Literature DB >> 12590807

Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue.

Maurice J Chacron1, Khashayar Pakdaman, André Longtin.   

Abstract

Neuronal adaptation as well as interdischarge interval correlations have been shown to be functionally important properties of physiological neurons. We explore the dynamics of a modified leaky integrate-and-fire (LIF) neuron, referred to as the LIF with threshold fatigue, and show that it reproduces these properties. In this model, the postdischarge threshold reset depends on the preceding sequence of discharge times. We show that in response to various classes of stimuli, namely, constant currents, step currents, white gaussian noise, and sinusoidal currents, the model exhibits new behavior compared with the standard LIF neuron. More precisely, (1) step currents lead to adaptation, that is, a progressive decrease of the discharge rate following the stimulus onset, while in the standard LIF, no such patterns are possible; (2) a saturation in the firing rate occurs in certain regimes, a behavior not seen in the LIF neuron; (3) interspike intervals of the noise-driven modified LIF under constant current are correlated in a way reminiscent of experimental observations, while those of the standard LIF are independent of one another; (4) the magnitude of the correlation coefficients decreases as a function of noise intensity; and (5) the dynamics of the sinusoidally forced modified LIF are described by iterates of an annulus map, an extension to the circle map dynamics displayed by the LIF model. Under certain conditions, this map can give rise to sensitivity to initial conditions and thus chaotic behavior.

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Year:  2003        PMID: 12590807     DOI: 10.1162/089976603762552915

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


  25 in total

1.  Comparison of coding capabilities of Type I and Type II neurons.

Authors:  Martin St-Hilaire; André Longtin
Journal:  J Comput Neurosci       Date:  2004 May-Jun       Impact factor: 1.621

2.  Neural heterogeneities influence envelope and temporal coding at the sensory periphery.

Authors:  M Savard; R Krahe; M J Chacron
Journal:  Neuroscience       Date:  2010-10-28       Impact factor: 3.590

3.  Phase locking in integrate-and-fire models with refractory periods and modulation.

Authors:  Tomás Gedeon; Matt Holzer
Journal:  J Math Biol       Date:  2004-03-03       Impact factor: 2.259

4.  Neural variability, detection thresholds, and information transmission in the vestibular system.

Authors:  Soroush G Sadeghi; Maurice J Chacron; Michael C Taylor; Kathleen E Cullen
Journal:  J Neurosci       Date:  2007-01-24       Impact factor: 6.167

5.  Threshold fatigue and information transfer.

Authors:  Maurice J Chacron; Benjamin Lindner; André Longtin
Journal:  J Comput Neurosci       Date:  2007-04-14       Impact factor: 1.621

6.  A psycholinguistic model of natural language parsing implemented in simulated neurons.

Authors:  Christian R Huyck
Journal:  Cogn Neurodyn       Date:  2009-03-20       Impact factor: 5.082

7.  Chaotic solutions in the quadratic integrate-and-fire neuron with adaptation.

Authors:  Gang Zheng; Arnaud Tonnelier
Journal:  Cogn Neurodyn       Date:  2008-11-06       Impact factor: 5.082

8.  Predicting spike timing in highly synchronous auditory neurons at different sound levels.

Authors:  Bertrand Fontaine; Victor Benichoux; Philip X Joris; Romain Brette
Journal:  J Neurophysiol       Date:  2013-07-17       Impact factor: 2.714

9.  Spectrum of Lyapunov exponents of non-smooth dynamical systems of integrate-and-fire type.

Authors:  Douglas Zhou; Yi Sun; Aaditya V Rangan; David Cai
Journal:  J Comput Neurosci       Date:  2009-12-09       Impact factor: 1.621

Review 10.  Efficient computation via sparse coding in electrosensory neural networks.

Authors:  Maurice J Chacron; André Longtin; Leonard Maler
Journal:  Curr Opin Neurobiol       Date:  2011-06-16       Impact factor: 6.627

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