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Burst firing regulates correlated activity in neurons

BMC Neuroscience200910 (Suppl 1) :P189

  • Published:


  • Information Transmission
  • Model Neuron
  • Firing Regime
  • Noise Correlation
  • Transmission Property

Understanding how sensory information is encoded by populations of neurons is complicated by the fact that neurons display variability in their responses to repeated presentations of the same stimulus. A further complication comes from the fact that these variabilities can be correlated. In fact, the role of correlations in neural variabilities (noise correlations) has been the focus of much debate in recent years as even a small amount of correlation between a pair of neurons can have dramatic effects on information transmission by neural populations [1]. Recent experimental evidence has shown that burst firing can modulate the amount of noise correlations displayed by neural populations [2]. Here we investigate the role of intrinsic bursting dynamics in model neurons receiving correlated input. These model neurons transition from a tonic firing regime to a bursting regime as the amount of depolarizing current is varied. We find that, given an input correlation c, the output correlation R between neural pairs in the network is less when both neurons are in bursting regime than when they are in the tonic regime. Our theoretical results are supported by experimental results obtained from a slice preparation. We show that intrinsic burst dynamics can decorrelate neural populations and therefore regulate their information transmission properties.



This work was supported by CONACyT (O. A. A.) and CIHR, CFI and CRC (M. C.).

Authors’ Affiliations

Centre for Nonlinear Dynamics in Physiology and Medicine, McGill University, Montreal, QC, H3G1Y6, Canada
Department of Physics, McGill University, Montreal, QC, H3A2T8, Canada
Department of Physiology, McGill University, Montreal, QC, H3G1Y6, Canada


  1. Schneidman E, Berry MJ, Segev R, Bialek W: Weak pairwise correlations imply strongly correlated network states in a neural population. Nature. 2008, 440: 1007-1012. 10.1038/nature04701.View ArticleGoogle Scholar
  2. Chacron MJ, Bastian J: Population coding by electrosensory neurons. J Neurophysiol. 2008, 99: 1825-1835. 10.1152/jn.01266.2007.PubMedView ArticleGoogle Scholar


© Akerberg and Chacron; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.