INFORMATION-FLOW IN SENSORY NEURONS

Recent experiments show that the neural codes at work in a wide range of creatures share some common features. At first sight, these observa tions seem unrelated. However, we show that all of these features of t he code arise naturally in a simple threshold crossing model when we c hoose the threshold to maximize the transmitted information. This maxi mization process requires neural adaptation to not only the d.c. signa l level, as in conventional light and dark adaptation (for example), b elt also to the statistical structure of the signal and noise distribu tions. Interestingly, if we fix the threshold level, we can observe a peak in the transmitted information at a finite value of the input sig nal-to-noise ratio. However, when we allow the threshold to adapt to t he statistical structure of the signal and noise, the transmitted info rmation-is always monotonically increasing with increasing input signa l-to-noise ratio.