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

The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system.

[1]  Nicolas Brunel,et al.  Mutual Information, Fisher Information, and Population Coding , 1998, Neural Computation.

[2]  M. McDonnell,et al.  Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations. , 2008, Physical review letters.

[3]  Paul V. Watkins,et al.  Specialized neuronal adaptation for preserving input sensitivity , 2008, Nature Neuroscience.

[4]  Ila Fiete,et al.  Grid cells generate an analog error-correcting code for singularly precise neural computation , 2011, Nature Neuroscience.

[5]  R. Cardé,et al.  Spatial and temporal structures of pheromone plumes in fields and forests , 2000 .

[6]  S. Laughlin.  A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[7]  J A Garcia-Lazaro,et al.  Shifting and scaling adaptation to dynamic stimuli in somatosensory cortex , 2011, The European journal of neuroscience.

[8]  Edward Challis,et al.  Fisher and Shannon Information in Finite Neural Populations , 2012, Neural Computation.

[9]  E. R. Rumbo,et al.  Adaptation processes in insect olfactory receptors - their relation to transduction and orientation , 1986 .

[10]  Johannes C. Dahmen,et al.  Adaptation to Stimulus Statistics in the Perception and Neural Representation of Auditory Space , 2010, Neuron.

[11]  A. Pouget,et al.  Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations , 2004, Nature Neuroscience.

[12]  Toby Berger,et al.  Capacity Analysis for Integrate-and-Fire Neurons With Descending Action Potential Thresholds , 2010, IEEE Transactions on Information Theory.

[13]  A. Holley,et al.  Convergence in the olfactory system: quantitative aspects of odour sensitivity. , 1978, Journal of theoretical biology.

[14]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[15]  J. Murlis.  Odor Plumes and the Signal They Provide , 1997 .

[16]  Philippe Lucas,et al.  Olfactory coding in the turbulent realm , 2017, bioRxiv.

[17]  J. Bernardo.  Reference Posterior Distributions for Bayesian Inference , 1979 .

[18]  I. Dean,et al.  Neural population coding of sound level adapts to stimulus statistics , 2005, Nature Neuroscience.

[19]  Mark A. Willis,et al.  Odor tracking flight of male Manduca sexta moths along plumes of different cross-sectional area , 2013, Journal of Comparative Physiology A.

[20]  Oakland J. Peters,et al.  Frontiers in Cellular Neuroscience Cellular Neuroscience , 2022 .

[21]  Susanne Ditlevsen,et al.  Identification of noisy response latency. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  P. Lucas,et al.  Voltage- and calcium-activated currents in cultured olfactory receptor neurons of male Mamestra brassicae (Lepidoptera). , 2002, Chemical senses.

[23]  B. Delgutte,et al.  Dynamic Range Adaptation to Sound Level Statistics in the Auditory Nerve , 2009, The Journal of Neuroscience.

[24]  David McAlpine,et al.  Optimal neural population coding of an auditory spatial cue , 2004, Nature.

[25]  Ryota Kobayashi,et al.  Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints , 2015, Biosyst..

[26]  M. Ibbotson,et al.  Characterizing contrast adaptation in a population of cat primary visual cortical neurons using Fisher information. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[27]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[28]  Petr Lánský,et al.  Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth , 2008, PLoS Comput. Biol..

[29]  Monika Stengl,et al.  Adaptation in pheromone-sensitive trichoid sensilla of the hawkmoth Manduca sexta , 2003, Journal of Experimental Biology.

[30]  B. Hansson,et al.  Olfaction in Lepidoptera , 1995, Experientia.

[31]  Hao Guo,et al.  Odorant Receptor Sensitivity Modulation in Drosophila , 2017, The Journal of Neuroscience.

[32]  Petr Lánský,et al.  Optimum signal in a simple neuronal model with signal-dependent noise , 2005, Biological Cybernetics.

[33]  A. Fairhall,et al.  Sensory adaptation , 2007, Current Opinion in Neurobiology.

[34]  M. Stengl,et al.  No Evidence for Ionotropic Pheromone Transduction in the Hawkmoth Manduca sexta , 2016, PloS one.

[35]  Philippe Lucas,et al.  Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons , 2014, PLoS Comput. Biol..

[36]  B L McNaughton,et al.  Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. , 1998, Journal of neurophysiology.

[37]  Dennis L Barbour,et al.  Level-tuned neurons in primary auditory cortex adapt differently to loud versus soft sounds. , 2011, Cerebral cortex.

[38]  Rachel I. Wilson.  Early olfactory processing in Drosophila: mechanisms and principles. , 2013, Annual review of neuroscience.

[39]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[40]  M. Vergassola,et al.  Odor Landscapes in Turbulent Environments , 2014, 1411.3507.

[41]  Fusao Kawai,et al.  Ca2+-activated K+ currents regulate odor adaptation by modulating spike encoding of olfactory receptor cells. , 2002, Biophysical journal.

[42]  Peter Dayan,et al.  The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.

[43]  T. Baker,et al.  Manoeuvres used by flying male oriental fruit moths to relocate a sex pheromone plume in an experimentally shifted wind‐field , 1987 .

[44]  D. Pressnitzer,et al.  Adaptive coding is constrained to midline locations in a spatial listening task. , 2012, Journal of neurophysiology.

[45]  P. Lánský,et al.  Accuracy of rate coding: When shorter time window and higher spontaneous activity help. , 2017, Physical review. E.

[46]  Alexander S. Ecker,et al.  Reassessing optimal neural population codes with neurometric functions , 2011, Proceedings of the National Academy of Sciences.

[47]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[48]  Lubomir Kostal,et al.  Stimulus reference frame and neural coding precision , 2016 .

[49]  Eero P. Simoncelli,et al.  Implicit encoding of prior probabilities in optimal neural populations , 2010, NIPS.

[50]  The role of the coreceptor Orco in insect olfactory transduction , 2013, Journal of Comparative Physiology A.

[51]  S. Laughlin,et al.  The rate of information transfer at graded-potential synapses , 1996, Nature.

[52]  Yuhai Tu,et al.  Distinct signaling of Drosophila chemoreceptors in olfactory sensory neurons , 2016, Proceedings of the National Academy of Sciences.

[53]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[54]  D. Wicher.  Tuning Insect Odorant Receptors , 2018, Front. Cell. Neurosci..

[55]  Thierry Emonet,et al.  Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli , 2017, eLife.

[56]  P. J. Mason,et al.  Concentration fluctuation measurements in a dispersing plume at a range of up to 1000 m , 1991 .

[57]  Thomas C. Baker,et al.  Field and laboratory electroantennographic measurements of pheromone plume structure correlated with oriental fruit moth behaviour , 1989 .

[58]  Leslie B Vosshall,et al.  Controversy and consensus: noncanonical signaling mechanisms in the insect olfactory system , 2009, Current Opinion in Neurobiology.

[59]  Shiro Ikeda,et al.  Capacity of a Single Spiking Neuron Channel , 2009, Neural Computation.

[60]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[61]  Richard H. Sherman,et al.  Chaotic communications in the presence of noise , 1993, Optics & Photonics.

[62]  H. Breer,et al.  Access to the odor world: olfactory receptors and their role for signal transduction in insects , 2017, Cellular and Molecular Life Sciences.

[63]  M. Stengl.  Pheromone Transduction in Moths , 2010, Front. Cell. Neurosci..

[64]  Michael S. Lewicki,et al.  Efficient coding of natural sounds , 2002, Nature Neuroscience.

[65]  H. Jeffreys.  An invariant form for the prior probability in estimation problems , 1946, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[66]  Mark D. McDonnell,et al.  Metabolic cost of neuronal information in an empirical stimulus-response model , 2013, Biological Cybernetics.

[67]  J. Schnupp,et al.  Shifting and scaling adaptation to dynamic stimuli in somatosensory cortex , 2007 .

[68]  C. Jones.  On the structure of instantaneous plumes in the atmosphere , 1983 .

[69]  Petr Lánský,et al.  A review of the methods for neuronal response latency estimation , 2015, Biosyst..