20 Years of “Noise”: Contributions of Computational Neuroscience to the Exploration of the Effect of Background Activity on Central Neurons

Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 9)


The central nervous system is subject to many different forms of noise, which have fascinated researchers since the beginning of electrophysiological recordings. In cerebral cortex, the largest amplitude noise source is the “synaptic noise,” which is dominant in intracellular recordings in vivo. The consequences of this background activity are a classic theme of modeling studies. In the last 20 years, this field tremendously progressed as the synaptic noise was measured for the first time using quantitative methods. These measurements have allowed computational models not only to be more realistic and closer to the biological data but also to investigate the consequences of synaptic noise in more quantitative terms, measurable in experiments. As a consequence, the “high-conductance state” conferred by this intense activity in vivo could also be replicated in neurons maintained in vitro using dynamic-clamp techniques. In addition, mathematical approaches of stochastic systems provided new methods to analyze synaptic noise and obtain critical information such as the optimal conductance patterns leading to spike discharges. It is only through such a combination of different disciplines, such as experiments, computational models, and theory, that we will be able to understand how noise participates to neural computations.


Background Activity Intracellular Recording Thalamic Neuron Synaptic Conductance Voltage Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The experimental data shown here were obtained in collaboration with Thierry Bal, Diego Contreras, Jean-Marc Fellous, Denis Paré, Zuzanna Piwkowska, Mircea Steriade and Igor Timofeev. The models and analyses were done in collaboration with Sami El Boustani, Martin Pospischil, Michelle Rudolph and Terrence Sejnowski. Research supported by the CNRS, ANR (HR-CORTEX project), HFSP and the European Community (FACETS project FP6-15879; BrainScales project FP7-269921).


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Unité de Neuroscience Information et Complexité (UNIC), CNRSGif-sur-YvetteFrance

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