Low-Pass Filtering of Information in the Leaky Integrate-and-Fire Neuron Driven by White Noise

  • Benjamin LindnerEmail author
Part of the Understanding Complex Systems book series (UCS)


The question of how noisy spiking neurons respond to external time-dependent stimuli is a central topic in computational neuroscience. An important aspect of the neural information transmission is, whether neurons encode preferentially information about slow or about fast components of the stimulus (signal). A convenient way to quantify this is the spectral coherence function, that in some experimental data shows a global maximum at low frequencies (low-pass information filter), in some other cases has a maximum at higher frequencies (band-pass or high-pass information filter); information-filtering defined in this way is related but not identical to the usual filtering of spectral power. Here I demonstrate numerically that the leaky integrate-and-fire neuron driven by white noise (a stimulus without temporal correlations) acts as a low-pass information filter irrespective of the dynamical regime (fluctuation-driven and irregular or mean-driven and regular firing).


Firing Rate Spike Train Noise Intensity Firing Regime Coherence Function 
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.



This research has been funded by the BMBF (FKZ: 01GQ1001A).


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Bernstein Center for Computational Neuroscience BerlinBerlinGermany
  2. 2.Physics DepartmentHumboldt University BerlinBerlinGermany

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