The statistical distribution of noisy transmission in human sensors.

OBJECTIVE: Brains, like other physical devices, are inherently noisy. This source of variability is large, to the extent that internal noise often impacts human sensory processing more than externally induced (stimulus-driven) perturbations. Despite the fundamental nature of this phenomenon, its statistical distribution remains unknown: for the past 40 years it has been assumed Gaussian, but the applicability (or lack thereof) of this assumption has not been checked. APPROACH: We obtained detailed measurements of this process by exploiting an integrated approach that combines experimental, theoretical and computational tools from bioengineering applications of system identification and reverse correlation methodologies. MAIN
RESULTS: The resulting characterization reveals that the underlying distribution is in fact not Gaussian, but well captured by the Laplace (double-exponential) distribution. SIGNIFICANCE: Potentially relevant to this result is the observation that image contrast follows leptokurtic distributions in natural scenes, suggesting that the properties of internal noise in human sensors may reflect environmental statistics.