Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth

Abstract

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.

Optimal decoding and information transmission in Hodgkin–Huxley neurons under metabolic cost constraints

“…Continuous input distributions, resulting either from the deterministic approach (Kostal et al, 2008;Laughlin, 1981), from the classical Gaussian channel application (de Ruyter van Steveninck and Laughlin, 1996) or from the low-noise approximation (McDonnell and Stocks, 2008;Nadal and Parga, 1994), potentially resemble the natural stimuli distributions. On the other hand, interpreting the exactly optimal discrete input distributions (Ikeda and Manton, 2009; as the natural stimulation statistics might lead to difficulties.…”

“…The classical application of information theory considers the neuron (or a population of neurons) to act as an information channel (Chacron et al, 2007;de Ruyter van Steveninck and Laughlin, 1996;Ikeda and Manton, 2009;Johnson, 2010;Stein, 1967). In many cases, motivated especially by the efficient coding hypothesis (Barlow, 1961), the goal is to provide the ultimate limits on neuronal performance in the point-to-point communication situations (Atick, 1992;de Ruyter van Steveninck and Laughlin, 1996;Kostal et al, 2008;Laughlin, 1981;Rieke et al, 1997). The communication process is described by means of mutual information between the neuronal inputs and responses, with channel capacity providing the upper bound on information transfer.…”

Modeling the response of a population of olfactory receptor neurons to an odorant

“…This is the case for example of adaptation in the case of long or repetitive stimulation (Zufall and Leinders-Zufall 2000). Although the stochastic pulse nature of real stimuli, due to atmospheric turbulences at various scales, is probably of importance in the case of insect olfaction (Rospars and Lánský 2004;Kostal et al 2008), most of this external variability is likely lost in the nasal cavity of mammals during inspiration. However, it would be worth to take into account the internal airflow in the cavity and its effect on the spatiotemporal distribution of odorants on the epithelium (Hahn et al 1994;Yang et al 2007).…”

“…With the advent of air-breathing animals in both arthropods and vertebrates, the olfactory organs diversified considerably for sensing chemicals in air (odorants). A common set of neural mechanisms evolved across phyla to solve this task in an efficient way (Hildebrand and Shepherd 1997;Strausfeld and Hildebrand 1999;Bargmann 2006;Kostal et al 2008).…”

Information capacity in the weak-signal approximation

“…The theory has been applied successfully to a wide range of problems [2], including, e.g., classical and quantum computation and communication [3][4][5], optical communication [6][7][8] or quantification of different aspects of information processing in real neurons and neuronal models [9][10][11][12][13][14][15].…”