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The role of noise in neural information processing is still a controversial topic and the notion that intrinsic noise inevitably degrades information transmission still dominates the debate. However, in a population of spiking neurons, noise can reduce redundancy and/or control the level of synchrony in response to certain stimulus features [1]. Target neurons may selectively read out synchronous spikes only (coincidence detector) or aspecifically integrate over all input spikes, thereby extracting different aspects of the signal that stimulated the population of input neurons [2]. In this view, the amount of noise in a neural system might be optimally tuned to achieve the required signal processing properties. Experimentally, it is very challenging to prove this hypothesis since it is very difficult, if not impossible, to manipulate the amount of noise independently in many neurons.

Distinct signal processing properties of two populations of electrosensory neurons exhibiting different levels of spike time variability