Literature DB >> 12184844

Dynamics of the firing probability of noisy integrate-and-fire neurons.

Nicolas Fourcaud1, Nicolas Brunel.   

Abstract

Cortical neurons in vivo undergo a continuous bombardment due to synaptic activity, which acts as a major source of noise. Here, we investigate the effects of the noise filtering by synapses with various levels of realism on integrate-and-fire neuron dynamics. The noise input is modeled by white (for instantaneous synapses) or colored (for synapses with a finite relaxation time) noise. Analytical results for the modulation of firing probability in response to an oscillatory input current are obtained by expanding a Fokker-Planck equation for small parameters of the problem - when both the amplitude of the modulation is small compared to the background firing rate and the synaptic time constant is small compared to the membrane time constant. We report here the detailed calculations showing that if a synaptic decay time constant is included in the synaptic current model, the firing-rate modulation of the neuron due to an oscillatory input remains finite in the high-frequency limit with no phase lag. In addition, we characterize the low-frequency behavior and the behavior of the high-frequency limit for intermediate decay times. We also characterize the effects of introducing a rise time to the synaptic currents and the presence of several synaptic receptors with different kinetics. In both cases, we determine, using numerical simulations, an effective decay time constant that describes the neuronal response completely.

Mesh:

Year:  2002        PMID: 12184844     DOI: 10.1162/089976602320264015

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


  80 in total

1.  How spike generation mechanisms determine the neuronal response to fluctuating inputs.

Authors:  Nicolas Fourcaud-Trocmé; David Hansel; Carl van Vreeswijk; Nicolas Brunel
Journal:  J Neurosci       Date:  2003-12-17       Impact factor: 6.167

2.  An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex.

Authors:  David Cai; Louis Tao; Michael Shelley; David W McLaughlin
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-06       Impact factor: 11.205

3.  An embedded network approach for scale-up of fluctuation-driven systems with preservation of spike information.

Authors:  David Cai; Louis Tao; David W McLaughlin
Journal:  Proc Natl Acad Sci U S A       Date:  2004-09-20       Impact factor: 11.205

4.  Population density models of integrate-and-fire neurons with jumps: well-posedness.

Authors:  Grégory Dumont; Jacques Henry
Journal:  J Math Biol       Date:  2012-06-20       Impact factor: 2.259

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

Authors:  Nicolas Fourcaud-Trocmé; Nicolas Brunel
Journal:  J Comput Neurosci       Date:  2005-06       Impact factor: 1.621

6.  Action potential onset dynamics and the response speed of neuronal populations.

Authors:  B Naundorf; T Geisel; F Wolf
Journal:  J Comput Neurosci       Date:  2005-06       Impact factor: 1.621

7.  Delayed excitatory and inhibitory feedback shape neural information transmission.

Authors:  Maurice J Chacron; André Longtin; Leonard Maler
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2005-11-14

8.  The most likely voltage path and large deviations approximations for integrate-and-fire neurons.

Authors:  Liam Paninski
Journal:  J Comput Neurosci       Date:  2006-04-22       Impact factor: 1.621

9.  A recurrent network mechanism of time integration in perceptual decisions.

Authors:  Kong-Fatt Wong; Xiao-Jing Wang
Journal:  J Neurosci       Date:  2006-01-25       Impact factor: 6.167

10.  On the application of "equation-free modelling" to neural systems.

Authors:  Carlo R Laing
Journal:  J Comput Neurosci       Date:  2006-02-20       Impact factor: 1.621

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