulations [12, 13] have shown that the result”
On the Phase Reduction and Response Dynamics of Neural Oscillator Populations (2004, methodology): “…and Ermentrout (1994), Ermentrout (1996), Hoppensteadt and Izhikevich (1997), Kuramoto (1997), Kim and Lee (2000), Bressloff and Coombes (2000), Izhikevich (2000b), Brown, Holmes, and Moehlis (2003a), and Lewis and Rinzel (2003); see also the related “spike response method” in Gerstner, van…”
A model of periodic oscillation for genetic regulatory systems (2002, background): “This kind of limit cycles have also been observed in neuronal systems [16], [17] and other biological systems.”
Different types of bursting calcium oscillations in non-excitable cells (2003, methodology): “Therefore, the classification of bursting oscillations can have further subtypes according to these additional bifurcations (for review see [17]).”
Resonate-and-fire neurons (2001, background): “Such neurons are referred to as being resonators (Izhikevich, 2000; Llinas, 1988).”
On the nature of seizure dynamics. (2014, methodology): “The 16 different classes are summarized in Table 1, where we follow the naming scheme of Izhikevich (2000), in which each class is labelled according to the type of bifurcation of equilibria/ bifurcation of limit cycle.”
Which model to use for cortical spiking neurons? (2004, background): “The ability to fire low-frequency spikes when the input is weak (but superthreshold) is called Class 1 excitability [8], [17], [22].”