Literature DB >> 10490941

Fast global oscillations in networks of integrate-and-fire neurons with low firing rates.

N Brunel1, V Hakim.   

Abstract

We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons --> infinity, the network exhibits a sharp transition between a stationary and an oscillatory global activity regime where neurons are weakly synchronized. The activity becomes oscillatory when the inhibitory feedback is strong enough. The period of the global oscillation is found to be mainly controlled by synaptic times but depends also on the characteristics of the external input. In large but finite networks, the analysis shows that global oscillations of finite coherence time generically exist both above and below the critical inhibition threshold. Their characteristics are determined as functions of systems parameters in these two different regions. The results are found to be in good agreement with numerical simulations.

Mesh:

Year:  1999        PMID: 10490941     DOI: 10.1162/089976699300016179

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


  227 in total

1.  A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning.

Authors:  D Q Nykamp; D Tranchina
Journal:  J Comput Neurosci       Date:  2000 Jan-Feb       Impact factor: 1.621

2.  Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

Authors:  N Brunel
Journal:  J Comput Neurosci       Date:  2000 May-Jun       Impact factor: 1.621

3.  Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron.

Authors:  A Herrmann; W Gerstner
Journal:  J Comput Neurosci       Date:  2001 Sep-Oct       Impact factor: 1.621

4.  Oscillations and irregular emission in networks of linear spiking neurons.

Authors:  G Mongillo; D J Amit
Journal:  J Comput Neurosci       Date:  2001 Nov-Dec       Impact factor: 1.621

5.  Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition.

Authors:  N Brunel; X J Wang
Journal:  J Comput Neurosci       Date:  2001 Jul-Aug       Impact factor: 1.621

6.  An analytical model for the "large, fluctuating synaptic conductance state" typical of neocortical neurons in vivo.

Authors:  Hamish Meffin; Anthony N Burkitt; David B Grayden
Journal:  J Comput Neurosci       Date:  2004 Mar-Apr       Impact factor: 1.621

7.  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

8.  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

9.  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

Review 10.  Neurophysiological and computational principles of cortical rhythms in cognition.

Authors:  Xiao-Jing Wang
Journal:  Physiol Rev       Date:  2010-07       Impact factor: 37.312

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