Literature DB >> 17436067

Threshold fatigue and information transfer.

Maurice J Chacron1, Benjamin Lindner, André Longtin.   

Abstract

Neurons in vivo must process sensory information in the presence of significant noise. It is thus plausible to assume that neural systems have developed mechanisms to reduce this noise. Theoretical studies have shown that threshold fatigue (i.e. cumulative increases in the threshold during repetitive firing) could lead to noise reduction at certain frequencies bands and thus improved signal transmission as well as noise increases and decreased signal transmission at other frequencies: a phenomenon called noise shaping. There is, however, no experimental evidence that threshold fatigue actually occurs and, if so, that it will actually lead to noise shaping. We analyzed action potential threshold variability in intracellular recordings in vivo from pyramidal neurons in weakly electric fish and found experimental evidence for threshold fatigue: an increase in instantaneous firing rate was on average accompanied by an increase in action potential threshold. We show that, with a minor modification, the standard Hodgkin-Huxley model can reproduce this phenomenon. We next compared the performance of models with and without threshold fatigue. Our results show that threshold fatigue will lead to a more regular spike train as well as robustness to intrinsic noise via noise shaping. We finally show that the increased/reduced noise levels due to threshold fatigue correspond to decreased/increased information transmission at different frequencies.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17436067      PMCID: PMC5053818          DOI: 10.1007/s10827-007-0033-y

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  52 in total

Review 1.  Detecting and estimating signals in noisy cable structure, I: neuronal noise sources.

Authors:  A Manwani; C Koch
Journal:  Neural Comput       Date:  1999-11-15       Impact factor: 2.026

2.  Cellular mechanisms contributing to response variability of cortical neurons in vivo.

Authors:  R Azouz; C M Gray
Journal:  J Neurosci       Date:  1999-03-15       Impact factor: 6.167

3.  Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli.

Authors:  M J Chacron; A Longtin; L Maler
Journal:  J Neurosci       Date:  2001-07-15       Impact factor: 6.167

4.  Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron.

Authors:  Y H Liu; X J Wang
Journal:  J Comput Neurosci       Date:  2001 Jan-Feb       Impact factor: 1.621

5.  Receptive field organization determines pyramidal cell stimulus-encoding capability and spatial stimulus selectivity.

Authors:  Joseph Bastian; Maurice J Chacron; Leonard Maler
Journal:  J Neurosci       Date:  2002-06-01       Impact factor: 6.167

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

Authors:  Maurice J Chacron; Khashayar Pakdaman; André Longtin
Journal:  Neural Comput       Date:  2003-02       Impact factor: 2.026

7.  Integrate-and-fire neurons with threshold noise: a tractable model of how interspike interval correlations affect neuronal signal transmission.

Authors:  Benjamin Lindner; Maurice J Chacron; André Longtin
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2005-08-26

8.  A stochastic model of the repetitive activity of neurons.

Authors:  C D Geisler; J M Goldberg
Journal:  Biophys J       Date:  1966-01       Impact factor: 4.033

9.  High-frequency vibratory sensitive neurons in monkey primary somatosensory cortex: entrained and nonentrained responses to vibration during the performance of vibratory-cued hand movements.

Authors:  M A Lebedev; R J Nelson
Journal:  Exp Brain Res       Date:  1996-10       Impact factor: 1.972

10.  Prolonged responses in rat cerebellar Purkinje cells following activation of the granule cell layer: an intracellular in vitro and in vivo investigation.

Authors:  D Jaeger; J M Bower
Journal:  Exp Brain Res       Date:  1994       Impact factor: 1.972

View more
  23 in total

1.  Impact of neural noise on a sensory-motor pathway signaling impending collision.

Authors:  Peter W Jones; Fabrizio Gabbiani
Journal:  J Neurophysiol       Date:  2011-11-23       Impact factor: 2.714

2.  Parallel coding of first- and second-order stimulus attributes by midbrain electrosensory neurons.

Authors:  Patrick McGillivray; Katrin Vonderschen; Eric S Fortune; Maurice J Chacron
Journal:  J Neurosci       Date:  2012-04-18       Impact factor: 6.167

3.  Balanced ionotropic receptor dynamics support signal estimation via voltage-dependent membrane noise.

Authors:  Curtis M Marcoux; Stephen E Clarke; William H Nesse; Andre Longtin; Leonard Maler
Journal:  J Neurophysiol       Date:  2015-11-11       Impact factor: 2.714

4.  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

5.  Noise shaping in neural populations.

Authors:  Oscar Avila Akerberg; Maurice J Chacron
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-01-21

6.  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

7.  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

8.  In vivo conditions influence the coding of stimulus features by bursts of action potentials.

Authors:  Oscar Avila Akerberg; Maurice J Chacron
Journal:  J Comput Neurosci       Date:  2011-01-27       Impact factor: 1.621

Review 9.  Nonrenewal spike train statistics: causes and functional consequences on neural coding.

Authors:  Oscar Avila-Akerberg; Maurice J Chacron
Journal:  Exp Brain Res       Date:  2011-01-26       Impact factor: 1.972

10.  Coding movement direction by burst firing in electrosensory neurons.

Authors:  Navid Khosravi-Hashemi; Eric S Fortune; Maurice J Chacron
Journal:  J Neurophysiol       Date:  2011-07-20       Impact factor: 2.714

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.