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A Contraction Argument for Two-Dimensional Spiking Neuron Models
Author(s) -
Eric Foxall,
Roderick Edwards,
Slim Ibrahim,
P. van den Driessche
Publication year - 2012
Publication title -
siam journal on applied dynamical systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.218
H-Index - 61
ISSN - 1536-0040
DOI - 10.1137/10081811x
Subject(s) - biological neuron model , computer science , variety (cybernetics) , statistical physics , contraction (grammar) , reset (finance) , stability (learning theory) , phase plane , equilibrium point , mathematics , artificial intelligence , mathematical analysis , physics , nonlinear system , artificial neural network , machine learning , quantum mechanics , financial economics , medicine , economics , differential equation
A number of two-dimensional spiking neuron models that combine continuous dynamics with an instantaneous reset have been introduced in the literature. The models are capable of reproducing a variety of experimentally observed spiking patterns and also have the advantage of being mathematically tractable. Here an analysis of the transverse stability of orbits in the phase plane leads to sufficient conditions on the model parameters for regular spiking to occur. The application of this method is illustrated by three examples, taken from existing models in the neuroscience literature. In the first two examples the model has no equilibrium states, and regular spiking follows directly. In the third example there are equilibrium points, and some additional quantitative arguments are given to prove that regular spiking occurs.

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