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.

Institute of Physiology Academy of Sciences Prague Czech Republic

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Barlow H. Possible principles underlying the transformation of sensory messages. In: Rosenblith W, editor. Sensory communication. Cambridge, Massachusetts: MIT Press; 1961. pp. 217–234.

Laughlin S. A simple coding procedure enhances a neuron's information capacity. Z Naturforsch. 1981;36:910–912. PubMed

Atick J. Could information theory provide an ecological theory of sensory processing? Network. Comp Neur Sys. 1992;3:213–251. PubMed

Bialek W, Owen WG. Temporal filtering in retinal bipolar cells. elements of an optimal computation? Biophys J. 1990;58:1227–1233. PubMed PMC

Hateren J. Theoretical predictions of spatiotemporal receptive fields of fly lmcs, and experimental validation. J Comp Physiol A. 1992;171:157–170.

Hornstein EP, O'Carroll DC, Anderson JC, Laughlin SB. Sexual dimorphism matches photoreceptor performance to behavioural requirements. Proc Biol Sci. 2000;267:2111–2117. PubMed PMC

Laughlin SB. Matched filtering by a photoreceptor membrane. Vision Res. 1996;36:1529–1541. PubMed

Simoncelli EP, Olshausen BA. Natural image statistics and neural representation. Annu Rev Neurosci. 2001;24:1193–1216. PubMed

Lewicki M. Efficient coding of natural sounds. Nat Neurosci. 2002;5:356–363. PubMed

Smith EC, Lewicki MS. Efficient auditory coding. Nature. 2006;439:978–982. PubMed

Kostal L, Lansky P, Rospars JP. Encoding of pheromone intensity by dynamic activation of pheromone receptors. Neurocomputing. 2007;70:1759–1763.

Rospars JP, Lansky P. Stochastic pulse stimulation in chemoreceptors and its properties. Math Biosci. 2004;188:133–145. PubMed

Cover T, Thomas J. Elements of information theory. New York: Wiley; 1991.

Dayan P, Abbott L. Theoretical neuroscience: computational and mathematical modeling of neural systems. Cambridge, Massachusetts: MIT Press; 2001.

Jones C. Structure of instantaneous plumes in the atmosphere. J Hazard Mat. 1983;7:87–112.

Murlis J. Odor plumes and the signal they provide. In: Carde R, Minks A, editors. Insect pheromone research: New directions. New York: Chapman and Hall; 1996. pp. 221–231.

Murlis J, Willis M, Cardé R. Spatial and temporal structures of pheromone plumes in fields and forests. Physiol Entomol. 2000;25:211–222.

Mylne K. Experimental measurements of concentration fluctuations. In: van Dopp H, editor. Air pollution modelling and its application VII. New York: Plenum Press; 1988. pp. 555–565.

Mylne K, Mason P. Concentration fluctuation measurements in a dispersing plume at a range of up to 1000m. Q J Roy Meteo Soc. 1991;117:177–206.

Kennedy J, Ludlow A, Sanders C. Guidance system used in moth sex attraction. Nature. 1980;288:475–477.

Kramer E. Turbulent diffusion and pheromone-triggered anemotaxis. In: Payne T, Birch M, Kennedy C, editors. Mechanisms in insect olfaction. Oxford, United Kingdom: Clarendon Press; 1986. pp. 59–67.

Willis M, Baker T. Effects of intermittent and continuous pheromone stimulation on the flight behaviour of the oriental fruit moth, Grapholita molesta. Physiol Entomol. 1984;9:341–358.

Vickers N, Baker T. Male Heliothis virescens maintain upwind flight in response to experimentally pulsed filaments of their sex pheromone. J Insect Behavior. 1992;5:669–687.

Lungarella M, Sporns O. Mapping information flow in sensorimotor networks. PLoS Comp Biol. 2006;2:1301–1312. doi:10.1371/journal.pcbi.0020144. PubMed PMC

Kaissling KE. Olfactory perireceptor and receptor events in moths: a kinetic model. Chem Senses. 2001;26:125–150. PubMed

Kaissling KE, Rospars JP. Dose-response relationships in an olfactory flux detector model revisited. Chem Senses. 2004;29:529–531. PubMed

Rospars J, Lansky P, Krivan V. Extracellular transduction events under pulsed stimulation in moth olfactory sensilla. Chem Senses. 2003;28:509–522. PubMed

Kodadová B. Resolution of pheromone pulses in receptor cells of Antheraea polyphemus at different temperatures. J Comp Physiol A. 1996;179:301–310.

Rumbo ER, Kaissling K. Temporal resolution of odour pulses by three types of pheromone receptor cells in Antheraea polyphemus. J Comp Physiol A. 1989;165:281–291.

Brunel N, Nadal JP. Mutual information, Fisher information, and population coding. Neural Comput. 1998;10:1731–1757. PubMed

Baker T, Haynes K. Field and laboratory electroantennographic measurements of pheromone plume structure correlated with oriental fruit moth behaviour. Physiol Entomol. 1989;14:1–12.

Hanna S. The exponential probability density function and concentration fluctuations in smoke plumes. Boundary Layer Meteorology. 1984;29:361–375.

Hanna S, Insley E. Time series analyses of concentration and wind fluctuations. Boundary Layer Meteorology. 1989;47:131–147.

Ruelle D. Chance and chaos. Princeton, New Jersey: Princeton University Press; 1993.

Kaissling K. Pheromone deactivation catalyzed by receptor molecules: a quantitative kinetic model. Chem Senses. 1998;23:385–395. PubMed

Kaissling K. Flux detectors versus concentration detectors: two types of chemoreceptors. Chem Senses. 1998;23:99–111. PubMed

Minor A, Kaissling K. Cell responses to single pheromone molecules may reflect the activation kinetics of olfactory receptor molecules. J Comp Physiol A. 2003;189:221–230. PubMed

Christensen T, Hildebrand J. Frequency coding by central olfactory neurons in the sphinx moth Manduca sexta. Chem Senses. 1988;13:123–130.

Murlis J. The structure of odour plumes. In: Payne T, Birch M, Kennedy C, editors. Mechanisms in insect olfaction. Oxford, United Kingdom: Clarendon Press; 1986. pp. 27–38.

Jaynes E, Bretthorst G. Probability theory: the logic of science. Cambridge, United Kingdom: Cambridge University Press; 2003.

Champeney D. Fourier transforms and their physical applications. New York: Academic Press; 1973.

Oppenheim A, Schafer R. Discrete-time signal processing. Englewood Cliffs, New Jersey: Prentice Hall; 1989.

Belanger J, Willis M. Biologically-inspired search algorithms for locating unseen odor sources. Proceedings of the IEEE International Symposium on Intelligent Control. 1998:265–270.

Shared input and recurrency in neural networks for metabolically efficient information transmission

Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth

Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations

Coding accuracy on the psychophysical scale