Efficient olfactory coding in the pheromone receptor neuron of a moth

The concept of coding efficiency holds that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical characteristics of their natural stimulus. Encouraged by the successful invocation of this principle to predict how neurons encode natural auditory and visual stimuli, we attempted its application to olfactory neurons. The pheromone receptor neuron of the male moth Antheraea polyphemus, for which quantitative properties of both the natural stimulus and the reception processes are available, was selected. We predicted several characteristics that the pheromone plume should possess under the hypothesis that the receptors perform optimally, i.e., transfer as much information on the stimulus per unit time as possible. Our results demonstrate that the statistical characteristics of the predicted stimulus, e. g., the probability distribution function of the stimulus concentration, the spectral density function of the stimulation course, and the intermittency, are in good agreement with those measured experimentally in the field. These results should stimulate further quantitative studies on the evolutionary adaptation of olfactory nervous systems to odorant plumes and on the plume characteristics that are most informative for the ' sniffer'. Both aspects are relevant to the design of olfactory sensors for odour-tracking robots.Efficient coding is an overarching principle, well tested in visual and auditory neurobiology, which states that sensory neurons are adapted to the statistical characteristics of their natural stimulus - in brief, neurons best process those stimuli that occur most frequently. To assess its validity in olfaction, we examine the pheromone communication of moths, in which males locate their female mates by the pheromone they release. We determine the characteristics of the pheromone plume which are best detected by the male reception system. We show that they are in agreement with plume measurements in the field, so providing quantitative evidence that this system also obeys the efficient coding principle. Exploring the quantitative relationship between the properties of biological sensory systems and their natural environment should lead not only to a better understanding of neural functions and evolutionary processes, but also to improvements in the design of artificial sensory systems