Literature DB >> 9744894

Linearization of F-I curves by adaptation.

B Ermentrout1.   

Abstract

We show that negative feedback to highly nonlinear frequency-current (F-I) curves results in an effective linearization. (By highly nonlinear we mean that the slope at threshold is infinite or very steep.) We then apply this to a specific model for spiking neurons and show that the details of the adaptation mechanism do not affect the results. The crucial points are that the adaptation is slow compared to other processes and the unadapted F-I curve is highly nonlinear.

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Year:  1998        PMID: 9744894     DOI: 10.1162/089976698300017106

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  69 in total

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4.  Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

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6.  Dynamics of the instantaneous firing rate in response to changes in input statistics.

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7.  Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron.

Authors:  Fabrizio Gabbiani; Holger G Krapp
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8.  Differential control of active and silent phases in relaxation models of neuronal rhythms.

Authors:  Joël Tabak; Michael J O'Donovan; John Rinzel
Journal:  J Comput Neurosci       Date:  2006-07-28       Impact factor: 1.621

9.  Spontaneous dynamics and response properties of a Hodgkin-Huxley-type neuron model driven by harmonic synaptic noise.

Authors:  Hoai Nguyen; Alexander B Neiman
Journal:  Eur Phys J Spec Top       Date:  2010-09       Impact factor: 2.707

10.  Reduction of spike afterdepolarization by increased leak conductance alters interspike interval variability.

Authors:  Fernando R Fernandez; John A White
Journal:  J Neurosci       Date:  2009-01-28       Impact factor: 6.167

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