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Human perception of sound, brightness, pain, etc. is always on logarithmic scale. It pays close attention to small signals and suppresses high-power signals. The value of a signal is mostly in the information it carries, not its intensity.
How much louder than a whisper is a conversation? 10x? No, more like 1000x. How much brighter is it outside in the sun compared to inside your house? 10x? No, more like 1000x.
I was shocked during the last partial solar eclipse at how unremarkable it was, which brought to mind how wide the variation in intensity between a clear noon sun and a sunrise/set or foggy/cloudy day is (or smoky/smoggy)
Still, in both cases, the sensory organ (mostly the brain) adapts to the expected margin of input to be sensitive in the relevant range. Any idea how this is realized?
Since neural nets are good at fitting data with high signal to noise ratio maybe they can be used with engineered data sets with precise signals?
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